{"ts": "2026-07-15T22:50:16.815352+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": "2026-07-15T22:50:16.815352+00:00", "type": "user", "text": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"}
{"ts": "2026-07-15T22:50:36.119624+00:00", "type": "llm_call", "call_index": 1, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T22:50:16.816861+00:00", "finished_at": "2026-07-15T22:50:36.119624+00:00", "duration_s": 19.312000000005355, "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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T22:50:16.816861+00:00", "finished_at": "2026-07-15T22:50:36.117582+00:00", "duration_s": 19.312000000005355, "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": "20770", "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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\\n\\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\\nscale pattern, and a calm watery backdrop that makes the orange pop.\\n\\n</task>\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. 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The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ","parameters":{"properties":{"job_id":{"default":null,"title":"Job Id","type":"string"}},"title":"poll_hunyuan_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset_hunyuan","description":"\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"zip_file_url":{"title":"Zip File Url","type":"string"}},"required":["name","zip_file_url"],"title":"import_generated_asset_hunyuanArguments","type":"object"}}}]}"}, "response": {"status_code": 200, "headers": {"date": "Wed, 15 Jul 2026 22:50:18 GMT", "content-type": "application/json", "transfer-encoding": "chunked", "connection": "keep-alive", "access-control-allow-origin": "*", "x-generation-id": "gen-1784155817-bk3tkGnh6qdqo9imVhTz", "access-control-expose-headers": "X-Generation-Id,X-Provider-Name,cf-ray", "set-cookie": "[REDACTED]", "permissions-policy": "payment=(self \"https://checkout.stripe.com\" \"https://connect-js.stripe.com\" \"https://js.stripe.com\" \"https://*.js.stripe.com\" \"https://hooks.stripe.com\")", "referrer-policy": "no-referrer, strict-origin-when-cross-origin", "x-content-type-options": "nosniff", "content-encoding": "gzip", "server": "cloudflare", "cf-ray": "a1bc5342ade3ec8e-MAD"}, "body": "\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n{\"id\":\"gen-1784155817-bk3tkGnh6qdqo9imVhTz\",\"object\":\"chat.completion\",\"created\":1784155817,\"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\":\"**Planning a fish scene**\\n\\nI’m working on building a scene using a script. I need to create a fish, a pool backdrop, and a camera render. It involves using mesh primitives and curves. I think one big script will work well, and I’ll save it as a blend file. The fish will have a scaled body made from a UV sphere, with tail fins and translucent materials. I'm considering procedural bump noise for the scale pattern. For the backdrop, I want a blue plane that resembles an aquarium bottom, satisfying the classic goldfish look.**Setting up the fish scene**\\n\\nI’m placing the camera at (5, -7, 3) and orienting the fish along the longitudinal X axis so that its head is facing it. The eyes will be positioned on both sides, helping the camera see the front at y = -0. The tail will sit at x = -1.3. I want the body to be spherical and maybe add a mouth and orange, translucent fins. I’ll consider using Cycles for rendering, creating a curved backdrop with a floor plane and a beveled back wall. Adding bubbles sounds fun too!