{"ts": "2026-07-16T17:34:49.935731+00:00", "type": "system_prompt", "text": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}
{"ts": "2026-07-16T17:34:49.935731+00:00", "type": "user", "text": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"}
{"ts": "2026-07-16T17:35:16.874614+00:00", "type": "llm_call", "call_index": 1, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-16T17:34:49.936237+00:00", "finished_at": "2026-07-16T17:35:16.874614+00:00", "duration_s": 26.938000000009197, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"}], "tools": [{"type": "function", "function": {"name": "execute_blender_code", "description": "\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code_for_cli", "description": "\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}, "code": {"title": "Code", "type": "string"}}, "required": ["blend_file", "code"], "title": "execute_blender_code_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks", "description": "\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_datablocksArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks_for_cli", "description": "\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_datablocks_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files", "description": "\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_missing_filesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files_for_cli", "description": "\n        Report missing file references by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_missing_files_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries", "description": "\n        Return a tree of directly and indirectly linked library files.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_of_linked_librariesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries_for_cli", "description": "\n        Return linked-library info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_of_linked_libraries_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info", "description": "\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_path_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info_for_cli", "description": "\n        Return path info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_path_info_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess", "description": "\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_usage_guessArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess_for_cli", "description": "\n        Guess use-cases by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_usage_guess_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_detail_summary", "description": "\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "get_object_detail_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_objects_summary", "description": "\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ", "parameters": {"properties": {}, "title": "get_objects_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_python_api_docs", "description": "\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ", "parameters": {"properties": {"identifier": {"title": "Identifier", "type": "string"}}, "required": ["identifier"], "title": "get_python_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_area_as_image", "description": "\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"area_ui_type": {"enum": ["VIEW_3D", "IMAGE_EDITOR", "UV", "ShaderNodeTree", "CompositorNodeTree", "GeometryNodeTree", "TextureNodeTree", "SEQUENCE_EDITOR", "CLIP_EDITOR", "DOPESHEET_EDITOR", "GRAPH_EDITOR", "NLA_EDITOR", "TEXT_EDITOR", "CONSOLE", "INFO", "TOPBAR", "STATUSBAR", "OUTLINER", "PROPERTIES", "FILE_BROWSER", "SPREADSHEET", "PREFERENCES"], "title": "Area Ui Type", "type": "string"}, "size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "required": ["area_ui_type"], "title": "get_screenshot_of_area_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_image", "description": "\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "title": "get_screenshot_of_window_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_json", "description": "\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ", "parameters": {"properties": {}, "title": "get_screenshot_of_window_as_jsonArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_name", "description": "\n        Switch the active workspace tab to *name*.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "jump_to_tab_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_space_type", "description": "\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ", "parameters": {"properties": {"space_type": {"title": "Space Type", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["space_type"], "title": "jump_to_tab_by_space_typeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_by_name", "description": "\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_data_by_name", "description": "\n        Move the 3D viewport to the object whose data block matches *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_data_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_thumbnail_to_path", "description": "\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_thumbnail_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_viewport_to_path", "description": "\n        Render the current scene to *output_path* using current render settings.\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_viewport_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_api_docs", "description": "\nFull-text search over the bundled Blender Python API reference.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_manual_docs", "description": "\nFull-text search over the bundled Blender user manual.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_manual_docsArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-16T17:34:49.936237+00:00", "finished_at": "2026-07-16T17:35:16.873621+00:00", "duration_s": 26.938000000009197, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "21863", "content-type": "application/json"}, "body": "{\"model\":\"openai/gpt-5.6-luna\",\"messages\":[{\"role\":\"system\",\"content\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\\n\\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls.\"},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\\n\\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\\n\\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\\n\\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\\n\\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\\n\\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\\n\\n</task>\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n        Execute Python code in the connected Blender instance.\\n\\n        The code runs in Blender's Python environment with full access to ``bpy``.\\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\\n        Deferred completion via ``check_is_finished`` is only supported by the\\n        interactive addon server, and is rejected in background mode.\\n        \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code_for_cli\",\"description\":\"\\n        Execute Python code in a background Blender process.\\n\\n        Opens *blend_file* with ``blender --background`` and runs *code*.\\n        Assign a dict to ``result`` to return data.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"},\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"blend_file\",\"code\"],\"title\":\"execute_blender_code_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks\",\"description\":\"\\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_datablocksArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks_for_cli\",\"description\":\"\\n        Return a data-block summary by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_datablocks_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files\",\"description\":\"\\n        Report external file references that are missing from disk\\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_missing_filesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files_for_cli\",\"description\":\"\\n        Report missing file references by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_missing_files_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries\",\"description\":\"\\n        Return a tree of directly and indirectly linked library files.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_of_linked_librariesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries_for_cli\",\"description\":\"\\n        Return linked-library info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_of_linked_libraries_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info\",\"description\":\"\\n        Simple/fast access to the blend file's path, save status, age, and backups.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_path_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info_for_cli\",\"description\":\"\\n        Return path info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_path_info_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess\",\"description\":\"\\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_usage_guessArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess_for_cli\",\"description\":\"\\n        Guess use-cases by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_usage_guess_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_detail_summary\",\"description\":\"\\n        Return a structured summary of the object identified by *name*.\\n\\n        Includes type, transforms, parent, children, modifiers, constraints,\\n        materials, visibility, data-block name, and collections.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"get_object_detail_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_objects_summary\",\"description\":\"\\n        Return the scene's collection hierarchy and their objects.\\n\\n        Each collection lists its objects (name, type, parent, data name,\\n        selection, visibility) and nested child collections.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_objects_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_python_api_docs\",\"description\":\"\\n        Return the Blender Python API docs for *identifier*, or list\\n        modules matching a trailing-``*`` discovery pattern.\\n\\n        *identifier* should be a fully-qualified Python name (e.g.\\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\\n        The trailing-``*`` forms are supported as discovery entry-points:\\n\\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\\n          ``mathutils``, ``gpu``, ...).\\n        - ``X.*`` enumerates the direct-child identifiers under the\\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\\n\\n        Both return a ``namespace`` response even when ``X.rst`` would\\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\\n        force the child listing.\\n\\n        The response always carries ``kind``, ``found``, and ``identifier``.\\n        The remaining keys depend on ``kind``:\\n\\n        - ``\\\"exact\\\"`` (``found=True``): ``<identifier>.rst`` was read.\\n          Extra keys: ``content`` (RST text), ``examples``. When the\\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\\n          summary of the file's top-level definitions (prefixed by a\\n          header noting the truncation) and ``examples`` is empty -\\n          re-query individual members for their rendered blocks.\\n        - ``\\\"namespace\\\"`` (``found=True``):\\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\\n          Extra key: ``submodules`` (list of child identifiers).\\n        - ``\\\"definition\\\"`` (``found=True``):\\n          *identifier* is defined inside a parent RST\\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\\n          Extra keys: ``content`` (rendered block), ``examples``.\\n        - ``\\\"partial\\\"`` (``found=False``):\\n          the parent RST was located but the trailing component isn't defined in it.\\n          Extra keys:\\n          - ``parent`` the identifier whose RST was loaded.\\n          - ``available`` top-level definitions in that RST.\\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\\n            filtered to those whose last component contains every character of the missing tail.\\n\\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\\n        - ``\\\"suggestions\\\"`` (``found=False``):\\n          no direct match, but *identifier* appears as a component of other files.\\n          Extra key: ``suggestions`` (list of full identifiers).\\n        - ``\\\"missing\\\"`` (``found=False``): nothing matched.\\n\\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\\n        is a list of ``{path, content}`` entries referenced from this documentation.\\n        \",\"parameters\":{\"properties\":{\"identifier\":{\"title\":\"Identifier\",\"type\":\"string\"}},\"required\":[\"identifier\"],\"title\":\"get_python_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_area_as_image\",\"description\":\"\\n        Take a screenshot of a single Blender area and return it as a PNG image.\\n\\n        *area_ui_type* matches the area's ``ui_type``.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"area_ui_type\":{\"enum\":[\"VIEW_3D\",\"IMAGE_EDITOR\",\"UV\",\"ShaderNodeTree\",\"CompositorNodeTree\",\"GeometryNodeTree\",\"TextureNodeTree\",\"SEQUENCE_EDITOR\",\"CLIP_EDITOR\",\"DOPESHEET_EDITOR\",\"GRAPH_EDITOR\",\"NLA_EDITOR\",\"TEXT_EDITOR\",\"CONSOLE\",\"INFO\",\"TOPBAR\",\"STATUSBAR\",\"OUTLINER\",\"PROPERTIES\",\"FILE_BROWSER\",\"SPREADSHEET\",\"PREFERENCES\"],\"title\":\"Area Ui Type\",\"type\":\"string\"},\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"required\":[\"area_ui_type\"],\"title\":\"get_screenshot_of_area_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"description\":\"\\n        Take a screenshot of the entire Blender window and return it as a PNG image.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"title\":\"get_screenshot_of_window_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_json\",\"description\":\"\\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_screenshot_of_window_as_jsonArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_name\",\"description\":\"\\n        Switch the active workspace tab to *name*.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"jump_to_tab_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_space_type\",\"description\":\"\\n        Switch to a workspace whose main area matches *space_type*.\\n\\n        If *allow_edits* is True and no matching workspace exists, a new one\\n        is created by duplicating the current workspace.\\n        \",\"parameters\":{\"properties\":{\"space_type\":{\"title\":\"Space Type\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"space_type\"],\"title\":\"jump_to_tab_by_space_typeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_by_name\",\"description\":\"\\n        Move the 3D viewport to focus on an object by *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_data_by_name\",\"description\":\"\\n        Move the 3D viewport to the object whose data block matches *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_data_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_thumbnail_to_path\",\"description\":\"\\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_thumbnail_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_viewport_to_path\",\"description\":\"\\n        Render the current scene to *output_path* using current render settings.\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_viewport_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_api_docs\",\"description\":\"\\nFull-text search over the bundled Blender Python API reference.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_manual_docs\",\"description\":\"\\nFull-text search over the bundled Blender user manual.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_manual_docsArguments\",\"type\":\"object\"}}}]}", "body_base64": "{"model":"openai/gpt-5.6-luna","messages":[{"role":"system","content":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"execute_blender_code","description":"\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ","parameters":{"properties":{"code":{"title":"Code","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code_for_cli","description":"\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"},"code":{"title":"Code","type":"string"}},"required":["blend_file","code"],"title":"execute_blender_code_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks","description":"\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_datablocksArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks_for_cli","description":"\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_datablocks_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files","description":"\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_missing_filesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files_for_cli","description":"\n        Report missing file references by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_missing_files_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries","description":"\n        Return a tree of directly and indirectly linked library files.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_of_linked_librariesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries_for_cli","description":"\n        Return linked-library info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_of_linked_libraries_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info","description":"\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_path_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info_for_cli","description":"\n        Return path info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_path_info_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess","description":"\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_usage_guessArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess_for_cli","description":"\n        Guess use-cases by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_usage_guess_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_detail_summary","description":"\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ","parameters":{"properties":{"name":{"title":"Name","type":"string"}},"required":["name"],"title":"get_object_detail_summaryArguments","type":"object"}}},{"type":"function","function":{"name":"get_objects_summary","description":"\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ","parameters":{"properties":{},"title":"get_objects_summaryArguments","type":"object"}}},{"type":"function","function":{"name":"get_python_api_docs","description":"\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n 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bpy.ops.object.delete(use_global=False)\\\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\\\n    return m\\\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\\\n# helpers\\\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\\\ndef cube(name,loc,scale,material,bev=0):\\\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\\\n    return o\\\\ndef cyl(name,loc,r,depth,material,verts=64):\\\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\\\ndef curve_tube(name, pts, bevel, material):\\\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\\\n# pedestal\\\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\\\n# neck and bust\\\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\\\n# shoulders/broken bust block\\\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\\\n# head cranium and jaw\\\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\\\n# ears\\\\nfor x in (-.69,.69):\\\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\\\n# cheekbones prominent\\\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\\\n# brow ridge\\\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\\\n# eyes, lids, iris raised shallow\\\\nfor x in (-.27,.27):\\\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\\\n# nose bridge and tip custom-ish overlapping smooth forms\\\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\\\n# philtrum\\\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\\\n# lips strong classical\\\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\\\n# chin plane\\\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\\\n# hair cap\\\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\\\n# hairline curls across forehead and temples, actual tubes\\\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\\\n    z=3.55-0.06*(abs(x)/.6)\\\\n    # curl descending front, slight spiral-ish polyline\\\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\\\n# side locks and crown rows\\\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\\\n        # clumped wave arcs\\\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\\\n        curve_tube('Crown wave',pts,.085,plaster)\\\\n# side curls near ears\\\\nfor side in (-1,1):\\\\n for j in range(4):\\\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\\\n# subtle casting seam collar ring\\\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\\\n# ground/backdrop large plane\\\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\\\n# backdrop vertical plane behind y=2\\\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(0,0,2.75))\\\\n# lights\\\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\\\n# world/render\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\\\n# organize collections? save\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\\\"}\"}}],\"reasoning_details\":[{\"type\":\"reasoning.encrypted\",\"data\":\"gAAAAABqWRZJO1u_L0ju09yrO1XmVV32_pm0RVq1rBqfT_x0HpOV76-HQG6gafvx1ZqvuxmgQG0Bx3-Qy1RFuohRDjdrCJ8lJRqzDdgPWDm1n9pi-8-46pKMJ_W_IJCd3zBkCg2vJJJM3xFIeHVLhWoG0hgms15fg86SepxymG3cO5Wq3ht211XM2P6C9nuePOVTWfBFPmg3O250em-yR5eTxqFEPs4P6DHuTG7H1Jlay1QyC2jSBJQXoDCfYMUVGbdw9AKbHz3_ijXn5d1MWprh73Zx-wwaiWXSWlbdLTjiaxHADUIGCDexlXX2ui3IYGN6AHGLfL-nQJXgznQNh0aY6yhcZmCY6Kn5T-7RqDsJFkY162KKyIB28prbTQk5oerSX5tr_aRocv4WNUfUxg1pGQVbflgs-pV60LBZpTXAX9Noiel0elRYiVG8tb2pYkrGV_BPZcBcisjom2KH8rJlsKx_LJtHHhHRYUh31PmD0WwZTXwq5-obdzuVKlfG5YSMRWJxiQk0POUyVy_yXfQJW1ENpP3jwwhH878LiGcQmzhFibSjSydDpp-37ujRoasiF2aaFQuilH9S_Lyb91nl1xxbRBcuLyyU8zbnGrXS55B2E0KSotvueU4xVcc_U3Q0CjbhCje7cZWWEIMiAoacXBtvWZ-0w5hXDkvpi-y8f2Mzt2X92jDY8s9KEQEqJuID1ASmP_alMD4ihEDdmGfIWF3j8mWlrvadLj76KHY0IQ6Gjo_Hu4Sf-rWov0QDOLhgqjjyFDSKA7wVZZN4D-Ycq-WG0p5t_GKGmNUMlo-mqAjjg-V4Bm2KyBzYob8GXP8hVDOYqnu4MsKC9j7duVWH7FFJ8rQPmJL2kxyOu10Fjgab7mTk6RNjJUMpGXqiw1hccVsL8tLWyf5jEEuYdZmAV8M1IeNvWFHf316iNPUuhklVfIFoB8oUbxlpokQkTlJRKHmAV0bzmywLvoYLtvnIaGC9DZ579PTnpk6u63jd1UDwRO_Q8s4knpDzv584YISSLo0d0wEhvfXvDptm3XwGmx4hfILIRtn2P13YHm4ldTNnSiKAhMfe1JkNNRvtM-aCWW9F7HFky1aYXh3pZd6nxp2io3yR3OdpYu_8gdBrAFyJMLXr4ih7D7waCMRSskfh3O8vseF5vdXlzavI6FXFoCKNF-UExVhyo66RyIBMt0XX9FQncqCjqQ3JD2SX4uQBnfb7jDskVLEVM6Im9Ik7DXHMG9Ii0BFNSbYMbq7rOnzrbBDdCYCm-KZkKPXnq6MR5uRxUiARPS7LrXmFXX8gq7j4nqo-vF0rErA1DanYx8dlqi58TSXj5GlufjF14z63QiP_xj6UnI3lz-dvhLlHTtvqvReu30-3fItu53aZV7DBTamjoNW4Es0IgGO9hI5TiKQVINa3VmAW6Zb25wed2XEXvjmCrFNTVIw4jmNA-ZMiQ7Q1z-WpLTe7GwLJd5DyMjHqApbeDpLhbKDcNbI6XHHaZjAIS1ZiRSWnQ5Dr5e2f-4SG4Lo-7svqEvefcgdca6gKUcNm5M79HbCPDyZOZhLqkKjHIfszDNO9Mb7_bPC1qeR2-xwVELO_iK61wlvzI1PsQsyido9AwZ_psGJNiFP9MHQN2yNwaEKvZ9habQG3dfEO_SEGjrqu1nwoVX7EwLwjb9fIYhDHF5V7gsI1w36Sy2vxIUF5wW4tMT7BYfVlGXXUUXFjO7qLXgx3QxavMqLCqOBIvXPR_z8QFgzkmIBwuZgocCZcH3eA62ws5DHVuQ2gVDLO9FbukW8KK_b7PrxWP6SK7SktKJrn5czKqRBL3HiQ34Uck99ZVzSsSwSelK3DXh7AsodrGRmLJ9jp5c36ZOYOXhZEAJ8sx_Li4iMerKSzEmbLwQp1Dm_xueK4ditfJ6wgYywthxLA4VELdHTH7PKxseOJmRy0gt4pg6xKnI4WBEXAFVgTg2cH8fd0Wg2aHDi6WezP0PPZC8fIfijhoT4tOmQnpaW6eUT-7WtqSgWurw==\",\"format\":\"openai-responses-v1\",\"id\":\"rs_004358d91ae47a09016a591647e9808190aaf06bb38ac478f7\",\"index\":0}]}}],\"usage\":{\"prompt_tokens\":3999,\"completion_tokens\":2631,\"total_tokens\":6630,\"cost\":0.020784,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":0,\"cache_write_tokens\":3996,\"audio_tokens\":0,\"video_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0.020784,\"upstream_inference_prompt_cost\":0.004998,\"upstream_inference_completions_cost\":0.015786},\"completion_tokens_details\":{\"reasoning_tokens\":182,\"image_tokens\":0,\"audio_tokens\":0}}}", "body_base64": "