\",\"tool_calls\":[{\"type\":\"function\",\"index\":0,\"id\":\"call_hruPvztwikjJCvStLh2lVWkZ\",\"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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\\\n    return m\\\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\\\n# helpers\\\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\\\n    if bevel:\\\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\\\n    return o\\\\n# body and head silhouette\\\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\\\n# subtle head cheek bulge\\\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\\\n# tail fin fan at rear x=-1.25, flowing S-ish\\\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\\\n# give thickness via duplicate y\\\\nv=[]\\\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\\\n# dorsal fin curved triangular mesh\\\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\\\nvv=[]\\\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\\\n# pectoral side fin visible near camera y=-\\\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\\\n# far fin\\\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\\\n# ventral small fin\\\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\\\n# eyes both sides, spheres + pupils oriented along y\\\\nfor side in [-1,1]:\\\\n    y=side*.68\\\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\\\n    # pupil outward\\\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\\\n# mouth lips as torus at head front along x\\\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\\\n# scale pattern material nodes on body\\\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\\\n# floor\\\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\\\n# backdrop vertical and side-ish\\\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\\\n# bubbles\\\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\\\n# lighting\\\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\\\ndef area(name,loc,energy,size,color):\\\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\\\n# 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/goldfish.png'; sc.render.film_transparent=False\\\\nsc.view_settings.look='AgX - Medium High Contrast'\\\\n# organize\\\\nfor o in bpy.context.scene.objects: o.select_set(False)\\\\n# save\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\\\"}\"}}],\"reasoning_details\":[{\"type\":\"reasoning.summary\",\"summary\":\"**Planning a fish scene**\\n\\nI’m working on building a scene using a script. I need to create a fish, a pool backdrop, and a camera render. It involves using mesh primitives and curves. I think one big script will work well, and I’ll save it as a blend file. The fish will have a scaled body made from a UV sphere, with tail fins and translucent materials. I'm considering procedural bump noise for the scale pattern. For the backdrop, I want a blue plane that resembles an aquarium bottom, satisfying the classic goldfish look.**Setting up the fish scene**\\n\\nI’m placing the camera at (5, -7, 3) and orienting the fish along the longitudinal X axis so that its head is facing it. The eyes will be positioned on both sides, helping the camera see the front at y = -0. The tail will sit at x = -1.3. I want the body to be spherical and maybe add a mouth and orange, translucent fins. I’ll consider using Cycles for rendering, creating a curved backdrop with a floor plane and a beveled back wall. Adding bubbles sounds fun too!\",\"format\":\"openai-responses-v1\",\"index\":0},{\"type\":\"reasoning.encrypted\",\"data\":\"gAAAAABqWA6vgfOVsvJck7XQXH8TFMKpd6wWaD4_jvKLStJjXxMYql3UFZEaVf0FEwnSwZY-mxcdw3czax9uG7ikt1KzL9oTB4Ng1ls88EiWC0IGeCdXOcpjs6aO6bI8G6OK3y9lq--szOV0h7u1eSXyeaSVCPo2dldtr0Yr6kQPUY8alLq9gqzyaj_892QxB8_jjfoYvLJZbrC2d6A6VbspqDJeTbl71QPaUoHcNTter2psHhy9ZiPqRADWv3a0KUI4QT1TE3wdvBs78C0xY9q7CiviZiPh-UEva4-PBl8oPVj_TH9rgdNxOm9DrGs6EacmAT60LmHTJUQ_7YXhSAR9EIX9jPnlBGeTqMci8gE16wM62hJt9wscscHwNf_pYWJR2eFqKf0IgUjawZzozvw36PXOTIS81PM9q_N75fAlzBaqsq4mBr1sFq7so2uLee8yrncgHZRbAwMPnxRJ_St3Sig2psobxVa3-IC-jGij_UeqzRMhUYbgU81Ut1UeP9C85m8jtmwdXMld6MQS-TrKZw5C9O18GemCA9t0MZne0CRIczVLDqkXqdXeZVlwutsesdHHK3unrnXlQWD4c2-klwjKW71nnIun4BhwsTDCxRgbaQTC0eQT_uRlhfVTOYZpyMy6yax_jCekBOO0WtbtpBOBe2xyPG3f_Mu5m_rVJOdGURtDFSw1CC-hUMGBgmRo901Hd7MoWyFiRBW5qQY9h11dw7K8Agg63RDT0ZZaws2hk0k69xjMdvewBef7v2zVYppDw3yUjrjtodPr009wlzWnJqLrF5HPoVru-oICaDIsftK3t5epTTtVeSKEtceAdcX5SvQAnCLc0-GEZznMWTIJUMg_-cJ2OqylMYUCPs-zq71lWMWixAYLDiTsWNuMyltief2pHrGB7nKKrzL7UDhcOjM51wxn_jWmxPqLZNCubSsWqGfk_fqisaI1OKn-UI8F0bYHPe3kxJnvaYnoOohshfQF0rHVEDVXK-HSR0_jBtnvmB6Lv4l7Z-d8i2CbLXkbBhN7GQ6E66f6zNhcLsqR_z61T5czTzMbC2yNcE1sfqkx6I-VtWAHUAi2jGnJLAtkWDkmwvUv-hAhG38WaclC4A4oTiFS46lEoIMg1nImAovO5GWqZSBBN9SqcaAP_6zlXev77JnvqaY4iUewqwLTsjI3fjtEcXgXnCSX_TEa3cCG24x5Aa3ZRYMdicepBjIqsUkR7J7pLQL6wWeqVsIIg0PyZmnbyrPlyJ6Cd59aFDdlkxYqPtWHiLymCuG0_W7UsUBTrnqSd_0qv6QrMfoba4g6DAx-O_eRATOH2752OTNGDYvO0_ii05cJwDecQKZHUiKfv3xXFXrFYTewOd8F6c0V22UUWRyIG473HZa8tTyZA95aq0x9ngY1ApnTZeiIlpR17k_TCXMFNhx7551RQkMNNGRxD61Xr0SSHnsBSdlCl-tR_vEUTcrUZxGjPZ-_VC0cnU_C89NgxnaO-vIoh_kZPffnFi-AzNqLhzzk6csTuqTpTIoW-AkZDAZjtSDVvhU2bUS2JBnVHvKSuDAHb3Z7KV21YpLSwzFXyCs0mQ6aUNbJt4nCO-52H1kE_vNr9gxjRZ6sQzsPVxikmOIZnWQ2UlXwBwvx9zzGZ-iceFf16HMXB205F1G93SD8-duDv4ssd6_wegmdmHdaeAk6E1GXg-Ady8a6Tc3sfmZzCKsMBgo23L1loHFxuAatAOXb4MRGuU829WLPPxF7nE2tljSyaVctxx6kYmtksUTHbl31yD-N95u_nu5fjz_ymgbNVFYxIj91rqUoU7dni-RxujzHWO4TcKeE3Kw4APzuIfTNoePPt2qPVJWcX87_sfDbkpNumI_S5_TT5nNeNJOfKj9I7c90wIx64ScUlgwQiV8M6j7kb_gNYAZe0hUBzJPn6DVoXthgu-IeAOimEPne9uMpqCs6Lr8_k5A2U3quyPMdm3HnX0ItbyOtbVaK2tG43MS6qRFnfDJew-SVn4iwTACbSqG5PF6HCAlhMLw0YKjsQlw2xn2F268BEx04_8S6Ibd2kkgs19tqPDfgnErF-2WPKibKLGI1aBAgD9DVEfC_YgqoPhoEigw8J16mEg3E8U_OU9hedKf9GdGZ8bwRpxYlE5OPKPsTV_MI2DdSlzVpb55nHBYAnJpUkoFWPpKoGOQnT6Nl0oQbOFCztOKiOC87ecB4OF4bce9aE7g67mNjPM3yLxc0J3-zc8fXlb9Czjf24JQ3HhPyZ7-dqBvNra_PLj5fMKpF6BmMU5OiAmK-EVw8wb9QuR1MXY1-jO0_5b5Vj2GXfGfJEWdymxLzB7tuczOXDzCpISuaJbW5xGU9Pp9Tv1JH2NtAp1YZtCx8Xhus-IJc5HaQydqEL-f-iG2yvvp-YEpk-D9DrFkln8FL1UmVXL9x3inVqQdWhUOophweZsuPNHkWuZyuCbjikCCTp8dFCPJ6QFyvyYjnz_aSpypZ8hfLBTM7oxBRVVnUJRjnxW9HSGkOBnHCdUdLMnUCzGbCrkhHQsNgYR9YE_SMffajEaEG41FN98GN00Nkos0L-FTaC4X2mix4_SSX3vSGmiF6mJmhGWXxBJkW2lT1erfmkwvK46L-oZVfg0NtQhzZJDeIN1bfTyg402gwFAUnF6goPE5rK6xzhtoBqPlzr2vcqCXSqotjvar6OqtrN9lCPPvUfvGS-UjT-m2qu42nKw==\",\"format\":\"openai-responses-v1\",\"id\":\"rs_0126df23b58745f5016a580eaa1f60819685cca320b10a1e5d\",\"index\":1}]}}],\"usage\":{\"prompt_tokens\":3711,\"completion_tokens\":2613,\"total_tokens\":6324,\"cost\":0.020316,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":0,\"cache_write_tokens\":3708,\"audio_tokens\":0,\"video_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0.020316,\"upstream_inference_prompt_cost\":0.004638,\"upstream_inference_completions_cost\":0.015678},\"completion_tokens_details\":{\"reasoning_tokens\":288,\"image_tokens\":0,\"audio_tokens\":0}}}", "body_base64": "
         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         