         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         
{"id":"gen-1784223303-oLLnsv5MOh6UPfpZjakW","object":"chat.completion","created":1784223303,"model":"openai/gpt-5.6-luna","provider":"OpenAI","system_fingerprint":null,"service_tier":"default","choices":[{"index":0,"logprobs":null,"finish_reason":"tool_calls","native_finish_reason":"completed","message":{"role":"assistant","content":null,"refusal":null,"reasoning":null,"tool_calls":[{"type":"function","index":0,"id":"call_EiAaMRToSKz0xJ3cuNin8l6H","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}],"reasoning_details":[{"type":"reasoning.encrypted","data":"gAAAAABqWRZJO1u_L0ju09yrO1XmVV32_pm0RVq1rBqfT_x0HpOV76-HQG6gafvx1ZqvuxmgQG0Bx3-Qy1RFuohRDjdrCJ8lJRqzDdgPWDm1n9pi-8-46pKMJ_W_IJCd3zBkCg2vJJJM3xFIeHVLhWoG0hgms15fg86SepxymG3cO5Wq3ht211XM2P6C9nuePOVTWfBFPmg3O250em-yR5eTxqFEPs4P6DHuTG7H1Jlay1QyC2jSBJQXoDCfYMUVGbdw9AKbHz3_ijXn5d1MWprh73Zx-wwaiWXSWlbdLTjiaxHADUIGCDexlXX2ui3IYGN6AHGLfL-nQJXgznQNh0aY6yhcZmCY6Kn5T-7RqDsJFkY162KKyIB28prbTQk5oerSX5tr_aRocv4WNUfUxg1pGQVbflgs-pV60LBZpTXAX9Noiel0elRYiVG8tb2pYkrGV_BPZcBcisjom2KH8rJlsKx_LJtHHhHRYUh31PmD0WwZTXwq5-obdzuVKlfG5YSMRWJxiQk0POUyVy_yXfQJW1ENpP3jwwhH878LiGcQmzhFibSjSydDpp-37ujRoasiF2aaFQuilH9S_Lyb91nl1xxbRBcuLyyU8zbnGrXS55B2E0KSotvueU4xVcc_U3Q0CjbhCje7cZWWEIMiAoacXBtvWZ-0w5hXDkvpi-y8f2Mzt2X92jDY8s9KEQEqJuID1ASmP_alMD4ihEDdmGfIWF3j8mWlrvadLj76KHY0IQ6Gjo_Hu4Sf-rWov0QDOLhgqjjyFDSKA7wVZZN4D-Ycq-WG0p5t_GKGmNUMlo-mqAjjg-V4Bm2KyBzYob8GXP8hVDOYqnu4MsKC9j7duVWH7FFJ8rQPmJL2kxyOu10Fjgab7mTk6RNjJUMpGXqiw1hccVsL8tLWyf5jEEuYdZmAV8M1IeNvWFHf316iNPUuhklVfIFoB8oUbxlpokQkTlJRKHmAV0bzmywLvoYLtvnIaGC9DZ579PTnpk6u63jd1UDwRO_Q8s4knpDzv584YISSLo0d0wEhvfXvDptm3XwGmx4hfILIRtn2P13YHm4ldTNnSiKAhMfe1JkNNRvtM-aCWW9F7HFky1aYXh3pZd6nxp2io3yR3OdpYu_8gdBrAFyJMLXr4ih7D7waCMRSskfh3O8vseF5vdXlzavI6FXFoCKNF-UExVhyo66RyIBMt0XX9FQncqCjqQ3JD2SX4uQBnfb7jDskVLEVM6Im9Ik7DXHMG9Ii0BFNSbYMbq7rOnzrbBDdCYCm-KZkKPXnq6MR5uRxUiARPS7LrXmFXX8gq7j4nqo-vF0rErA1DanYx8dlqi58TSXj5GlufjF14z63QiP_xj6UnI3lz-dvhLlHTtvqvReu30-3fItu53aZV7DBTamjoNW4Es0IgGO9hI5TiKQVINa3VmAW6Zb25wed2XEXvjmCrFNTVIw4jmNA-ZMiQ7Q1z-WpLTe7GwLJd5DyMjHqApbeDpLhbKDcNbI6XHHaZjAIS1ZiRSWnQ5Dr5e2f-4SG4Lo-7svqEvefcgdca6gKUcNm5M79HbCPDyZOZhLqkKjHIfszDNO9Mb7_bPC1qeR2-xwVELO_iK61wlvzI1PsQsyido9AwZ_psGJNiFP9MHQN2yNwaEKvZ9habQG3dfEO_SEGjrqu1nwoVX7EwLwjb9fIYhDHF5V7gsI1w36Sy2vxIUF5wW4tMT7BYfVlGXXUUXFjO7qLXgx3QxavMqLCqOBIvXPR_z8QFgzkmIBwuZgocCZcH3eA62ws5DHVuQ2gVDLO9FbukW8KK_b7PrxWP6SK7SktKJrn5czKqRBL3HiQ34Uck99ZVzSsSwSelK3DXh7AsodrGRmLJ9jp5c36ZOYOXhZEAJ8sx_Li4iMerKSzEmbLwQp1Dm_xueK4ditfJ6wgYywthxLA4VELdHTH7PKxseOJmRy0gt4pg6xKnI4WBEXAFVgTg2cH8fd0Wg2aHDi6WezP0PPZC8fIfijhoT4tOmQnpaW6eUT-7WtqSgWurw==","format":"openai-responses-v1","id":"rs_004358d91ae47a09016a591647e9808190aaf06bb38ac478f7","index":0}]}}],"usage":{"prompt_tokens":3999,"completion_tokens":2631,"total_tokens":6630,"cost":0.020784,"is_byok":false,"prompt_tokens_details":{"cached_tokens":0,"cache_write_tokens":3996,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.020784,"upstream_inference_prompt_cost":0.004998,"upstream_inference_completions_cost":0.015786},"completion_tokens_details":{"reasoning_tokens":182,"image_tokens":0,"audio_tokens":0}}}"}, "error": null}], "response": {"text": null, "tool_calls": [{"id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "name": "execute_blender_code", "arguments": {"code": "import bpy, math\nfrom mathutils import Vector\n# clear\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\n    pass\n# materials\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\n    return m\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\n# helpers\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\ndef cube(name,loc,scale,material,bev=0):\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\n    return o\ndef cyl(name,loc,r,depth,material,verts=64):\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\ndef curve_tube(name, pts, bevel, material):\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\n# pedestal\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\n# neck and bust\nuv('Neck',(0,0,2.28),(.47,.42,.78))\n# shoulders/broken bust block\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\n# head cranium and jaw\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\n# ears\nfor x in (-.69,.69):\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\n# cheekbones prominent\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\n# brow ridge\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\n# eyes, lids, iris raised shallow\nfor x in (-.27,.27):\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\n# nose bridge and tip custom-ish overlapping smooth forms\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\n# philtrum\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\n# lips strong classical\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\n# chin plane\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\n# hair cap\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\n# hairline curls across forehead and temples, actual tubes\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\n    z=3.55-0.06*(abs(x)/.6)\n    # curl descending front, slight spiral-ish polyline\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\n# side locks and crown rows\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\n        # clumped wave arcs\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\n        curve_tube('Crown wave',pts,.085,plaster)\n# side curls near ears\nfor side in (-1,1):\n for j in range(4):\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\n# subtle casting seam collar ring\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\n# ground/backdrop large plane\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\n# backdrop vertical plane behind y=2\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\n# camera\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\ntrack(cam,(0,0,2.75))\n# lights\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\n# world/render\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\n# organize collections? save\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\nbpy.ops.render.render(write_still=True)\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}"}, "raw_arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}", "parse_error": null}], "tokens_in": 3999, "tokens_out": 2631, "cost_usd": 0.020784, "cached_tokens": 0}, "error": null}
{"ts": "2026-07-16T17:35:16.875612+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "name": "execute_blender_code", "arguments": {"code": "import bpy, math\nfrom mathutils import Vector\n# clear\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\n    pass\n# materials\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\n    return m\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\n# helpers\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\ndef cube(name,loc,scale,material,bev=0):\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\n    return o\ndef cyl(name,loc,r,depth,material,verts=64):\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\ndef curve_tube(name, pts, bevel, material):\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\n# pedestal\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\n# neck and bust\nuv('Neck',(0,0,2.28),(.47,.42,.78))\n# shoulders/broken bust block\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\n# head cranium and jaw\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\n# ears\nfor x in (-.69,.69):\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\n# cheekbones prominent\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\n# brow ridge\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\n# eyes, lids, iris raised shallow\nfor x in (-.27,.27):\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\n# nose bridge and tip custom-ish overlapping smooth forms\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\n# philtrum\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\n# lips strong classical\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\n# chin plane\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\n# hair cap\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\n# hairline curls across forehead and temples, actual tubes\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\n    z=3.55-0.06*(abs(x)/.6)\n    # curl descending front, slight spiral-ish polyline\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\n# side locks and crown rows\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\n        # clumped wave arcs\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\n        curve_tube('Crown wave',pts,.085,plaster)\n# side curls near ears\nfor side in (-1,1):\n for j in range(4):\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\n# subtle casting seam collar ring\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\n# ground/backdrop large plane\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\n# backdrop vertical plane behind y=2\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\n# camera\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\ntrack(cam,(0,0,2.75))\n# lights\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\n# world/render\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\n# organize collections? save\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\nbpy.ops.render.render(write_still=True)\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}"}, "raw_arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}", "parse_error": null}]}
{"ts": "2026-07-16T17:35:16.875612+00:00", "type": "tool_call", "id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "name": "execute_blender_code", "arguments": {"code": "import bpy, math\nfrom mathutils import Vector\n# clear\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\n    pass\n# materials\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\n    return m\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\n# helpers\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\ndef cube(name,loc,scale,material,bev=0):\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\n    return o\ndef cyl(name,loc,r,depth,material,verts=64):\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\ndef curve_tube(name, pts, bevel, material):\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\n# pedestal\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\n# neck and bust\nuv('Neck',(0,0,2.28),(.47,.42,.78))\n# shoulders/broken bust block\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\n# head cranium and jaw\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\n# ears\nfor x in (-.69,.69):\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\n# cheekbones prominent\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\n# brow ridge\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\n# eyes, lids, iris raised shallow\nfor x in (-.27,.27):\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\n# nose bridge and tip custom-ish overlapping smooth forms\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\n# philtrum\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\n# lips strong classical\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\n# chin plane\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\n# hair cap\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\n# hairline curls across forehead and temples, actual tubes\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\n    z=3.55-0.06*(abs(x)/.6)\n    # curl descending front, slight spiral-ish polyline\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\n# side locks and crown rows\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\n        # clumped wave arcs\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\n        curve_tube('Crown wave',pts,.085,plaster)\n# side curls near ears\nfor side in (-1,1):\n for j in range(4):\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\n# subtle casting seam collar ring\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\n# ground/backdrop large plane\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\n# backdrop vertical plane behind y=2\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\n# camera\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\ntrack(cam,(0,0,2.75))\n# lights\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\n# world/render\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\n# organize collections? save\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\nbpy.ops.render.render(write_still=True)\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}"}, "raw_arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}
{"ts": "2026-07-16T17:35:19.247104+00:00", "type": "tool_result", "tool_call_id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "name": "execute_blender_code", "text": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 103, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-16T17:35:21.522631+00:00", "type": "llm_call", "call_index": 2, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-16T17:35:19.248113+00:00", "finished_at": "2026-07-16T17:35:21.521620+00:00", "duration_s": 2.264999999999418, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 103, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"}], "tools": [{"type": "function", "function": {"name": "execute_blender_code", "description": "\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code_for_cli", "description": "\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}, "code": {"title": "Code", "type": "string"}}, "required": ["blend_file", "code"], "title": "execute_blender_code_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks", "description": "\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_datablocksArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks_for_cli", "description": "\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_datablocks_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files", "description": "\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_missing_filesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files_for_cli", "description": "\n        Report missing file references by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_missing_files_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries", "description": "\n        Return a tree of directly and indirectly linked library files.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_of_linked_librariesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries_for_cli", "description": "\n        Return linked-library info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_of_linked_libraries_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info", "description": "\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_path_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info_for_cli", "description": "\n        Return path info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_path_info_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess", "description": "\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_usage_guessArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess_for_cli", "description": "\n        Guess use-cases by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_usage_guess_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_detail_summary", "description": "\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "get_object_detail_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_objects_summary", "description": "\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ", "parameters": {"properties": {}, "title": "get_objects_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_python_api_docs", "description": "\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ", "parameters": {"properties": {"identifier": {"title": "Identifier", "type": "string"}}, "required": ["identifier"], "title": "get_python_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_area_as_image", "description": "\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"area_ui_type": {"enum": ["VIEW_3D", "IMAGE_EDITOR", "UV", "ShaderNodeTree", "CompositorNodeTree", "GeometryNodeTree", "TextureNodeTree", "SEQUENCE_EDITOR", "CLIP_EDITOR", "DOPESHEET_EDITOR", "GRAPH_EDITOR", "NLA_EDITOR", "TEXT_EDITOR", "CONSOLE", "INFO", "TOPBAR", "STATUSBAR", "OUTLINER", "PROPERTIES", "FILE_BROWSER", "SPREADSHEET", "PREFERENCES"], "title": "Area Ui Type", "type": "string"}, "size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "required": ["area_ui_type"], "title": "get_screenshot_of_area_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_image", "description": "\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "title": "get_screenshot_of_window_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_json", "description": "\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ", "parameters": {"properties": {}, "title": "get_screenshot_of_window_as_jsonArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_name", "description": "\n        Switch the active workspace tab to *name*.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "jump_to_tab_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_space_type", "description": "\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ", "parameters": {"properties": {"space_type": {"title": "Space Type", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["space_type"], "title": "jump_to_tab_by_space_typeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_by_name", "description": "\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_data_by_name", "description": "\n        Move the 3D viewport to the object whose data block matches *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_data_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_thumbnail_to_path", "description": "\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_thumbnail_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_viewport_to_path", "description": "\n        Render the current scene to *output_path* using current render settings.\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_viewport_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_api_docs", "description": "\nFull-text search over the bundled Blender Python API reference.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_manual_docs", "description": "\nFull-text search over the bundled Blender user manual.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_manual_docsArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-16T17:35:19.248113+00:00", "finished_at": "2026-07-16T17:35:21.521620+00:00", "duration_s": 2.264999999999418, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "29558", "content-type": "application/json"}, "body": "{\"model\":\"openai/gpt-5.6-luna\",\"messages\":[{\"role\":\"system\",\"content\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\\n\\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls.\"},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\\n\\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\\n\\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\\n\\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\\n\\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\\n\\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_EiAaMRToSKz0xJ3cuNin8l6H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy, math\\\\nfrom mathutils import Vector\\\\n# clear\\\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\\\n    return m\\\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\\\n# helpers\\\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\\\ndef cube(name,loc,scale,material,bev=0):\\\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\\\n    return o\\\\ndef cyl(name,loc,r,depth,material,verts=64):\\\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\\\ndef curve_tube(name, pts, bevel, material):\\\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\\\n# pedestal\\\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\\\n# neck and bust\\\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\\\n# shoulders/broken bust block\\\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\\\n# head cranium and jaw\\\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\\\n# ears\\\\nfor x in (-.69,.69):\\\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\\\n# cheekbones prominent\\\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\\\n# brow ridge\\\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\\\n# eyes, lids, iris raised shallow\\\\nfor x in (-.27,.27):\\\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\\\n# nose bridge and tip custom-ish overlapping smooth forms\\\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\\\n# philtrum\\\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\\\n# lips strong classical\\\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\\\n# chin plane\\\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\\\n# hair cap\\\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\\\n# hairline curls across forehead and temples, actual tubes\\\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\\\n    z=3.55-0.06*(abs(x)/.6)\\\\n    # curl descending front, slight spiral-ish polyline\\\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\\\n# side locks and crown rows\\\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\\\n        # clumped wave arcs\\\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\\\n        curve_tube('Crown wave',pts,.085,plaster)\\\\n# side curls near ears\\\\nfor side in (-1,1):\\\\n for j in range(4):\\\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\\\n# subtle casting seam collar ring\\\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\\\n# ground/backdrop large plane\\\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\\\n# backdrop vertical plane behind y=2\\\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(0,0,2.75))\\\\n# lights\\\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\\\n# world/render\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\\\n# organize collections? save\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_EiAaMRToSKz0xJ3cuNin8l6H\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 103, in <module>\\\\nTypeError: bpy_struct: item.attr = val: enum \\\\\\\"BLENDER_EEVEE_NEXT\\\\\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\\\n\\\"\\n}\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n        Execute Python code in the connected Blender instance.\\n\\n        The code runs in Blender's Python environment with full access to ``bpy``.\\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\\n        Deferred completion via ``check_is_finished`` is only supported by the\\n        interactive addon server, and is rejected in background mode.\\n        \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code_for_cli\",\"description\":\"\\n        Execute Python code in a background Blender process.\\n\\n        Opens *blend_file* with ``blender --background`` and runs *code*.\\n        Assign a dict to ``result`` to return data.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"},\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"blend_file\",\"code\"],\"title\":\"execute_blender_code_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks\",\"description\":\"\\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_datablocksArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks_for_cli\",\"description\":\"\\n        Return a data-block summary by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_datablocks_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files\",\"description\":\"\\n        Report external file references that are missing from disk\\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_missing_filesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files_for_cli\",\"description\":\"\\n        Report missing file references by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_missing_files_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries\",\"description\":\"\\n        Return a tree of directly and indirectly linked library files.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_of_linked_librariesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries_for_cli\",\"description\":\"\\n        Return linked-library info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_of_linked_libraries_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info\",\"description\":\"\\n        Simple/fast access to the blend file's path, save status, age, and backups.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_path_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info_for_cli\",\"description\":\"\\n        Return path info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_path_info_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess\",\"description\":\"\\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_usage_guessArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess_for_cli\",\"description\":\"\\n        Guess use-cases by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_usage_guess_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_detail_summary\",\"description\":\"\\n        Return a structured summary of the object identified by *name*.\\n\\n        Includes type, transforms, parent, children, modifiers, constraints,\\n        materials, visibility, data-block name, and collections.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"get_object_detail_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_objects_summary\",\"description\":\"\\n        Return the scene's collection hierarchy and their objects.\\n\\n        Each collection lists its objects (name, type, parent, data name,\\n        selection, visibility) and nested child collections.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_objects_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_python_api_docs\",\"description\":\"\\n        Return the Blender Python API docs for *identifier*, or list\\n        modules matching a trailing-``*`` discovery pattern.\\n\\n        *identifier* should be a fully-qualified Python name (e.g.\\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\\n        The trailing-``*`` forms are supported as discovery entry-points:\\n\\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\\n          ``mathutils``, ``gpu``, ...).\\n        - ``X.*`` enumerates the direct-child identifiers under the\\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\\n\\n        Both return a ``namespace`` response even when ``X.rst`` would\\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\\n        force the child listing.\\n\\n        The response always carries ``kind``, ``found``, and ``identifier``.\\n        The remaining keys depend on ``kind``:\\n\\n        - ``\\\"exact\\\"`` (``found=True``): ``<identifier>.rst`` was read.\\n          Extra keys: ``content`` (RST text), ``examples``. When the\\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\\n          summary of the file's top-level definitions (prefixed by a\\n          header noting the truncation) and ``examples`` is empty -\\n          re-query individual members for their rendered blocks.\\n        - ``\\\"namespace\\\"`` (``found=True``):\\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\\n          Extra key: ``submodules`` (list of child identifiers).\\n        - ``\\\"definition\\\"`` (``found=True``):\\n          *identifier* is defined inside a parent RST\\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\\n          Extra keys: ``content`` (rendered block), ``examples``.\\n        - ``\\\"partial\\\"`` (``found=False``):\\n          the parent RST was located but the trailing component isn't defined in it.\\n          Extra keys:\\n          - ``parent`` the identifier whose RST was loaded.\\n          - ``available`` top-level definitions in that RST.\\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\\n            filtered to those whose last component contains every character of the missing tail.\\n\\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\\n        - ``\\\"suggestions\\\"`` (``found=False``):\\n          no direct match, but *identifier* appears as a component of other files.\\n          Extra key: ``suggestions`` (list of full identifiers).\\n        - ``\\\"missing\\\"`` (``found=False``): nothing matched.\\n\\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\\n        is a list of ``{path, content}`` entries referenced from this documentation.\\n        \",\"parameters\":{\"properties\":{\"identifier\":{\"title\":\"Identifier\",\"type\":\"string\"}},\"required\":[\"identifier\"],\"title\":\"get_python_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_area_as_image\",\"description\":\"\\n        Take a screenshot of a single Blender area and return it as a PNG image.\\n\\n        *area_ui_type* matches the area's ``ui_type``.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"area_ui_type\":{\"enum\":[\"VIEW_3D\",\"IMAGE_EDITOR\",\"UV\",\"ShaderNodeTree\",\"CompositorNodeTree\",\"GeometryNodeTree\",\"TextureNodeTree\",\"SEQUENCE_EDITOR\",\"CLIP_EDITOR\",\"DOPESHEET_EDITOR\",\"GRAPH_EDITOR\",\"NLA_EDITOR\",\"TEXT_EDITOR\",\"CONSOLE\",\"INFO\",\"TOPBAR\",\"STATUSBAR\",\"OUTLINER\",\"PROPERTIES\",\"FILE_BROWSER\",\"SPREADSHEET\",\"PREFERENCES\"],\"title\":\"Area Ui Type\",\"type\":\"string\"},\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"required\":[\"area_ui_type\"],\"title\":\"get_screenshot_of_area_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"description\":\"\\n        Take a screenshot of the entire Blender window and return it as a PNG image.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"title\":\"get_screenshot_of_window_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_json\",\"description\":\"\\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_screenshot_of_window_as_jsonArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_name\",\"description\":\"\\n        Switch the active workspace tab to *name*.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"jump_to_tab_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_space_type\",\"description\":\"\\n        Switch to a workspace whose main area matches *space_type*.\\n\\n        If *allow_edits* is True and no matching workspace exists, a new one\\n        is created by duplicating the current workspace.\\n        \",\"parameters\":{\"properties\":{\"space_type\":{\"title\":\"Space Type\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"space_type\"],\"title\":\"jump_to_tab_by_space_typeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_by_name\",\"description\":\"\\n        Move the 3D viewport to focus on an object by *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_data_by_name\",\"description\":\"\\n        Move the 3D viewport to the object whose data block matches *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_data_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_thumbnail_to_path\",\"description\":\"\\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_thumbnail_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_viewport_to_path\",\"description\":\"\\n        Render the current scene to *output_path* using current render settings.\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_viewport_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_api_docs\",\"description\":\"\\nFull-text search over the bundled Blender Python API reference.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_manual_docs\",\"description\":\"\\nFull-text search over the bundled Blender user manual.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_manual_docsArguments\",\"type\":\"object\"}}}]}", "body_base64": "{"model":"openai/gpt-5.6-luna","messages":[{"role":"system","content":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_EiAaMRToSKz0xJ3cuNin8l6H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]},{"role":"tool","tool_call_id":"call_EiAaMRToSKz0xJ3cuNin8l6H","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 103, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"execute_blender_code","description":"\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ","parameters":{"properties":{"code":{"title":"Code","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code_for_cli","description":"\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"},"code":{"title":"Code","type":"string"}},"required":["blend_file","code"],"title":"execute_blender_code_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks","description":"\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_datablocksArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks_for_cli","description":"\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_datablocks_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files","description":"\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_missing_filesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files_for_cli","description":"\n        Report missing file references by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_missing_files_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries","description":"\n        Return a tree of directly and indirectly linked library files.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_of_linked_librariesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries_for_cli","description":"\n        Return linked-library info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_of_linked_libraries_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info","description":"\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_path_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info_for_cli","description":"\n        Return path info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_path_info_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess","description":"\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_usage_guessArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess_for_cli","description":"\n        Guess use-cases by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_usage_guess_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_detail_summary","description":"\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ","parameters":{"properties":{"name":{"title":"Name","type":"string"}},"required":["name"],"title":"get_object_detail_summaryArguments","type":"object"}}},{"type":"function","function":{"name":"get_objects_summary","description":"\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ","parameters":{"properties":{},"title":"get_objects_summaryArguments","type":"object"}}},{"type":"function","function":{"name":"get_python_api_docs","description":"\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        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{"ts": "2026-07-16T17:35:21.523136+00:00", "type": "tool_call", "id": "call_eaQymwe5S0AnbTBEr8NAI4wi", "name": "execute_blender_code", "arguments": {"code": "import bpy\nsc=bpy.context.scene\nsc.render.engine='BLENDER_EEVEE'\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\nsc.world.color=(.035,.03,.025)\ntry: sc.view_settings.look='AgX - Medium High Contrast'\nexcept: pass\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\nbpy.ops.render.render(write_still=True)\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}"}, "raw_arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}
{"ts": "2026-07-16T17:35:21.597446+00:00", "type": "tool_result", "tool_call_id": "call_eaQymwe5S0AnbTBEr8NAI4wi", "name": "execute_blender_code", "text": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 9, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\"\n}", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-16T17:35:24.567521+00:00", "type": "llm_call", "call_index": 3, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-16T17:35:21.597446+00:00", "finished_at": "2026-07-16T17:35:24.567521+00:00", "duration_s": 2.9679999999934807, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 103, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_eaQymwe5S0AnbTBEr8NAI4wi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_eaQymwe5S0AnbTBEr8NAI4wi", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 9, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\"\n}"}], "tools": [{"type": "function", "function": {"name": "execute_blender_code", "description": "\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code_for_cli", "description": "\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}, "code": {"title": "Code", "type": "string"}}, "required": ["blend_file", "code"], "title": "execute_blender_code_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks", "description": "\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_datablocksArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks_for_cli", "description": "\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_datablocks_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files", "description": "\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_missing_filesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files_for_cli", "description": "\n        Report missing file references by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_missing_files_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries", "description": "\n        Return a tree of directly and indirectly linked library files.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_of_linked_librariesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries_for_cli", "description": "\n        Return linked-library info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_of_linked_libraries_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info", "description": "\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_path_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info_for_cli", "description": "\n        Return path info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_path_info_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess", "description": "\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_usage_guessArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess_for_cli", "description": "\n        Guess use-cases by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_usage_guess_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_detail_summary", "description": "\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "get_object_detail_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_objects_summary", "description": "\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ", "parameters": {"properties": {}, "title": "get_objects_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_python_api_docs", "description": "\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ", "parameters": {"properties": {"identifier": {"title": "Identifier", "type": "string"}}, "required": ["identifier"], "title": "get_python_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_area_as_image", "description": "\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"area_ui_type": {"enum": ["VIEW_3D", "IMAGE_EDITOR", "UV", "ShaderNodeTree", "CompositorNodeTree", "GeometryNodeTree", "TextureNodeTree", "SEQUENCE_EDITOR", "CLIP_EDITOR", "DOPESHEET_EDITOR", "GRAPH_EDITOR", "NLA_EDITOR", "TEXT_EDITOR", "CONSOLE", "INFO", "TOPBAR", "STATUSBAR", "OUTLINER", "PROPERTIES", "FILE_BROWSER", "SPREADSHEET", "PREFERENCES"], "title": "Area Ui Type", "type": "string"}, "size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "required": ["area_ui_type"], "title": "get_screenshot_of_area_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_image", "description": "\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "title": "get_screenshot_of_window_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_json", "description": "\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ", "parameters": {"properties": {}, "title": "get_screenshot_of_window_as_jsonArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_name", "description": "\n        Switch the active workspace tab to *name*.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "jump_to_tab_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_space_type", "description": "\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ", "parameters": {"properties": {"space_type": {"title": "Space Type", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["space_type"], "title": "jump_to_tab_by_space_typeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_by_name", "description": "\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_data_by_name", "description": "\n        Move the 3D viewport to the object whose data block matches *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_data_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_thumbnail_to_path", "description": "\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_thumbnail_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_viewport_to_path", "description": "\n        Render the current scene to *output_path* using current render settings.\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_viewport_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_api_docs", "description": "\nFull-text search over the bundled Blender Python API reference.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_manual_docs", "description": "\nFull-text search over the bundled Blender user manual.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_manual_docsArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-16T17:35:21.597446+00:00", "finished_at": "2026-07-16T17:35:24.567521+00:00", "duration_s": 2.9679999999934807, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "31237", "content-type": "application/json"}, "body": "{\"model\":\"openai/gpt-5.6-luna\",\"messages\":[{\"role\":\"system\",\"content\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\\n\\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls.\"},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\\n\\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\\n\\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\\n\\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\\n\\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\\n\\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_EiAaMRToSKz0xJ3cuNin8l6H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy, math\\\\nfrom mathutils import Vector\\\\n# clear\\\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\\\n    return m\\\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\\\n# helpers\\\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\\\ndef cube(name,loc,scale,material,bev=0):\\\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\\\n    return o\\\\ndef cyl(name,loc,r,depth,material,verts=64):\\\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\\\ndef curve_tube(name, pts, bevel, material):\\\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\\\n# pedestal\\\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\\\n# neck and bust\\\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\\\n# shoulders/broken bust block\\\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\\\n# head cranium and jaw\\\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\\\n# ears\\\\nfor x in (-.69,.69):\\\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\\\n# cheekbones prominent\\\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\\\n# brow ridge\\\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\\\n# eyes, lids, iris raised shallow\\\\nfor x in (-.27,.27):\\\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\\\n# nose bridge and tip custom-ish overlapping smooth forms\\\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\\\n# philtrum\\\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\\\n# lips strong classical\\\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\\\n# chin plane\\\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\\\n# hair cap\\\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\\\n# hairline curls across forehead and temples, actual tubes\\\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\\\n    z=3.55-0.06*(abs(x)/.6)\\\\n    # curl descending front, slight spiral-ish polyline\\\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\\\n# side locks and crown rows\\\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\\\n        # clumped wave arcs\\\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\\\n        curve_tube('Crown wave',pts,.085,plaster)\\\\n# side curls near ears\\\\nfor side in (-1,1):\\\\n for j in range(4):\\\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\\\n# subtle casting seam collar ring\\\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\\\n# ground/backdrop large plane\\\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\\\n# backdrop vertical plane behind y=2\\\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(0,0,2.75))\\\\n# lights\\\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\\\n# world/render\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\\\n# organize collections? save\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_EiAaMRToSKz0xJ3cuNin8l6H\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 103, in <module>\\\\nTypeError: bpy_struct: item.attr = val: enum \\\\\\\"BLENDER_EEVEE_NEXT\\\\\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_eaQymwe5S0AnbTBEr8NAI4wi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene\\\\nsc.render.engine='BLENDER_EEVEE'\\\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\\\nsc.world.color=(.035,.03,.025)\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_eaQymwe5S0AnbTBEr8NAI4wi\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 9, in <module>\\\\nRuntimeError: Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_bust.blend@ for writing: No such file or directory\\\\n\\\\n\\\",\\n  \\\"stdout\\\": \\\"Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_bust.blend@ for writing: No such file or directory\\\\n\\\"\\n}\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n        Execute Python code in the connected Blender instance.\\n\\n        The code runs in Blender's Python environment with full access to ``bpy``.\\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\\n        Deferred completion via ``check_is_finished`` is only supported by the\\n        interactive addon server, and is rejected in background mode.\\n        \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code_for_cli\",\"description\":\"\\n        Execute Python code in a background Blender process.\\n\\n        Opens *blend_file* with ``blender --background`` and runs *code*.\\n        Assign a dict to ``result`` to return data.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"},\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"blend_file\",\"code\"],\"title\":\"execute_blender_code_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks\",\"description\":\"\\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_datablocksArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks_for_cli\",\"description\":\"\\n        Return a data-block summary by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_datablocks_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files\",\"description\":\"\\n        Report external file references that are missing from disk\\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_missing_filesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files_for_cli\",\"description\":\"\\n        Report missing file references by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_missing_files_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries\",\"description\":\"\\n        Return a tree of directly and indirectly linked library files.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_of_linked_librariesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries_for_cli\",\"description\":\"\\n        Return linked-library info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_of_linked_libraries_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info\",\"description\":\"\\n        Simple/fast access to the blend file's path, save status, age, and backups.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_path_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info_for_cli\",\"description\":\"\\n        Return path info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_path_info_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess\",\"description\":\"\\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_usage_guessArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess_for_cli\",\"description\":\"\\n        Guess use-cases by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_usage_guess_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_detail_summary\",\"description\":\"\\n        Return a structured summary of the object identified by *name*.\\n\\n        Includes type, transforms, parent, children, modifiers, constraints,\\n        materials, visibility, data-block name, and collections.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"get_object_detail_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_objects_summary\",\"description\":\"\\n        Return the scene's collection hierarchy and their objects.\\n\\n        Each collection lists its objects (name, type, parent, data name,\\n        selection, visibility) and nested child collections.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_objects_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_python_api_docs\",\"description\":\"\\n        Return the Blender Python API docs for *identifier*, or list\\n        modules matching a trailing-``*`` discovery pattern.\\n\\n        *identifier* should be a fully-qualified Python name (e.g.\\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\\n        The trailing-``*`` forms are supported as discovery entry-points:\\n\\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\\n          ``mathutils``, ``gpu``, ...).\\n        - ``X.*`` enumerates the direct-child identifiers under the\\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\\n\\n        Both return a ``namespace`` response even when ``X.rst`` would\\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\\n        force the child listing.\\n\\n        The response always carries ``kind``, ``found``, and ``identifier``.\\n        The remaining keys depend on ``kind``:\\n\\n        - ``\\\"exact\\\"`` (``found=True``): ``<identifier>.rst`` was read.\\n          Extra keys: ``content`` (RST text), ``examples``. When the\\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\\n          summary of the file's top-level definitions (prefixed by a\\n          header noting the truncation) and ``examples`` is empty -\\n          re-query individual members for their rendered blocks.\\n        - ``\\\"namespace\\\"`` (``found=True``):\\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\\n          Extra key: ``submodules`` (list of child identifiers).\\n        - ``\\\"definition\\\"`` (``found=True``):\\n          *identifier* is defined inside a parent RST\\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\\n          Extra keys: ``content`` (rendered block), ``examples``.\\n        - ``\\\"partial\\\"`` (``found=False``):\\n          the parent RST was located but the trailing component isn't defined in it.\\n          Extra keys:\\n          - ``parent`` the identifier whose RST was loaded.\\n          - ``available`` top-level definitions in that RST.\\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\\n            filtered to those whose last component contains every character of the missing tail.\\n\\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\\n        - ``\\\"suggestions\\\"`` (``found=False``):\\n          no direct match, but *identifier* appears as a component of other files.\\n          Extra key: ``suggestions`` (list of full identifiers).\\n        - ``\\\"missing\\\"`` (``found=False``): nothing matched.\\n\\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\\n        is a list of ``{path, content}`` entries referenced from this documentation.\\n        \",\"parameters\":{\"properties\":{\"identifier\":{\"title\":\"Identifier\",\"type\":\"string\"}},\"required\":[\"identifier\"],\"title\":\"get_python_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_area_as_image\",\"description\":\"\\n        Take a screenshot of a single Blender area and return it as a PNG image.\\n\\n        *area_ui_type* matches the area's ``ui_type``.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"area_ui_type\":{\"enum\":[\"VIEW_3D\",\"IMAGE_EDITOR\",\"UV\",\"ShaderNodeTree\",\"CompositorNodeTree\",\"GeometryNodeTree\",\"TextureNodeTree\",\"SEQUENCE_EDITOR\",\"CLIP_EDITOR\",\"DOPESHEET_EDITOR\",\"GRAPH_EDITOR\",\"NLA_EDITOR\",\"TEXT_EDITOR\",\"CONSOLE\",\"INFO\",\"TOPBAR\",\"STATUSBAR\",\"OUTLINER\",\"PROPERTIES\",\"FILE_BROWSER\",\"SPREADSHEET\",\"PREFERENCES\"],\"title\":\"Area Ui Type\",\"type\":\"string\"},\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"required\":[\"area_ui_type\"],\"title\":\"get_screenshot_of_area_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"description\":\"\\n        Take a screenshot of the entire Blender window and return it as a PNG image.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"title\":\"get_screenshot_of_window_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_json\",\"description\":\"\\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_screenshot_of_window_as_jsonArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_name\",\"description\":\"\\n        Switch the active workspace tab to *name*.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"jump_to_tab_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_space_type\",\"description\":\"\\n        Switch to a workspace whose main area matches *space_type*.\\n\\n        If *allow_edits* is True and no matching workspace exists, a new one\\n        is created by duplicating the current workspace.\\n        \",\"parameters\":{\"properties\":{\"space_type\":{\"title\":\"Space Type\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"space_type\"],\"title\":\"jump_to_tab_by_space_typeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_by_name\",\"description\":\"\\n        Move the 3D viewport to focus on an object by *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_data_by_name\",\"description\":\"\\n        Move the 3D viewport to the object whose data block matches *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_data_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_thumbnail_to_path\",\"description\":\"\\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_thumbnail_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_viewport_to_path\",\"description\":\"\\n        Render the current scene to *output_path* using current render settings.\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_viewport_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_api_docs\",\"description\":\"\\nFull-text search over the bundled Blender Python API reference.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_manual_docs\",\"description\":\"\\nFull-text search over the bundled Blender user manual.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_manual_docsArguments\",\"type\":\"object\"}}}]}", "body_base64": "{"model":"openai/gpt-5.6-luna","messages":[{"role":"system","content":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_EiAaMRToSKz0xJ3cuNin8l6H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]},{"role":"tool","tool_call_id":"call_EiAaMRToSKz0xJ3cuNin8l6H","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 103, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_eaQymwe5S0AnbTBEr8NAI4wi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]},{"role":"tool","tool_call_id":"call_eaQymwe5S0AnbTBEr8NAI4wi","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 9, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\"\n}"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"execute_blender_code","description":"\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ","parameters":{"properties":{"code":{"title":"Code","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code_for_cli","description":"\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"},"code":{"title":"Code","type":"string"}},"required":["blend_file","code"],"title":"execute_blender_code_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks","description":"\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_datablocksArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks_for_cli","description":"\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_datablocks_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files","description":"\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_missing_filesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files_for_cli","description":"\n        Report missing file references by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_missing_files_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries","description":"\n        Return a tree of directly and indirectly linked library files.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_of_linked_librariesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries_for_cli","description":"\n        Return linked-library info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_of_linked_libraries_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info","description":"\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_path_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info_for_cli","description":"\n        Return path info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_path_info_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess","description":"\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_usage_guessArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess_for_cli","description":"\n        Guess use-cases by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_usage_guess_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_detail_summary","description":"\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ","parameters":{"properties":{"name":{"title":"Name","type":"string"}},"required":["name"],"title":"get_object_detail_summaryArguments","type":"object"}}},{"type":"function","function":{"name":"get_objects_summary","description":"\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ","parameters":{"properties":{},"title":"get_objects_summaryArguments","type":"object"}}},{"type":"function","function":{"name":"get_python_api_docs","description":"\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ","parameters":{"properties":{"identifier":{"title":"Identifier","type":"string"}},"required":["identifier"],"title":"get_python_api_docsArguments","type":"object"}}},{"type":"function","function":{"name":"get_screenshot_of_area_as_image","description":"\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ","parameters":{"properties":{"area_ui_type":{"enum":["VIEW_3D","IMAGE_EDITOR","UV","ShaderNodeTree","CompositorNodeTree","GeometryNodeTree","TextureNodeTree","SEQUENCE_EDITOR","CLIP_EDITOR","DOPESHEET_EDITOR","GRAPH_EDITOR","NLA_EDITOR","TEXT_EDITOR","CONSOLE","INFO","TOPBAR","STATUSBAR","OUTLINER","PROPERTIES","FILE_BROWSER","SPREADSHEET","PREFERENCES"],"title":"Area Ui Type","type":"string"},"size_limit_in_bytes":{"default":0,"title":"Size Limit In Bytes","type":"integer"}},"required":["area_ui_type"],"title":"get_screenshot_of_area_as_imageArguments","type":"object"}}},{"type":"function","function":{"name":"get_screenshot_of_window_as_image","description":"\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ","parameters":{"properties":{"size_limit_in_bytes":{"default":0,"title":"Size Limit In Bytes","type":"integer"}},"title":"get_screenshot_of_window_as_imageArguments","type":"object"}}},{"type":"function","function":{"name":"get_screenshot_of_window_as_json","description":"\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ","parameters":{"properties":{},"title":"get_screenshot_of_window_as_jsonArguments","type":"object"}}},{"type":"function","function":{"name":"jump_to_tab_by_name","description":"\n  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{"ts": "2026-07-16T17:35:24.568526+00:00", "type": "tool_call", "id": "call_vJspENn0bQYVZ8T1VfGsLASy", "name": "execute_blender_code", "arguments": {"code": "import bpy\nsc=bpy.context.scene; sc.render.engine='BLENDER_EEVEE'; sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.image_settings.file_format='PNG';sc.render.filepath='//classical_bust.png';sc.world.color=(.035,.03,.025)\ntry: sc.view_settings.look='AgX - Medium High Contrast'\nexcept: pass\nbpy.ops.wm.save_as_mainfile(filepath='//classical_bust.blend'); bpy.ops.render.render(write_still=True)\nresult={'objects':len(bpy.context.scene.objects),'file':bpy.data.filepath}"}, "raw_arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene; sc.render.engine='BLENDER_EEVEE'; sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.image_settings.file_format='PNG';sc.render.filepath='//classical_bust.png';sc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_bust.blend'); bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':bpy.data.filepath}\"}"}
{"ts": "2026-07-16T17:35:25.594838+00:00", "type": "tool_result", "tool_call_id": "call_vJspENn0bQYVZ8T1VfGsLASy", "name": "execute_blender_code", "text": "{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 85,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_bust.blend\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_bust.blend\\\"\\n\",\n  \"stderr\": \"<string>:5: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-16T17:35:28.966961+00:00", "type": "llm_call", "call_index": 4, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-16T17:35:25.594838+00:00", "finished_at": "2026-07-16T17:35:28.966961+00:00", "duration_s": 3.375, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 103, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_eaQymwe5S0AnbTBEr8NAI4wi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_eaQymwe5S0AnbTBEr8NAI4wi", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 9, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_vJspENn0bQYVZ8T1VfGsLASy", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene; sc.render.engine='BLENDER_EEVEE'; sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.image_settings.file_format='PNG';sc.render.filepath='//classical_bust.png';sc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_bust.blend'); bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':bpy.data.filepath}\"}"}}]}, {"role": "tool", "tool_call_id": "call_vJspENn0bQYVZ8T1VfGsLASy", "content": "{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 85,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_bust.blend\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_bust.blend\\\"\\n\",\n  \"stderr\": \"<string>:5: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}"}], "tools": [{"type": "function", "function": {"name": "execute_blender_code", "description": "\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code_for_cli", "description": "\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}, "code": {"title": "Code", "type": "string"}}, "required": ["blend_file", "code"], "title": "execute_blender_code_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks", "description": "\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_datablocksArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks_for_cli", "description": "\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_datablocks_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files", "description": "\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_missing_filesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files_for_cli", "description": "\n        Report missing file references by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_missing_files_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries", "description": "\n        Return a tree of directly and indirectly linked library files.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_of_linked_librariesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries_for_cli", "description": "\n        Return linked-library info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_of_linked_libraries_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info", "description": "\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_path_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info_for_cli", "description": "\n        Return path info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_path_info_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess", "description": "\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_usage_guessArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess_for_cli", "description": "\n        Guess use-cases by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_usage_guess_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_detail_summary", "description": "\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "get_object_detail_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_objects_summary", "description": "\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ", "parameters": {"properties": {}, "title": "get_objects_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_python_api_docs", "description": "\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ", "parameters": {"properties": {"identifier": {"title": "Identifier", "type": "string"}}, "required": ["identifier"], "title": "get_python_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_area_as_image", "description": "\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"area_ui_type": {"enum": ["VIEW_3D", "IMAGE_EDITOR", "UV", "ShaderNodeTree", "CompositorNodeTree", "GeometryNodeTree", "TextureNodeTree", "SEQUENCE_EDITOR", "CLIP_EDITOR", "DOPESHEET_EDITOR", "GRAPH_EDITOR", "NLA_EDITOR", "TEXT_EDITOR", "CONSOLE", "INFO", "TOPBAR", "STATUSBAR", "OUTLINER", "PROPERTIES", "FILE_BROWSER", "SPREADSHEET", "PREFERENCES"], "title": "Area Ui Type", "type": "string"}, "size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "required": ["area_ui_type"], "title": "get_screenshot_of_area_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_image", "description": "\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "title": "get_screenshot_of_window_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_json", "description": "\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ", "parameters": {"properties": {}, "title": "get_screenshot_of_window_as_jsonArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_name", "description": "\n        Switch the active workspace tab to *name*.