{"id":"gen-1784155817-bk3tkGnh6qdqo9imVhTz","object":"chat.completion","created":1784155817,"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":"**Planning a fish scene**\n\nI’m working on building a scene using a script. I need to create a fish, a pool backdrop, and a camera render. It involves using mesh primitives and curves. I think one big script will work well, and I’ll save it as a blend file. The fish will have a scaled body made from a UV sphere, with tail fins and translucent materials. I'm considering procedural bump noise for the scale pattern. For the backdrop, I want a blue plane that resembles an aquarium bottom, satisfying the classic goldfish look.**Setting up the fish scene**\n\nI’m placing the camera at (5, -7, 3) and orienting the fish along the longitudinal X axis so that its head is facing it. The eyes will be positioned on both sides, helping the camera see the front at y = -0. The tail will sit at x = -1.3. I want the body to be spherical and maybe add a mouth and orange, translucent fins. I’ll consider using Cycles for rendering, creating a curved backdrop with a floor plane and a beveled back wall. Adding bubbles sounds fun too!","tool_calls":[{"type":"function","index":0,"id":"call_hruPvztwikjJCvStLh2lVWkZ","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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}"}}],"reasoning_details":[{"type":"reasoning.summary","summary":"**Planning a fish scene**\n\nI’m working on building a scene using a script. I need to create a fish, a pool backdrop, and a camera render. It involves using mesh primitives and curves. I think one big script will work well, and I’ll save it as a blend file. The fish will have a scaled body made from a UV sphere, with tail fins and translucent materials. I'm considering procedural bump noise for the scale pattern. For the backdrop, I want a blue plane that resembles an aquarium bottom, satisfying the classic goldfish look.**Setting up the fish scene**\n\nI’m placing the camera at (5, -7, 3) and orienting the fish along the longitudinal X axis so that its head is facing it. The eyes will be positioned on both sides, helping the camera see the front at y = -0. The tail will sit at x = -1.3. I want the body to be spherical and maybe add a mouth and orange, translucent fins. I’ll consider using Cycles for rendering, creating a curved backdrop with a floor plane and a beveled back wall. Adding bubbles sounds fun too!","format":"openai-responses-v1","index":0},{"type":"reasoning.encrypted","data":"gAAAAABqWA6vgfOVsvJck7XQXH8TFMKpd6wWaD4_jvKLStJjXxMYql3UFZEaVf0FEwnSwZY-mxcdw3czax9uG7ikt1KzL9oTB4Ng1ls88EiWC0IGeCdXOcpjs6aO6bI8G6OK3y9lq--szOV0h7u1eSXyeaSVCPo2dldtr0Yr6kQPUY8alLq9gqzyaj_892QxB8_jjfoYvLJZbrC2d6A6VbspqDJeTbl71QPaUoHcNTter2psHhy9ZiPqRADWv3a0KUI4QT1TE3wdvBs78C0xY9q7CiviZiPh-UEva4-PBl8oPVj_TH9rgdNxOm9DrGs6EacmAT60LmHTJUQ_7YXhSAR9EIX9jPnlBGeTqMci8gE16wM62hJt9wscscHwNf_pYWJR2eFqKf0IgUjawZzozvw36PXOTIS81PM9q_N75fAlzBaqsq4mBr1sFq7so2uLee8yrncgHZRbAwMPnxRJ_St3Sig2psobxVa3-IC-jGij_UeqzRMhUYbgU81Ut1UeP9C85m8jtmwdXMld6MQS-TrKZw5C9O18GemCA9t0MZne0CRIczVLDqkXqdXeZVlwutsesdHHK3unrnXlQWD4c2-klwjKW71nnIun4BhwsTDCxRgbaQTC0eQT_uRlhfVTOYZpyMy6yax_jCekBOO0WtbtpBOBe2xyPG3f_Mu5m_rVJOdGURtDFSw1CC-hUMGBgmRo901Hd7MoWyFiRBW5qQY9h11dw7K8Agg63RDT0ZZaws2hk0k69xjMdvewBef7v2zVYppDw3yUjrjtodPr009wlzWnJqLrF5HPoVru-oICaDIsftK3t5epTTtVeSKEtceAdcX5SvQAnCLc0-GEZznMWTIJUMg_-cJ2OqylMYUCPs-zq71lWMWixAYLDiTsWNuMyltief2pHrGB7nKKrzL7UDhcOjM51wxn_jWmxPqLZNCubSsWqGfk_fqisaI1OKn-UI8F0bYHPe3kxJnvaYnoOohshfQF0rHVEDVXK-HSR0_jBtnvmB6Lv4l7Z-d8i2CbLXkbBhN7GQ6E66f6zNhcLsqR_z61T5czTzMbC2yNcE1sfqkx6I-VtWAHUAi2jGnJLAtkWDkmwvUv-hAhG38WaclC4A4oTiFS46lEoIMg1nImAovO5GWqZSBBN9SqcaAP_6zlXev77JnvqaY4iUewqwLTsjI3fjtEcXgXnCSX_TEa3cCG24x5Aa3ZRYMdicepBjIqsUkR7J7pLQL6wWeqVsIIg0PyZmnbyrPlyJ6Cd59aFDdlkxYqPtWHiLymCuG0_W7UsUBTrnqSd_0qv6QrMfoba4g6DAx-O_eRATOH2752OTNGDYvO0_ii05cJwDecQKZHUiKfv3xXFXrFYTewOd8F6c0V22UUWRyIG473HZa8tTyZA95aq0x9ngY1ApnTZeiIlpR17k_TCXMFNhx7551RQkMNNGRxD61Xr0SSHnsBSdlCl-tR_vEUTcrUZxGjPZ-_VC0cnU_C89NgxnaO-vIoh_kZPffnFi-AzNqLhzzk6csTuqTpTIoW-AkZDAZjtSDVvhU2bUS2JBnVHvKSuDAHb3Z7KV21YpLSwzFXyCs0mQ6aUNbJt4nCO-52H1kE_vNr9gxjRZ6sQzsPVxikmOIZnWQ2UlXwBwvx9zzGZ-iceFf16HMXB205F1G93SD8-duDv4ssd6_wegmdmHdaeAk6E1GXg-Ady8a6Tc3sfmZzCKsMBgo23L1loHFxuAatAOXb4MRGuU829WLPPxF7nE2tljSyaVctxx6kYmtksUTHbl31yD-N95u_nu5fjz_ymgbNVFYxIj91rqUoU7dni-RxujzHWO4TcKeE3Kw4APzuIfTNoePPt2qPVJWcX87_sfDbkpNumI_S5_TT5nNeNJOfKj9I7c90wIx64ScUlgwQiV8M6j7kb_gNYAZe0hUBzJPn6DVoXthgu-IeAOimEPne9uMpqCs6Lr8_k5A2U3quyPMdm3HnX0ItbyOtbVaK2tG43MS6qRFnfDJew-SVn4iwTACbSqG5PF6HCAlhMLw0YKjsQlw2xn2F268BEx04_8S6Ibd2kkgs19tqPDfgnErF-2WPKibKLGI1aBAgD9DVEfC_YgqoPhoEigw8J16mEg3E8U_OU9hedKf9GdGZ8bwRpxYlE5OPKPsTV_MI2DdSlzVpb55nHBYAnJpUkoFWPpKoGOQnT6Nl0oQbOFCztOKiOC87ecB4OF4bce9aE7g67mNjPM3yLxc0J3-zc8fXlb9Czjf24JQ3HhPyZ7-dqBvNra_PLj5fMKpF6BmMU5OiAmK-EVw8wb9QuR1MXY1-jO0_5b5Vj2GXfGfJEWdymxLzB7tuczOXDzCpISuaJbW5xGU9Pp9Tv1JH2NtAp1YZtCx8Xhus-IJc5HaQydqEL-f-iG2yvvp-YEpk-D9DrFkln8FL1UmVXL9x3inVqQdWhUOophweZsuPNHkWuZyuCbjikCCTp8dFCPJ6QFyvyYjnz_aSpypZ8hfLBTM7oxBRVVnUJRjnxW9HSGkOBnHCdUdLMnUCzGbCrkhHQsNgYR9YE_SMffajEaEG41FN98GN00Nkos0L-FTaC4X2mix4_SSX3vSGmiF6mJmhGWXxBJkW2lT1erfmkwvK46L-oZVfg0NtQhzZJDeIN1bfTyg402gwFAUnF6goPE5rK6xzhtoBqPlzr2vcqCXSqotjvar6OqtrN9lCPPvUfvGS-UjT-m2qu42nKw==","format":"openai-responses-v1","id":"rs_0126df23b58745f5016a580eaa1f60819685cca320b10a1e5d","index":1}]}}],"usage":{"prompt_tokens":3711,"completion_tokens":2613,"total_tokens":6324,"cost":0.020316,"is_byok":false,"prompt_tokens_details":{"cached_tokens":0,"cache_write_tokens":3708,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.020316,"upstream_inference_prompt_cost":0.004638,"upstream_inference_completions_cost":0.015678},"completion_tokens_details":{"reasoning_tokens":288,"image_tokens":0,"audio_tokens":0}}}"}, "error": null}], "response": {"text": null, "tool_calls": [{"id": "call_hruPvztwikjJCvStLh2lVWkZ", "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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\n    pass\n# materials\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\n    return m\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\n# helpers\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\n    if bevel:\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\n    return o\n# body and head silhouette\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\n# subtle head cheek bulge\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\n# tail fin fan at rear x=-1.25, flowing S-ish\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\n# give thickness via duplicate y\nv=[]\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\n# dorsal fin curved triangular mesh\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\nvv=[]\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\n# pectoral side fin visible near camera y=-\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\n# far fin\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\n# ventral small fin\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\n# eyes both sides, spheres + pupils oriented along y\nfor side in [-1,1]:\n    y=side*.68\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\n    # pupil outward\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\n# mouth lips as torus at head front along x\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\n# scale pattern material nodes on body\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\n# floor\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\n# backdrop vertical and side-ish\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\n# bubbles\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\n# lighting\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\ndef area(name,loc,energy,size,color):\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\n# camera\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\n# 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/goldfish.png'; sc.render.film_transparent=False\nsc.view_settings.look='AgX - Medium High Contrast'\n# organize\nfor o in bpy.context.scene.objects: o.select_set(False)\n# save\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\nbpy.ops.render.render(write_still=True)\n", "user_prompt": "Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape."}, "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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}", "parse_error": null}], "tokens_in": 3711, "tokens_out": 2613, "cost_usd": 0.020316, "cached_tokens": 0}, "error": null}
{"ts": "2026-07-15T22:50:36.122132+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "call_hruPvztwikjJCvStLh2lVWkZ", "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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\n    pass\n# materials\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\n    return m\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\n# helpers\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\n    if bevel:\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\n    return o\n# body and head silhouette\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\n# subtle head cheek bulge\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\n# tail fin fan at rear x=-1.25, flowing S-ish\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\n# give thickness via duplicate y\nv=[]\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\n# dorsal fin curved triangular mesh\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\nvv=[]\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\n# pectoral side fin visible near camera y=-\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\n# far fin\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\n# ventral small fin\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\n# eyes both sides, spheres + pupils oriented along y\nfor side in [-1,1]:\n    y=side*.68\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\n    # pupil outward\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\n# mouth lips as torus at head front along x\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\n# scale pattern material nodes on body\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\n# floor\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\n# backdrop vertical and side-ish\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\n# bubbles\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\n# lighting\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\ndef area(name,loc,energy,size,color):\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\n# camera\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\n# 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/goldfish.png'; sc.render.film_transparent=False\nsc.view_settings.look='AgX - Medium High Contrast'\n# organize\nfor o in bpy.context.scene.objects: o.select_set(False)\n# save\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\nbpy.ops.render.render(write_still=True)\n", "user_prompt": "Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape."}, "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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}", "parse_error": null}]}