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "jump_to_tab_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_space_type", "description": "\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ", "parameters": {"properties": {"space_type": {"title": "Space Type", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["space_type"], "title": "jump_to_tab_by_space_typeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_by_name", "description": "\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_data_by_name", "description": "\n        Move the 3D viewport to the object whose data block matches *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_data_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_thumbnail_to_path", "description": "\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_thumbnail_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_viewport_to_path", "description": "\n        Render the current scene to *output_path* using current render settings.\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_viewport_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_api_docs", "description": "\nFull-text search over the bundled Blender Python API reference.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_manual_docs", "description": "\nFull-text search over the bundled Blender user manual.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_manual_docsArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-16T17:35:25.594838+00:00", "finished_at": "2026-07-16T17:35:28.966961+00:00", "duration_s": 3.375, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "32444", "content-type": "application/json"}, "body": "{\"model\":\"openai/gpt-5.6-luna\",\"messages\":[{\"role\":\"system\",\"content\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\\n\\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls.\"},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\\n\\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\\n\\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\\n\\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\\n\\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\\n\\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_EiAaMRToSKz0xJ3cuNin8l6H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy, math\\\\nfrom mathutils import Vector\\\\n# clear\\\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\\\n    return m\\\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\\\n# helpers\\\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\\\ndef cube(name,loc,scale,material,bev=0):\\\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\\\n    return o\\\\ndef cyl(name,loc,r,depth,material,verts=64):\\\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\\\ndef curve_tube(name, pts, bevel, material):\\\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\\\n# pedestal\\\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\\\n# neck and bust\\\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\\\n# shoulders/broken bust block\\\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\\\n# head cranium and jaw\\\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\\\n# ears\\\\nfor x in (-.69,.69):\\\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\\\n# cheekbones prominent\\\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\\\n# brow ridge\\\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\\\n# eyes, lids, iris raised shallow\\\\nfor x in (-.27,.27):\\\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\\\n# nose bridge and tip custom-ish overlapping smooth forms\\\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\\\n# philtrum\\\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\\\n# lips strong classical\\\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\\\n# chin plane\\\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\\\n# hair cap\\\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\\\n# hairline curls across forehead and temples, actual tubes\\\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\\\n    z=3.55-0.06*(abs(x)/.6)\\\\n    # curl descending front, slight spiral-ish polyline\\\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\\\n# side locks and crown rows\\\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\\\n        # clumped wave arcs\\\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\\\n        curve_tube('Crown wave',pts,.085,plaster)\\\\n# side curls near ears\\\\nfor side in (-1,1):\\\\n for j in range(4):\\\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\\\n# subtle casting seam collar ring\\\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\\\n# ground/backdrop large plane\\\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\\\n# backdrop vertical plane behind y=2\\\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(0,0,2.75))\\\\n# lights\\\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\\\n# world/render\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\\\n# organize collections? save\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_EiAaMRToSKz0xJ3cuNin8l6H\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 103, in <module>\\\\nTypeError: bpy_struct: item.attr = val: enum \\\\\\\"BLENDER_EEVEE_NEXT\\\\\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_eaQymwe5S0AnbTBEr8NAI4wi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene\\\\nsc.render.engine='BLENDER_EEVEE'\\\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\\\nsc.world.color=(.035,.03,.025)\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_eaQymwe5S0AnbTBEr8NAI4wi\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 9, in <module>\\\\nRuntimeError: Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_bust.blend@ for writing: No such file or directory\\\\n\\\\n\\\",\\n  \\\"stdout\\\": \\\"Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_bust.blend@ for writing: No such file or directory\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_vJspENn0bQYVZ8T1VfGsLASy\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene; sc.render.engine='BLENDER_EEVEE'; sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.image_settings.file_format='PNG';sc.render.filepath='//classical_bust.png';sc.world.color=(.035,.03,.025)\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_bust.blend'); bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':bpy.data.filepath}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_vJspENn0bQYVZ8T1VfGsLASy\",\"content\":\"{\\n  \\\"status\\\": \\\"ok\\\",\\n  \\\"result\\\": {\\n    \\\"objects\\\": 85,\\n    \\\"file\\\": \\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\classical_bust.blend\\\"\\n  },\\n  \\\"stdout\\\": \\\"Info: Saved as \\\\\\\"classical_bust.blend\\\\\\\"\\\\n\\\",\\n  \\\"stderr\\\": \\\"<string>:5: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\\\\\"//\\\\\\\" prefix\\\\n\\\"\\n}\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n        Execute Python code in the connected Blender instance.\\n\\n        The code runs in Blender's Python environment with full access to ``bpy``.\\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\\n        Deferred completion via ``check_is_finished`` is only supported by the\\n        interactive addon server, and is rejected in background mode.\\n        \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code_for_cli\",\"description\":\"\\n        Execute Python code in a background Blender process.\\n\\n        Opens *blend_file* with ``blender --background`` and runs *code*.\\n        Assign a dict to ``result`` to return data.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"},\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"blend_file\",\"code\"],\"title\":\"execute_blender_code_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks\",\"description\":\"\\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_datablocksArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks_for_cli\",\"description\":\"\\n        Return a data-block summary by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_datablocks_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files\",\"description\":\"\\n        Report external file references that are missing from disk\\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_missing_filesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files_for_cli\",\"description\":\"\\n        Report missing file references by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_missing_files_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries\",\"description\":\"\\n        Return a tree of directly and indirectly linked library files.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_of_linked_librariesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries_for_cli\",\"description\":\"\\n        Return linked-library info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_of_linked_libraries_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info\",\"description\":\"\\n        Simple/fast access to the blend file's path, save status, age, and backups.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_path_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info_for_cli\",\"description\":\"\\n        Return path info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_path_info_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess\",\"description\":\"\\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_usage_guessArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess_for_cli\",\"description\":\"\\n        Guess use-cases by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_usage_guess_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_detail_summary\",\"description\":\"\\n        Return a structured summary of the object identified by *name*.\\n\\n        Includes type, transforms, parent, children, modifiers, constraints,\\n        materials, visibility, data-block name, and collections.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"get_object_detail_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_objects_summary\",\"description\":\"\\n        Return the scene's collection hierarchy and their objects.\\n\\n        Each collection lists its objects (name, type, parent, data name,\\n        selection, visibility) and nested child collections.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_objects_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_python_api_docs\",\"description\":\"\\n        Return the Blender Python API docs for *identifier*, or list\\n        modules matching a trailing-``*`` discovery pattern.\\n\\n        *identifier* should be a fully-qualified Python name (e.g.\\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\\n        The trailing-``*`` forms are supported as discovery entry-points:\\n\\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\\n          ``mathutils``, ``gpu``, ...).\\n        - ``X.*`` enumerates the direct-child identifiers under the\\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\\n\\n        Both return a ``namespace`` response even when ``X.rst`` would\\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\\n        force the child listing.\\n\\n        The response always carries ``kind``, ``found``, and ``identifier``.\\n        The remaining keys depend on ``kind``:\\n\\n        - ``\\\"exact\\\"`` (``found=True``): ``<identifier>.rst`` was read.\\n          Extra keys: ``content`` (RST text), ``examples``. When the\\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\\n          summary of the file's top-level definitions (prefixed by a\\n          header noting the truncation) and ``examples`` is empty -\\n          re-query individual members for their rendered blocks.\\n        - ``\\\"namespace\\\"`` (``found=True``):\\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\\n          Extra key: ``submodules`` (list of child identifiers).\\n        - ``\\\"definition\\\"`` (``found=True``):\\n          *identifier* is defined inside a parent RST\\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\\n          Extra keys: ``content`` (rendered block), ``examples``.\\n        - ``\\\"partial\\\"`` (``found=False``):\\n          the parent RST was located but the trailing component isn't defined in it.\\n          Extra keys:\\n          - ``parent`` the identifier whose RST was loaded.\\n          - ``available`` top-level definitions in that RST.\\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\\n            filtered to those whose last component contains every character of the missing tail.\\n\\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\\n        - ``\\\"suggestions\\\"`` (``found=False``):\\n          no direct match, but *identifier* appears as a component of other files.\\n          Extra key: ``suggestions`` (list of full identifiers).\\n        - ``\\\"missing\\\"`` (``found=False``): nothing matched.\\n\\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\\n        is a list of ``{path, content}`` entries referenced from this documentation.\\n        \",\"parameters\":{\"properties\":{\"identifier\":{\"title\":\"Identifier\",\"type\":\"string\"}},\"required\":[\"identifier\"],\"title\":\"get_python_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_area_as_image\",\"description\":\"\\n        Take a screenshot of a single Blender area and return it as a PNG image.\\n\\n        *area_ui_type* matches the area's ``ui_type``.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"area_ui_type\":{\"enum\":[\"VIEW_3D\",\"IMAGE_EDITOR\",\"UV\",\"ShaderNodeTree\",\"CompositorNodeTree\",\"GeometryNodeTree\",\"TextureNodeTree\",\"SEQUENCE_EDITOR\",\"CLIP_EDITOR\",\"DOPESHEET_EDITOR\",\"GRAPH_EDITOR\",\"NLA_EDITOR\",\"TEXT_EDITOR\",\"CONSOLE\",\"INFO\",\"TOPBAR\",\"STATUSBAR\",\"OUTLINER\",\"PROPERTIES\",\"FILE_BROWSER\",\"SPREADSHEET\",\"PREFERENCES\"],\"title\":\"Area Ui Type\",\"type\":\"string\"},\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"required\":[\"area_ui_type\"],\"title\":\"get_screenshot_of_area_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"description\":\"\\n        Take a screenshot of the entire Blender window and return it as a PNG image.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"title\":\"get_screenshot_of_window_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_json\",\"description\":\"\\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_screenshot_of_window_as_jsonArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_name\",\"description\":\"\\n        Switch the active workspace tab to *name*.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"jump_to_tab_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_space_type\",\"description\":\"\\n        Switch to a workspace whose main area matches *space_type*.\\n\\n        If *allow_edits* is True and no matching workspace exists, a new one\\n        is created by duplicating the current workspace.\\n        \",\"parameters\":{\"properties\":{\"space_type\":{\"title\":\"Space Type\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"space_type\"],\"title\":\"jump_to_tab_by_space_typeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_by_name\",\"description\":\"\\n        Move the 3D viewport to focus on an object by *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_data_by_name\",\"description\":\"\\n        Move the 3D viewport to the object whose data block matches *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_data_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_thumbnail_to_path\",\"description\":\"\\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_thumbnail_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_viewport_to_path\",\"description\":\"\\n        Render the current scene to *output_path* using current render settings.\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_viewport_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_api_docs\",\"description\":\"\\nFull-text search over the bundled Blender Python API reference.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_manual_docs\",\"description\":\"\\nFull-text search over the bundled Blender user manual.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_manual_docsArguments\",\"type\":\"object\"}}}]}", "body_base64": "{"model":"openai/gpt-5.6-luna","messages":[{"role":"system","content":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_EiAaMRToSKz0xJ3cuNin8l6H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]},{"role":"tool","tool_call_id":"call_EiAaMRToSKz0xJ3cuNin8l6H","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 103, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_eaQymwe5S0AnbTBEr8NAI4wi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]},{"role":"tool","tool_call_id":"call_eaQymwe5S0AnbTBEr8NAI4wi","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 9, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_vJspENn0bQYVZ8T1VfGsLASy","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene; sc.render.engine='BLENDER_EEVEE'; sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.image_settings.file_format='PNG';sc.render.filepath='//classical_bust.png';sc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_bust.blend'); bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':bpy.data.filepath}\"}"}}]},{"role":"tool","tool_call_id":"call_vJspENn0bQYVZ8T1VfGsLASy","content":"{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 85,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_bust.blend\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_bust.blend\\\"\\n\",\n  \"stderr\": \"<string>:5: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"execute_blender_code","description":"\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ","parameters":{"properties":{"code":{"title":"Code","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code_for_cli","description":"\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"},"code":{"title":"Code","type":"string"}},"required":["blend_file","code"],"title":"execute_blender_code_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks","description":"\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_datablocksArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks_for_cli","description":"\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_datablocks_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files","description":"\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_missing_filesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files_for_cli","description":"\n        Report missing file references by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_missing_files_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries","description":"\n        Return a tree of directly and indirectly linked library files.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_of_linked_librariesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries_for_cli","description":"\n        Return linked-library info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_of_linked_libraries_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info","description":"\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_path_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info_for_cli","description":"\n        Return path info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_path_info_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess","description":"\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_usage_guessArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess_for_cli","description":"\n        Guess use-cases by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_usage_guess_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_detail_summary","description":"\n        Return a structured summary of the object identified by 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"}
{"ts": "2026-07-16T17:35:29.232364+00:00", "type": "image", "tool": "get_screenshot_of_window_as_image"}
{"ts": "2026-07-16T17:35:36.662372+00:00", "type": "llm_call", "call_index": 5, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-16T17:35:29.233371+00:00", "finished_at": "2026-07-16T17:35:36.662372+00:00", "duration_s": 7.422000000005937, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 103, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_eaQymwe5S0AnbTBEr8NAI4wi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_eaQymwe5S0AnbTBEr8NAI4wi", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 9, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_vJspENn0bQYVZ8T1VfGsLASy", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene; sc.render.engine='BLENDER_EEVEE'; sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.image_settings.file_format='PNG';sc.render.filepath='//classical_bust.png';sc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_bust.blend'); bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':bpy.data.filepath}\"}"}}]}, {"role": "tool", "tool_call_id": "call_vJspENn0bQYVZ8T1VfGsLASy", "content": "{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 85,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_bust.blend\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_bust.blend\\\"\\n\",\n  \"stderr\": \"<string>:5: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_OyFy3Y7eQ3kOttgsLm2zh734", "type": "function", "function": {"name": "get_screenshot_of_window_as_image", "arguments": "{\"size_limit_in_bytes\":200000}"}}]}, {"role": "tool", "tool_call_id": "call_OyFy3Y7eQ3kOttgsLm2zh734", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}], "tools": [{"type": "function", "function": {"name": "execute_blender_code", "description": "\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code_for_cli", "description": "\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}, "code": {"title": "Code", "type": "string"}}, "required": ["blend_file", "code"], "title": "execute_blender_code_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks", "description": "\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_datablocksArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks_for_cli", "description": "\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_datablocks_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files", "description": "\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_missing_filesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files_for_cli", "description": "\n        Report missing file references by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_missing_files_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries", "description": "\n        Return a tree of directly and indirectly linked library files.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_of_linked_librariesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries_for_cli", "description": "\n        Return linked-library info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_of_linked_libraries_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info", "description": "\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_path_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info_for_cli", "description": "\n        Return path info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_path_info_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess", "description": "\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_usage_guessArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess_for_cli", "description": "\n        Guess use-cases by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_usage_guess_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_detail_summary", "description": "\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "get_object_detail_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_objects_summary", "description": "\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ", "parameters": {"properties": {}, "title": "get_objects_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_python_api_docs", "description": "\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ", "parameters": {"properties": {"identifier": {"title": "Identifier", "type": "string"}}, "required": ["identifier"], "title": "get_python_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_area_as_image", "description": "\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"area_ui_type": {"enum": ["VIEW_3D", "IMAGE_EDITOR", "UV", "ShaderNodeTree", "CompositorNodeTree", "GeometryNodeTree", "TextureNodeTree", "SEQUENCE_EDITOR", "CLIP_EDITOR", "DOPESHEET_EDITOR", "GRAPH_EDITOR", "NLA_EDITOR", "TEXT_EDITOR", "CONSOLE", "INFO", "TOPBAR", "STATUSBAR", "OUTLINER", "PROPERTIES", "FILE_BROWSER", "SPREADSHEET", "PREFERENCES"], "title": "Area Ui Type", "type": "string"}, "size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "required": ["area_ui_type"], "title": "get_screenshot_of_area_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_image", "description": "\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "title": "get_screenshot_of_window_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_json", "description": "\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ", "parameters": {"properties": {}, "title": "get_screenshot_of_window_as_jsonArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_name", "description": "\n        Switch the active workspace tab to *name*.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "jump_to_tab_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_space_type", "description": "\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ", "parameters": {"properties": {"space_type": {"title": "Space Type", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["space_type"], "title": "jump_to_tab_by_space_typeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_by_name", "description": "\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_data_by_name", "description": "\n        Move the 3D viewport to the object whose data block matches *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_data_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_thumbnail_to_path", "description": "\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_thumbnail_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_viewport_to_path", "description": "\n        Render the current scene to *output_path* using current render settings.\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_viewport_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_api_docs", "description": "\nFull-text search over the bundled Blender Python API reference.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_manual_docs", "description": "\nFull-text search over the bundled Blender user manual.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_manual_docsArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-16T17:35:29.233371+00:00", "finished_at": "2026-07-16T17:35:36.661372+00:00", "duration_s": 7.422000000005937, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "203785", "content-type": "application/json"}, "body": "{\"model\":\"openai/gpt-5.6-luna\",\"messages\":[{\"role\":\"system\",\"content\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\\n\\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls.\"},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\\n\\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\\n\\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\\n\\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\\n\\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\\n\\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_EiAaMRToSKz0xJ3cuNin8l6H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy, math\\\\nfrom mathutils import Vector\\\\n# clear\\\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\\\n    return m\\\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\\\n# helpers\\\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\\\ndef cube(name,loc,scale,material,bev=0):\\\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\\\n    return o\\\\ndef cyl(name,loc,r,depth,material,verts=64):\\\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\\\ndef curve_tube(name, pts, bevel, material):\\\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\\\n# pedestal\\\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\\\n# neck and bust\\\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\\\n# shoulders/broken bust block\\\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\\\n# head cranium and jaw\\\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\\\n# ears\\\\nfor x in (-.69,.69):\\\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\\\n# cheekbones prominent\\\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\\\n# brow ridge\\\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\\\n# eyes, lids, iris raised shallow\\\\nfor x in (-.27,.27):\\\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\\\n# nose bridge and tip custom-ish overlapping smooth forms\\\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\\\n# philtrum\\\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\\\n# lips strong classical\\\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\\\n# chin plane\\\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\\\n# hair cap\\\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\\\n# hairline curls across forehead and temples, actual tubes\\\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\\\n    z=3.55-0.06*(abs(x)/.6)\\\\n    # curl descending front, slight spiral-ish polyline\\\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\\\n# side locks and crown rows\\\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\\\n        # clumped wave arcs\\\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\\\n        curve_tube('Crown wave',pts,.085,plaster)\\\\n# side curls near ears\\\\nfor side in (-1,1):\\\\n for j in range(4):\\\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\\\n# subtle casting seam collar ring\\\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\\\n# ground/backdrop large plane\\\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\\\n# backdrop vertical plane behind y=2\\\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(0,0,2.75))\\\\n# lights\\\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\\\n# world/render\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\\\n# organize collections? save\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_EiAaMRToSKz0xJ3cuNin8l6H\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 103, in <module>\\\\nTypeError: bpy_struct: item.attr = val: enum \\\\\\\"BLENDER_EEVEE_NEXT\\\\\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_eaQymwe5S0AnbTBEr8NAI4wi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene\\\\nsc.render.engine='BLENDER_EEVEE'\\\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\\\nsc.world.color=(.035,.03,.025)\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_eaQymwe5S0AnbTBEr8NAI4wi\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 9, in <module>\\\\nRuntimeError: Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_bust.blend@ for writing: No such file or directory\\\\n\\\\n\\\",\\n  \\\"stdout\\\": \\\"Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_bust.blend@ for writing: No such file or directory\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_vJspENn0bQYVZ8T1VfGsLASy\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene; sc.render.engine='BLENDER_EEVEE'; sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.image_settings.file_format='PNG';sc.render.filepath='//classical_bust.png';sc.world.color=(.035,.03,.025)\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_bust.blend'); bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':bpy.data.filepath}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_vJspENn0bQYVZ8T1VfGsLASy\",\"content\":\"{\\n  \\\"status\\\": \\\"ok\\\",\\n  \\\"result\\\": {\\n    \\\"objects\\\": 85,\\n    \\\"file\\\": \\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\classical_bust.blend\\\"\\n  },\\n  \\\"stdout\\\": \\\"Info: Saved as \\\\\\\"classical_bust.blend\\\\\\\"\\\\n\\\",\\n  \\\"stderr\\\": \\\"<string>:5: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\\\\\"//\\\\\\\" prefix\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_OyFy3Y7eQ3kOttgsLm2zh734\",\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"arguments\":\"{\\\"size_limit_in_bytes\\\":200000}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_OyFy3Y7eQ3kOttgsLm2zh734\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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       Execute Python code in the connected Blender instance.\\n\\n        The code runs in Blender's Python environment with full access to ``bpy``.\\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\\n        Deferred completion via ``check_is_finished`` is only supported by the\\n        interactive addon server, and is rejected in background mode.\\n        \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code_for_cli\",\"description\":\"\\n        Execute Python code in a background Blender process.\\n\\n        Opens *blend_file* with ``blender --background`` and runs *code*.\\n        Assign a dict to ``result`` to return data.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"},\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"blend_file\",\"code\"],\"title\":\"execute_blender_code_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks\",\"description\":\"\\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_datablocksArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks_for_cli\",\"description\":\"\\n        Return a data-block summary by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_datablocks_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files\",\"description\":\"\\n        Report external file references that are missing from disk\\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_missing_filesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files_for_cli\",\"description\":\"\\n        Report missing file references by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_missing_files_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries\",\"description\":\"\\n        Return a tree of directly and indirectly linked library files.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_of_linked_librariesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries_for_cli\",\"description\":\"\\n        Return linked-library info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_of_linked_libraries_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info\",\"description\":\"\\n        Simple/fast access to the blend file's path, save status, age, and backups.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_path_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info_for_cli\",\"description\":\"\\n        Return path info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_path_info_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess\",\"description\":\"\\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_usage_guessArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess_for_cli\",\"description\":\"\\n        Guess use-cases by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_usage_guess_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_detail_summary\",\"description\":\"\\n        Return a structured summary of the object identified by *name*.\\n\\n        Includes type, transforms, parent, children, modifiers, constraints,\\n        materials, visibility, data-block name, and collections.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"get_object_detail_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_objects_summary\",\"description\":\"\\n        Return the scene's collection hierarchy and their objects.\\n\\n        Each collection lists its objects (name, type, parent, data name,\\n        selection, visibility) and nested child collections.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_objects_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_python_api_docs\",\"description\":\"\\n        Return the Blender Python API docs for *identifier*, or list\\n        modules matching a trailing-``*`` discovery pattern.\\n\\n        *identifier* should be a fully-qualified Python name (e.g.\\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\\n        The trailing-``*`` forms are supported as discovery entry-points:\\n\\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\\n          ``mathutils``, ``gpu``, ...).\\n        - ``X.*`` enumerates the direct-child identifiers under the\\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\\n\\n        Both return a ``namespace`` response even when ``X.rst`` would\\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\\n        force the child listing.\\n\\n        The response always carries ``kind``, ``found``, and ``identifier``.\\n        The remaining keys depend on ``kind``:\\n\\n        - ``\\\"exact\\\"`` (``found=True``): ``<identifier>.rst`` was read.\\n          Extra keys: ``content`` (RST text), ``examples``. When the\\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\\n          summary of the file's top-level definitions (prefixed by a\\n          header noting the truncation) and ``examples`` is empty -\\n          re-query individual members for their rendered blocks.\\n        - ``\\\"namespace\\\"`` (``found=True``):\\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\\n          Extra key: ``submodules`` (list of child identifiers).\\n        - ``\\\"definition\\\"`` (``found=True``):\\n          *identifier* is defined inside a parent RST\\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\\n          Extra keys: ``content`` (rendered block), ``examples``.\\n        - ``\\\"partial\\\"`` (``found=False``):\\n          the parent RST was located but the trailing component isn't defined in it.\\n          Extra keys:\\n          - ``parent`` the identifier whose RST was loaded.\\n          - ``available`` top-level definitions in that RST.\\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\\n            filtered to those whose last component contains every character of the missing tail.\\n\\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\\n        - ``\\\"suggestions\\\"`` (``found=False``):\\n          no direct match, but *identifier* appears as a component of other files.\\n          Extra key: ``suggestions`` (list of full identifiers).\\n        - ``\\\"missing\\\"`` (``found=False``): nothing matched.\\n\\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\\n        is a list of ``{path, content}`` entries referenced from this documentation.\\n        \",\"parameters\":{\"properties\":{\"identifier\":{\"title\":\"Identifier\",\"type\":\"string\"}},\"required\":[\"identifier\"],\"title\":\"get_python_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_area_as_image\",\"description\":\"\\n        Take a screenshot of a single Blender area and return it as a PNG image.\\n\\n        *area_ui_type* matches the area's ``ui_type``.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"area_ui_type\":{\"enum\":[\"VIEW_3D\",\"IMAGE_EDITOR\",\"UV\",\"ShaderNodeTree\",\"CompositorNodeTree\",\"GeometryNodeTree\",\"TextureNodeTree\",\"SEQUENCE_EDITOR\",\"CLIP_EDITOR\",\"DOPESHEET_EDITOR\",\"GRAPH_EDITOR\",\"NLA_EDITOR\",\"TEXT_EDITOR\",\"CONSOLE\",\"INFO\",\"TOPBAR\",\"STATUSBAR\",\"OUTLINER\",\"PROPERTIES\",\"FILE_BROWSER\",\"SPREADSHEET\",\"PREFERENCES\"],\"title\":\"Area Ui Type\",\"type\":\"string\"},\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"required\":[\"area_ui_type\"],\"title\":\"get_screenshot_of_area_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"description\":\"\\n        Take a screenshot of the entire Blender window and return it as a PNG image.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"title\":\"get_screenshot_of_window_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_json\",\"description\":\"\\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_screenshot_of_window_as_jsonArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_name\",\"description\":\"\\n        Switch the active workspace tab to *name*.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"jump_to_tab_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_space_type\",\"description\":\"\\n        Switch to a workspace whose main area matches *space_type*.\\n\\n        If *allow_edits* is True and no matching workspace exists, a new one\\n        is created by duplicating the current workspace.\\n        \",\"parameters\":{\"properties\":{\"space_type\":{\"title\":\"Space Type\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"space_type\"],\"title\":\"jump_to_tab_by_space_typeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_by_name\",\"description\":\"\\n        Move the 3D viewport to focus on an object by *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_data_by_name\",\"description\":\"\\n        Move the 3D viewport to the object whose data block matches *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_data_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_thumbnail_to_path\",\"description\":\"\\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_thumbnail_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_viewport_to_path\",\"description\":\"\\n        Render the current scene to *output_path* using current render settings.\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_viewport_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_api_docs\",\"description\":\"\\nFull-text search over the bundled Blender Python API reference.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_manual_docs\",\"description\":\"\\nFull-text search over the bundled Blender user manual.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_manual_docsArguments\",\"type\":\"object\"}}}]}", "body_base64": "{"model":"openai/gpt-5.6-luna","messages":[{"role":"system","content":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_EiAaMRToSKz0xJ3cuNin8l6H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]},{"role":"tool","tool_call_id":"call_EiAaMRToSKz0xJ3cuNin8l6H","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 103, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_eaQymwe5S0AnbTBEr8NAI4wi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]},{"role":"tool","tool_call_id":"call_eaQymwe5S0AnbTBEr8NAI4wi","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 9, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_vJspENn0bQYVZ8T1VfGsLASy","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene; sc.render.engine='BLENDER_EEVEE'; sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.image_settings.file_format='PNG';sc.render.filepath='//classical_bust.png';sc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_bust.blend'); bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':bpy.data.filepath}\"}"}}]},{"role":"tool","tool_call_id":"call_vJspENn0bQYVZ8T1VfGsLASy","content":"{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 85,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_bust.blend\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_bust.blend\\\"\\n\",\n  \"stderr\": \"<string>:5: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_OyFy3Y7eQ3kOttgsLm2zh734","type":"function","function":{"name":"get_screenshot_of_window_as_image","arguments":"{\"size_limit_in_bytes\":200000}"}}]},{"role":"tool","tool_call_id":"call_OyFy3Y7eQ3kOttgsLm2zh734","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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       Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ","parameters":{"properties":{"code":{"title":"Code","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code_for_cli","description":"\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"},"code":{"title":"Code","type":"string"}},"required":["blend_file","code"],"title":"execute_blender_code_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks","description":"\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_datablocksArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks_for_cli","description":"\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_datablocks_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files","description":"\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_missing_filesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files_for_cli","description":"\n        Report missing file references by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_missing_files_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries","description":"\n        Return a tree of directly and indirectly linked library files.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_of_linked_librariesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries_for_cli","description":"\n        Return linked-library info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_of_linked_libraries_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info","description":"\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_path_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info_for_cli","description":"\n        Return path info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_path_info_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess","description":"\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_usage_guessArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess_for_cli","description":"\n        Guess use-cases by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_usage_guess_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_detail_summary","description":"\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ","parameters":{"properties":{"name":{"title":"Name","type":"string"}},"required":["name"],"title":"get_object_detail_summaryArguments","type":"object"}}},{"type":"function","function":{"name":"get_objects_summary","description":"\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ","parameters":{"properties":{},"title":"get_objects_summaryArguments","type":"object"}}},{"type":"function","function":{"name":"get_python_api_docs","description":"\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ","parameters":{"properties":{"identifier":{"title":"Identifier","type":"string"}},"required":["identifier"],"title":"get_python_api_docsArguments","type":"object"}}},{"type":"function","function":{"name":"get_screenshot_of_area_as_image","description":"\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ","parameters":{"properties":{"area_ui_type":{"enum":["VIEW_3D","IMAGE_EDITOR","UV","ShaderNodeTree","CompositorNodeTree","GeometryNodeTree","TextureNodeTree","SEQUENCE_EDITOR","CLIP_EDITOR","DOPESHEET_EDITOR","GRAPH_EDITOR","NLA_EDITOR","TEXT_EDITOR","CONSOLE","INFO","TOPBAR","STATUSBAR","OUTLINER","PROPERTIES","FILE_BROWSER","SPREADSHEET","PREFERENCES"],"title":"Area Ui Type","type":"string"},"size_limit_in_bytes":{"default":0,"title":"Size Limit In Bytes","type":"integer"}},"required":["area_ui_type"],"title":"get_screenshot_of_area_as_imageArguments","type":"object"}}},{"type":"function","function":{"name":"get_screenshot_of_window_as_image","description":"\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ","parameters":{"properties":{"size_limit_in_bytes":{"default":0,"title":"Size Limit In Bytes","type":"integer"}},"title":"get_screenshot_of_window_as_imageArguments","type":"object"}}},{"type":"function","function":{"name":"get_screenshot_of_window_as_json","description":"\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ","parameters":{"properties":{},"title":"get_screenshot_of_window_as_jsonArguments","type":"object"}}},{"type":"function","function":{"name":"jump_to_tab_by_name","description":"\n        Switch the active workspace tab to *name*.\n        ","parameters":{"properties":{"name":{"title":"Name","type":"string"}},"required":["name"],"title":"jump_to_tab_by_nameArguments","type":"object"}}},{"type":"function","function":{"name":"jump_to_tab_by_space_type","description":"\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ","parameters":{"properties":{"space_type":{"title":"Space Type","type":"string"},"allow_edits":{"default":false,"title":"Allow Edits","type":"boolean"}},"required":["space_type"],"title":"jump_to_tab_by_space_typeArguments","type":"object"}}},{"type":"function","function":{"name":"jump_to_view3d_object_by_name","description":"\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled 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{"ts": "2026-07-16T17:35:36.666886+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "call_DJNNcXg3I6yb0V1lUyYTTG0p", "name": "execute_blender_code", "arguments": {"code": "import bpy\nsc=bpy.context.scene\nsc.render.engine='CYCLES'\nsc.cycles.samples=64\nsc.cycles.use_denoising=True\n# keep final benchmark resolution settings modest overridden by benchmark\nsc.render.resolution_x=1536;sc.render.resolution_y=1536;sc.render.resolution_percentage=100\n# make camera active and ensure studio settings\nsc.camera=bpy.data.objects.get('Presentation Camera')\n# save final\nbpy.ops.wm.save_as_mainfile(filepath=bpy.data.filepath)\nresult={'engine':sc.render.engine,'camera':sc.camera.name,'path':bpy.data.filepath}"}, "raw_arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='CYCLES'\\nsc.cycles.samples=64\\nsc.cycles.use_denoising=True\\n# keep final benchmark resolution settings modest overridden by benchmark\\nsc.render.resolution_x=1536;sc.render.resolution_y=1536;sc.render.resolution_percentage=100\\n# make camera active and ensure studio settings\\nsc.camera=bpy.data.objects.get('Presentation Camera')\\n# save final\\nbpy.ops.wm.save_as_mainfile(filepath=bpy.data.filepath)\\nresult={'engine':sc.render.engine,'camera':sc.camera.name,'path':bpy.data.filepath}\"}", "parse_error": null}]}
{"ts": "2026-07-16T17:35:36.666886+00:00", "type": "tool_call", "id": "call_DJNNcXg3I6yb0V1lUyYTTG0p", "name": "execute_blender_code", "arguments": {"code": "import bpy\nsc=bpy.context.scene\nsc.render.engine='CYCLES'\nsc.cycles.samples=64\nsc.cycles.use_denoising=True\n# keep final benchmark resolution settings modest overridden by benchmark\nsc.render.resolution_x=1536;sc.render.resolution_y=1536;sc.render.resolution_percentage=100\n# make camera active and ensure studio settings\nsc.camera=bpy.data.objects.get('Presentation Camera')\n# save final\nbpy.ops.wm.save_as_mainfile(filepath=bpy.data.filepath)\nresult={'engine':sc.render.engine,'camera':sc.camera.name,'path':bpy.data.filepath}"}, "raw_arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='CYCLES'\\nsc.cycles.samples=64\\nsc.cycles.use_denoising=True\\n# keep final benchmark resolution settings modest overridden by benchmark\\nsc.render.resolution_x=1536;sc.render.resolution_y=1536;sc.render.resolution_percentage=100\\n# make camera active and ensure studio settings\\nsc.camera=bpy.data.objects.get('Presentation Camera')\\n# save final\\nbpy.ops.wm.save_as_mainfile(filepath=bpy.data.filepath)\\nresult={'engine':sc.render.engine,'camera':sc.camera.name,'path':bpy.data.filepath}\"}"}
{"ts": "2026-07-16T17:35:37.675019+00:00", "type": "tool_result", "tool_call_id": "call_DJNNcXg3I6yb0V1lUyYTTG0p", "name": "execute_blender_code", "text": "{\n  \"status\": \"ok\",\n  \"result\": {\n    \"engine\": \"CYCLES\",\n    \"camera\": \"Presentation Camera\",\n    \"path\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_bust.blend\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_bust.blend\\\"\\n\"\n}", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-16T17:35:38.861327+00:00", "type": "llm_call", "call_index": 6, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-16T17:35:37.676021+00:00", "finished_at": "2026-07-16T17:35:38.860326+00:00", "duration_s": 1.187000000005355, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_EiAaMRToSKz0xJ3cuNin8l6H", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 103, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_eaQymwe5S0AnbTBEr8NAI4wi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_eaQymwe5S0AnbTBEr8NAI4wi", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 9, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_vJspENn0bQYVZ8T1VfGsLASy", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene; sc.render.engine='BLENDER_EEVEE'; sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.image_settings.file_format='PNG';sc.render.filepath='//classical_bust.png';sc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_bust.blend'); bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':bpy.data.filepath}\"}"}}]}, {"role": "tool", "tool_call_id": "call_vJspENn0bQYVZ8T1VfGsLASy", "content": "{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 85,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_bust.blend\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_bust.blend\\\"\\n\",\n  \"stderr\": \"<string>:5: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_OyFy3Y7eQ3kOttgsLm2zh734", "type": "function", "function": {"name": "get_screenshot_of_window_as_image", "arguments": "{\"size_limit_in_bytes\":200000}"}}]}, {"role": "tool", "tool_call_id": "call_OyFy3Y7eQ3kOttgsLm2zh734", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_DJNNcXg3I6yb0V1lUyYTTG0p", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='CYCLES'\\nsc.cycles.samples=64\\nsc.cycles.use_denoising=True\\n# keep final benchmark resolution settings modest overridden by benchmark\\nsc.render.resolution_x=1536;sc.render.resolution_y=1536;sc.render.resolution_percentage=100\\n# make camera active and ensure studio settings\\nsc.camera=bpy.data.objects.get('Presentation Camera')\\n# save final\\nbpy.ops.wm.save_as_mainfile(filepath=bpy.data.filepath)\\nresult={'engine':sc.render.engine,'camera':sc.camera.name,'path':bpy.data.filepath}\"}"}}]}, {"role": "tool", "tool_call_id": "call_DJNNcXg3I6yb0V1lUyYTTG0p", "content": "{\n  \"status\": \"ok\",\n  \"result\": {\n    \"engine\": \"CYCLES\",\n    \"camera\": \"Presentation Camera\",\n    \"path\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_bust.blend\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_bust.blend\\\"\\n\"\n}"}], "tools": [{"type": "function", "function": {"name": "execute_blender_code", "description": "\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code_for_cli", "description": "\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}, "code": {"title": "Code", "type": "string"}}, "required": ["blend_file", "code"], "title": "execute_blender_code_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks", "description": "\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_datablocksArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks_for_cli", "description": "\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_datablocks_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files", "description": "\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_missing_filesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files_for_cli", "description": "\n        Report missing file references by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_missing_files_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries", "description": "\n        Return a tree of directly and indirectly linked library files.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_of_linked_librariesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries_for_cli", "description": "\n        Return linked-library info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_of_linked_libraries_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info", "description": "\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_path_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info_for_cli", "description": "\n        Return path info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_path_info_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess", "description": "\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_usage_guessArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess_for_cli", "description": "\n        Guess use-cases by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_usage_guess_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_detail_summary", "description": "\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "get_object_detail_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_objects_summary", "description": "\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ", "parameters": {"properties": {}, "title": "get_objects_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_python_api_docs", "description": "\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ", "parameters": {"properties": {"identifier": {"title": "Identifier", "type": "string"}}, "required": ["identifier"], "title": "get_python_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_area_as_image", "description": "\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"area_ui_type": {"enum": ["VIEW_3D", "IMAGE_EDITOR", "UV", "ShaderNodeTree", "CompositorNodeTree", "GeometryNodeTree", "TextureNodeTree", "SEQUENCE_EDITOR", "CLIP_EDITOR", "DOPESHEET_EDITOR", "GRAPH_EDITOR", "NLA_EDITOR", "TEXT_EDITOR", "CONSOLE", "INFO", "TOPBAR", "STATUSBAR", "OUTLINER", "PROPERTIES", "FILE_BROWSER", "SPREADSHEET", "PREFERENCES"], "title": "Area Ui Type", "type": "string"}, "size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "required": ["area_ui_type"], "title": "get_screenshot_of_area_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_image", "description": "\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "title": "get_screenshot_of_window_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_json", "description": "\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ", "parameters": {"properties": {}, "title": "get_screenshot_of_window_as_jsonArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_name", "description": "\n        Switch the active workspace tab to *name*.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "jump_to_tab_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_space_type", "description": "\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ", "parameters": {"properties": {"space_type": {"title": "Space Type", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["space_type"], "title": "jump_to_tab_by_space_typeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_by_name", "description": "\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_data_by_name", "description": "\n        Move the 3D viewport to the object whose data block matches *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_data_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_thumbnail_to_path", "description": "\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_thumbnail_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_viewport_to_path", "description": "\n        Render the current scene to *output_path* using current render settings.\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_viewport_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_api_docs", "description": "\nFull-text search over the bundled Blender Python API reference.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_manual_docs", "description": "\nFull-text search over the bundled Blender user manual.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_manual_docsArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-16T17:35:37.676021+00:00", "finished_at": "2026-07-16T17:35:38.860326+00:00", "duration_s": 1.187000000005355, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "204927", "content-type": "application/json"}, "body": "{\"model\":\"openai/gpt-5.6-luna\",\"messages\":[{\"role\":\"system\",\"content\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\\n\\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls.\"},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\\n\\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\\n\\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\\n\\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\\n\\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\\n\\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_EiAaMRToSKz0xJ3cuNin8l6H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy, math\\\\nfrom mathutils import Vector\\\\n# clear\\\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\\\n    return m\\\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\\\n# helpers\\\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\\\ndef cube(name,loc,scale,material,bev=0):\\\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\\\n    return o\\\\ndef cyl(name,loc,r,depth,material,verts=64):\\\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\\\ndef curve_tube(name, pts, bevel, material):\\\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\\\n# pedestal\\\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\\\n# neck and bust\\\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\\\n# shoulders/broken bust block\\\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\\\n# head cranium and jaw\\\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\\\n# ears\\\\nfor x in (-.69,.69):\\\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\\\n# cheekbones prominent\\\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\\\n# brow ridge\\\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\\\n# eyes, lids, iris raised shallow\\\\nfor x in (-.27,.27):\\\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\\\n# nose bridge and tip custom-ish overlapping smooth forms\\\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\\\n# philtrum\\\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\\\n# lips strong classical\\\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\\\n# chin plane\\\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\\\n# hair cap\\\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\\\n# hairline curls across forehead and temples, actual tubes\\\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\\\n    z=3.55-0.06*(abs(x)/.6)\\\\n    # curl descending front, slight spiral-ish polyline\\\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\\\n# side locks and crown rows\\\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\\\n        # clumped wave arcs\\\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\\\n        curve_tube('Crown wave',pts,.085,plaster)\\\\n# side curls near ears\\\\nfor side in (-1,1):\\\\n for j in range(4):\\\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\\\n# subtle casting seam collar ring\\\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\\\n# ground/backdrop large plane\\\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\\\n# backdrop vertical plane behind y=2\\\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(0,0,2.75))\\\\n# lights\\\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\\\n# world/render\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\\\n# organize collections? save\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_EiAaMRToSKz0xJ3cuNin8l6H\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 103, in <module>\\\\nTypeError: bpy_struct: item.attr = val: enum \\\\\\\"BLENDER_EEVEE_NEXT\\\\\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_eaQymwe5S0AnbTBEr8NAI4wi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene\\\\nsc.render.engine='BLENDER_EEVEE'\\\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\\\nsc.world.color=(.035,.03,.025)\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_eaQymwe5S0AnbTBEr8NAI4wi\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 9, in <module>\\\\nRuntimeError: Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_bust.blend@ for writing: No such file or directory\\\\n\\\\n\\\",\\n  \\\"stdout\\\": \\\"Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_bust.blend@ for writing: No such file or directory\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_vJspENn0bQYVZ8T1VfGsLASy\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene; sc.render.engine='BLENDER_EEVEE'; sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.image_settings.file_format='PNG';sc.render.filepath='//classical_bust.png';sc.world.color=(.035,.03,.025)\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_bust.blend'); bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'file':bpy.data.filepath}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_vJspENn0bQYVZ8T1VfGsLASy\",\"content\":\"{\\n  \\\"status\\\": \\\"ok\\\",\\n  \\\"result\\\": {\\n    \\\"objects\\\": 85,\\n    \\\"file\\\": \\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\classical_bust.blend\\\"\\n  },\\n  \\\"stdout\\\": \\\"Info: Saved as \\\\\\\"classical_bust.blend\\\\\\\"\\\\n\\\",\\n  \\\"stderr\\\": \\\"<string>:5: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\\\\\"//\\\\\\\" prefix\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_OyFy3Y7eQ3kOttgsLm2zh734\",\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"arguments\":\"{\\\"size_limit_in_bytes\\\":200000}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_OyFy3Y7eQ3kOttgsLm2zh734\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_DJNNcXg3I6yb0V1lUyYTTG0p\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene\\\\nsc.render.engine='CYCLES'\\\\nsc.cycles.samples=64\\\\nsc.cycles.use_denoising=True\\\\n# keep final benchmark resolution settings modest overridden by benchmark\\\\nsc.render.resolution_x=1536;sc.render.resolution_y=1536;sc.render.resolution_percentage=100\\\\n# make camera active and ensure studio settings\\\\nsc.camera=bpy.data.objects.get('Presentation Camera')\\\\n# save final\\\\nbpy.ops.wm.save_as_mainfile(filepath=bpy.data.filepath)\\\\nresult={'engine':sc.render.engine,'camera':sc.camera.name,'path':bpy.data.filepath}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_DJNNcXg3I6yb0V1lUyYTTG0p\",\"content\":\"{\\n  \\\"status\\\": \\\"ok\\\",\\n  \\\"result\\\": {\\n    \\\"engine\\\": \\\"CYCLES\\\",\\n    \\\"camera\\\": \\\"Presentation Camera\\\",\\n    \\\"path\\\": \\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\classical_bust.blend\\\"\\n  },\\n  \\\"stdout\\\": \\\"Info: Saved as \\\\\\\"classical_bust.blend\\\\\\\"\\\\n\\\"\\n}\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n        Execute Python code in the connected Blender instance.\\n\\n        The code runs in Blender's Python environment with full access to ``bpy``.\\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\\n        Deferred completion via ``check_is_finished`` is only supported by the\\n        interactive addon server, and is rejected in background mode.\\n        \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code_for_cli\",\"description\":\"\\n        Execute Python code in a background Blender process.\\n\\n        Opens *blend_file* with ``blender --background`` and runs *code*.\\n        Assign a dict to ``result`` to return data.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"},\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"blend_file\",\"code\"],\"title\":\"execute_blender_code_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks\",\"description\":\"\\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_datablocksArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks_for_cli\",\"description\":\"\\n        Return a data-block summary by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_datablocks_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files\",\"description\":\"\\n        Report external file references that are missing from disk\\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_missing_filesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files_for_cli\",\"description\":\"\\n        Report missing file references by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_missing_files_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries\",\"description\":\"\\n        Return a tree of directly and indirectly linked library files.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_of_linked_librariesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries_for_cli\",\"description\":\"\\n        Return linked-library info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_of_linked_libraries_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info\",\"description\":\"\\n        Simple/fast access to the blend file's path, save status, age, and backups.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_path_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info_for_cli\",\"description\":\"\\n        Return path info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_path_info_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess\",\"description\":\"\\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_usage_guessArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess_for_cli\",\"description\":\"\\n        Guess use-cases by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_usage_guess_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_detail_summary\",\"description\":\"\\n        Return a structured summary of the object identified by *name*.\\n\\n        Includes type, transforms, parent, children, modifiers, constraints,\\n        materials, visibility, data-block name, and collections.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"get_object_detail_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_objects_summary\",\"description\":\"\\n        Return the scene's collection hierarchy and their objects.\\n\\n        Each collection lists its objects (name, type, parent, data name,\\n        selection, visibility) and nested child collections.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_objects_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_python_api_docs\",\"description\":\"\\n        Return the Blender Python API docs for *identifier*, or list\\n        modules matching a trailing-``*`` discovery pattern.\\n\\n        *identifier* should be a fully-qualified Python name (e.g.\\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\\n        The trailing-``*`` forms are supported as discovery entry-points:\\n\\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\\n          ``mathutils``, ``gpu``, ...).\\n        - ``X.*`` enumerates the direct-child identifiers under the\\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\\n\\n        Both return a ``namespace`` response even when ``X.rst`` would\\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\\n        force the child listing.\\n\\n        The response always carries ``kind``, ``found``, and ``identifier``.\\n        The remaining keys depend on ``kind``:\\n\\n        - ``\\\"exact\\\"`` (``found=True``): ``<identifier>.rst`` was read.\\n          Extra keys: ``content`` (RST text), ``examples``. When the\\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\\n          summary of the file's top-level definitions (prefixed by a\\n          header noting the truncation) and ``examples`` is empty -\\n          re-query individual members for their rendered blocks.\\n        - ``\\\"namespace\\\"`` (``found=True``):\\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\\n          Extra key: ``submodules`` (list of child identifiers).\\n        - ``\\\"definition\\\"`` (``found=True``):\\n          *identifier* is defined inside a parent RST\\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\\n          Extra keys: ``content`` (rendered block), ``examples``.\\n        - ``\\\"partial\\\"`` (``found=False``):\\n          the parent RST was located but the trailing component isn't defined in it.\\n          Extra keys:\\n          - ``parent`` the identifier whose RST was loaded.\\n          - ``available`` top-level definitions in that RST.\\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\\n            filtered to those whose last component contains every character of the missing tail.\\n\\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\\n        - ``\\\"suggestions\\\"`` (``found=False``):\\n          no direct match, but *identifier* appears as a component of other files.\\n          Extra key: ``suggestions`` (list of full identifiers).\\n        - ``\\\"missing\\\"`` (``found=False``): nothing matched.\\n\\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\\n        is a list of ``{path, content}`` entries referenced from this documentation.\\n        \",\"parameters\":{\"properties\":{\"identifier\":{\"title\":\"Identifier\",\"type\":\"string\"}},\"required\":[\"identifier\"],\"title\":\"get_python_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_area_as_image\",\"description\":\"\\n        Take a screenshot of a single Blender area and return it as a PNG image.\\n\\n        *area_ui_type* matches the area's ``ui_type``.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"area_ui_type\":{\"enum\":[\"VIEW_3D\",\"IMAGE_EDITOR\",\"UV\",\"ShaderNodeTree\",\"CompositorNodeTree\",\"GeometryNodeTree\",\"TextureNodeTree\",\"SEQUENCE_EDITOR\",\"CLIP_EDITOR\",\"DOPESHEET_EDITOR\",\"GRAPH_EDITOR\",\"NLA_EDITOR\",\"TEXT_EDITOR\",\"CONSOLE\",\"INFO\",\"TOPBAR\",\"STATUSBAR\",\"OUTLINER\",\"PROPERTIES\",\"FILE_BROWSER\",\"SPREADSHEET\",\"PREFERENCES\"],\"title\":\"Area Ui Type\",\"type\":\"string\"},\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"required\":[\"area_ui_type\"],\"title\":\"get_screenshot_of_area_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"description\":\"\\n        Take a screenshot of the entire Blender window and return it as a PNG image.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"title\":\"get_screenshot_of_window_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_json\",\"description\":\"\\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_screenshot_of_window_as_jsonArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_name\",\"description\":\"\\n        Switch the active workspace tab to *name*.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"jump_to_tab_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_space_type\",\"description\":\"\\n        Switch to a workspace whose main area matches *space_type*.\\n\\n        If *allow_edits* is True and no matching workspace exists, a new one\\n        is created by duplicating the current workspace.\\n        \",\"parameters\":{\"properties\":{\"space_type\":{\"title\":\"Space Type\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"space_type\"],\"title\":\"jump_to_tab_by_space_typeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_by_name\",\"description\":\"\\n        Move the 3D viewport to focus on an object by *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_data_by_name\",\"description\":\"\\n        Move the 3D viewport to the object whose data block matches *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_data_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_thumbnail_to_path\",\"description\":\"\\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_thumbnail_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_viewport_to_path\",\"description\":\"\\n        Render the current scene to *output_path* using current render settings.\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_viewport_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_api_docs\",\"description\":\"\\nFull-text search over the bundled Blender Python API reference.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_manual_docs\",\"description\":\"\\nFull-text search over the bundled Blender user manual.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_manual_docsArguments\",\"type\":\"object\"}}}]}", "body_base64": "{"model":"openai/gpt-5.6-luna","messages":[{"role":"system","content":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical head bust in the Greco-Roman tradition — the kind of plaster cast after the antique that sits in every drawing atelier — presented alone on a pedestal under studio light. This is a pure sculpting test: no costume, no scene, no story to hide behind. The face is the whole assignment.\n\nThe head. An idealized classical head in the manner of the great antique casts (a David, an Apollo, a Doryphoros — the specific reference is free, the idiom is not): calm, symmetric, youthful-heroic features with the canonical structure that atelier casts exist to teach. The planes of the face must be right — brow ridge stepping to the nose, clear cheekbone structure, the philtrum and the strong classical mouth, eyes with sculpted (not textured) iris-and-pupil suggestion or the blank antique eye — choose one convention and hold it. Hair in sculpted classical curls or waves with real clumped mass, not a smooth cap and not noise. The head sits on a neck cut off in the traditional bust termination — a clean geometric truncation into a small integral socle, or shoulders ending in the classic broken-edge cut.\n\nAnatomy is judged mercilessly here. A slightly stylized but structurally correct head beats an uncanny near-likeness. Asymmetry from sculpting is human and fine; collapsed eye sockets, pasted-on ears, or a rubber jawline are not.\n\nMaterial — plaster or marble, committed. Either: matte white atelier plaster with the subtlest subsurface scattering, faint seam lines from the casting mold, small chips at the socle edge, and a whisper of dust-grey in the recesses; or white statuary marble with soft SSS depth and delicate grey veining that follows the form. Pick one and make it convincing at close range. No pure-white shadeless material — the render must prove the material has depth.\n\nPedestal & setting. A simple column pedestal or plinth, a neutral studio backdrop, nothing else. One soft key light placed to rake the facial planes the way a drawing instructor would set it — form shadow down one side of the nose and cheek, a clear terminator, gentle bounce fill so the shadow side keeps its planes. The classic atelier lighting is part of the brief.\n\nComposition. Camera at the bust's eye level, three-quarter view, head filling most of the frame. The final image should look like a photograph from a cast collection catalogue.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_EiAaMRToSKz0xJ3cuNin8l6H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.meshes, bpy.data.curves, bpy.data.materials, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, rough=.55, sss=0.0, metallic=0):\\n    m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True\\n    bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metallic\\n    if 'Subsurface Weight' in bs.inputs: bs.inputs['Subsurface Weight'].default_value=sss\\n    elif 'Subsurface' in bs.inputs: bs.inputs['Subsurface'].default_value=sss\\n    return m\\nplaster=mat('Warm Atelier Plaster',(0.72,0.68,0.58),.62,.055)\\nplaster2=mat('Plaster Recess',(0.31,0.29,0.25),.8,.02)\\nbaseMat=mat('Pedestal Stone',(0.42,0.39,0.34),.7,.02)\\nbackMat=mat('Backdrop',(0.12,0.105,0.09),.8)\\n# helpers\\ndef uv(name, loc, scale, material=plaster, seg=48, rings=32):\\n    bpy.ops.mesh.primitive_uv_sphere_add(segments=seg, ring_count=rings, location=loc); o=bpy.context.object; o.name=name; o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True); o.data.materials.append(material); bpy.ops.object.shade_smooth(); return o\\ndef cube(name,loc,scale,material,bev=0):\\n    bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=name;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(material)\\n    if bev: mod=o.modifiers.new('soft stone edges','BEVEL');mod.width=bev;mod.segments=3\\n    return o\\ndef cyl(name,loc,r,depth,material,verts=64):\\n    bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=depth,location=loc);o=bpy.context.object;o.name=name;o.data.materials.append(material);be=o.modifiers.new('edge softness','BEVEL');be.width=.035;be.segments=3; bpy.ops.object.shade_smooth();return o\\ndef curve_tube(name, pts, bevel, material):\\n    cu=bpy.data.curves.new(name,'CURVE');cu.dimensions='3D';cu.resolution_u=3;cu.bevel_depth=bevel;cu.bevel_resolution=4\\n    sp=cu.splines.new('BEZIER');sp.bezier_points.add(len(pts)-1)\\n    for b,p in zip(sp.bezier_points,pts): b.co=p;b.handle_left_type='AUTO';b.handle_right_type='AUTO'\\n    ob=bpy.data.objects.new(name,cu);bpy.context.collection.objects.link(ob);ob.data.materials.append(material);return ob\\n# pedestal\\ncyl('Lower plinth',(0,0,0.18),1.05,.36,baseMat)\\ncyl('Plinth upper step',(0,0,.43),.87,.18,plaster2)\\ncyl('Classical pedestal',(0,0,1.15),.68,1.35,baseMat)\\ncyl('Pedestal capital',(0,0,1.82),.82,.18,baseMat)\\n# neck and bust\\nuv('Neck',(0,0,2.28),(.47,.42,.78))\\n# shoulders/broken bust block\\nuv('Shoulder mass',(0,.02,2.02),(1.08,.55,.42))\\n# head cranium and jaw\\nuv('Cranium',(0,0,3.12),(.72,.58,.92))\\nuv('Jaw and chin',(0,-.055,2.72),(.57,.48,.58))\\n# ears\\nfor x in (-.69,.69):\\n    uv('Ear_L' if x<0 else 'Ear_R',(x,-.015,3.03),(.14,.09,.25))\\n    uv('Ear inner',(x,-.095,3.03),(.065,.025,.13),plaster2)\\n# cheekbones prominent\\nfor x in (-.36,.36): uv('Cheekbone',(x,-.47,2.94),(.29,.11,.19))\\n# brow ridge\\nfor x in (-.27,.27): uv('Brow ridge',(x,-.515,3.34),(.27,.10,.105))\\n# eyes, lids, iris raised shallow\\nfor x in (-.27,.27):\\n    uv('Upper eyelid',(x,-.586,3.23),(.19,.035,.075),plaster)\\n    uv('Lower eyelid',(x,-.575,3.16),(.17,.028,.045),plaster)\\n    uv('Sculpted iris',(x,-.612,3.21),(.075,.022,.075),plaster2,32,16)\\n    uv('Pupil suggestion',(x,-.632,3.21),(.027,.012,.03),plaster,24,12)\\n# nose bridge and tip custom-ish overlapping smooth forms\\nuv('Nose bridge',(0,-.54,3.16),(.115,.13,.36))\\nuv('Nose tip',(0,-.68,2.98),(.17,.15,.15))\\nfor x in (-.09,.09): uv('Nostril wing',(x,-.66,2.96),(.10,.08,.065))\\n# philtrum\\nuv('Philtrum',(0,-.545,2.82),(.055,.035,.13),plaster2)\\n# lips strong classical\\nuv('Upper lip',(0,-.57,2.77),(.22,.065,.065),plaster)\\nuv('Lower lip',(0,-.585,2.68),(.23,.075,.085),plaster)\\ncurve_tube('Mouth seam',[(-.20,-.65,2.75),(0,-.675,2.73),(.20,-.65,2.75)],.018,plaster2)\\n# chin plane\\nuv('Chin',(0,-.48,2.54),(.27,.12,.16))\\n# hair cap\\nuv('Hair mass',(0,.08,3.67),(.77,.62,.55),plaster2)\\n# hairline curls across forehead and temples, actual tubes\\nfor i,x in enumerate([-0.62,-.48,-.34,-.19,0,.19,.34,.48,.62]):\\n    z=3.55-0.06*(abs(x)/.6)\\n    # curl descending front, slight spiral-ish polyline\\n    curve_tube('Forehead curl',[(x,-.47,z),(x*1.03,-.59,z-.10),(x*.98,-.57,z-.19),(x*.90,-.49,z-.22)],.075,plaster)\\n# side locks and crown rows\\nfor row,(z,y) in enumerate([(3.73,.0),(3.86,.12),(3.96,.2)]):\\n    for x in [-.7,-.53,-.36,-.18,0,.18,.36,.53,.7]:\\n        # clumped wave arcs\\n        pts=[(x,y-.50,z),(x+(.07 if x>=0 else -.07),y-.58,z+.06),(x+(.10 if x>=0 else -.10),y-.48,z+.16)]\\n        curve_tube('Crown wave',pts,.085,plaster)\\n# side curls near ears\\nfor side in (-1,1):\\n for j in range(4):\\n    x=side*(.68+.035*(j%2)); z=3.48-j*.17\\n    curve_tube('Temple curl',[(x,-.30,z),(side*(.78),-.43,z-.07),(side*.69,-.50,z-.15)],.075,plaster)\\n# subtle casting seam collar ring\\ncyl('Bust termination',(0,0,2.05),.54,.09,plaster2)\\n# ground/backdrop large plane\\ncube('Studio floor',(0,0,-.06),(6,6,.05),backMat)\\n# backdrop vertical plane behind y=2\\ncube('Backdrop',(0,3.2,3),(6,.05,3),backMat)\\n# camera\\nbpy.ops.object.camera_add(location=(4.25,-7.2,3.55));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=70\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,2.75))\\n# lights\\nbpy.ops.object.light_add(type='AREA', location=(-3.8,-4.5,6.2)); key=bpy.context.object;key.name='Large soft key';key.data.energy=850;key.data.shape='DISK';key.data.size=4.0;track(key,(0,0,3))\\nbpy.ops.object.light_add(type='AREA', location=(3.5,-2.5,3.3));fill=bpy.context.object;fill.name='Gentle bounce fill';fill.data.energy=280;fill.data.size=5;track(fill,(0,0,2.9))\\nbpy.ops.object.light_add(type='AREA', location=(0,2.0,5.5));rim=bpy.context.object;rim.name='Rim light';rim.data.energy=500;rim.data.size=3;track(rim,(0,0,3.4))\\n# world/render\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025); sc.view_settings.look='AgX - Medium High Contrast'\\n# organize collections? save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]},{"role":"tool","tool_call_id":"call_EiAaMRToSKz0xJ3cuNin8l6H","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 103, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_eaQymwe5S0AnbTBEr8NAI4wi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/classical_bust.png'\\nsc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_bust.blend')\\nbpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':'/tmp/classical_bust.blend','render':'/tmp/classical_bust.png'}\"}"}}]},{"role":"tool","tool_call_id":"call_eaQymwe5S0AnbTBEr8NAI4wi","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 9, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_bust.blend@ for writing: No such file or directory\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_vJspENn0bQYVZ8T1VfGsLASy","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene; sc.render.engine='BLENDER_EEVEE'; sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.image_settings.file_format='PNG';sc.render.filepath='//classical_bust.png';sc.world.color=(.035,.03,.025)\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_bust.blend'); bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'file':bpy.data.filepath}\"}"}}]},{"role":"tool","tool_call_id":"call_vJspENn0bQYVZ8T1VfGsLASy","content":"{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 85,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_bust.blend\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_bust.blend\\\"\\n\",\n  \"stderr\": \"<string>:5: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_OyFy3Y7eQ3kOttgsLm2zh734","type":"function","function":{"name":"get_screenshot_of_window_as_image","arguments":"{\"size_limit_in_bytes\":200000}"}}]},{"role":"tool","tool_call_id":"call_OyFy3Y7eQ3kOttgsLm2zh734","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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bpy\\nsc=bpy.context.scene\\nsc.render.engine='CYCLES'\\nsc.cycles.samples=64\\nsc.cycles.use_denoising=True\\n# keep final benchmark resolution settings modest overridden by benchmark\\nsc.render.resolution_x=1536;sc.render.resolution_y=1536;sc.render.resolution_percentage=100\\n# make camera active and ensure studio settings\\nsc.camera=bpy.data.objects.get('Presentation Camera')\\n# save final\\nbpy.ops.wm.save_as_mainfile(filepath=bpy.data.filepath)\\nresult={'engine':sc.render.engine,'camera':sc.camera.name,'path':bpy.data.filepath}\"}"}}]},{"role":"tool","tool_call_id":"call_DJNNcXg3I6yb0V1lUyYTTG0p","content":"{\n  \"status\": \"ok\",\n  \"result\": {\n    \"engine\": \"CYCLES\",\n    \"camera\": \"Presentation Camera\",\n    \"path\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_bust.blend\"\n  },\n  \"stdout\": \"Info: Saved as 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","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"},"code":{"title":"Code","type":"string"}},"required":["blend_file","code"],"title":"execute_blender_code_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks","description":"\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_datablocksArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks_for_cli","description":"\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_datablocks_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files","description":"\n     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","parameters":{"properties":{},"title":"get_blendfile_summary_of_linked_librariesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries_for_cli","description":"\n        Return linked-library info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_of_linked_libraries_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info","description":"\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_path_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info_for_cli","description":"\n        Return path info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_path_info_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess","description":"\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_usage_guessArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess_for_cli","description":"\n        Guess use-cases by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_usage_guess_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_detail_summary","description":"\n        Return a structured summary of the object identified by 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When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        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