{"ts": "2026-07-15T22:50:36.123142+00:00", "type": "tool_call", "id": "call_hruPvztwikjJCvStLh2lVWkZ", "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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\n    pass\n# materials\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\n    return m\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\n# helpers\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\n    if bevel:\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\n    return o\n# body and head silhouette\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\n# subtle head cheek bulge\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\n# tail fin fan at rear x=-1.25, flowing S-ish\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\n# give thickness via duplicate y\nv=[]\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\n# dorsal fin curved triangular mesh\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\nvv=[]\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\n# pectoral side fin visible near camera y=-\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\n# far fin\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\n# ventral small fin\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\n# eyes both sides, spheres + pupils oriented along y\nfor side in [-1,1]:\n    y=side*.68\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\n    # pupil outward\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\n# mouth lips as torus at head front along x\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\n# scale pattern material nodes on body\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\n# floor\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\n# backdrop vertical and side-ish\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\n# bubbles\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\n# lighting\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\ndef area(name,loc,energy,size,color):\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\n# camera\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\n# 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/goldfish.png'; sc.render.film_transparent=False\nsc.view_settings.look='AgX - Medium High Contrast'\n# organize\nfor o in bpy.context.scene.objects: o.select_set(False)\n# save\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\nbpy.ops.render.render(write_still=True)\n", "user_prompt": "Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape."}, "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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}"}
{"ts": "2026-07-15T22:50:36.495840+00:00", "type": "tool_result", "tool_call_id": "call_hruPvztwikjJCvStLh2lVWkZ", "name": "execute_blender_code", "text": "Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \"BLENDER_EEVEE_NEXT\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T22:50:40.520283+00:00", "type": "llm_call", "call_index": 2, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T22:50:36.495840+00:00", "finished_at": "2026-07-15T22:50:40.520283+00:00", "duration_s": 4.01600000000326, "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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_hruPvztwikjJCvStLh2lVWkZ", "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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}"}}]}, {"role": "tool", "tool_call_id": "call_hruPvztwikjJCvStLh2lVWkZ", "content": "Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \"BLENDER_EEVEE_NEXT\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T22:50:36.495840+00:00", "finished_at": "2026-07-15T22:50:40.520283+00:00", "duration_s": 4.01600000000326, "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": "27894", "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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\\n\\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\\nscale pattern, and a calm watery backdrop that makes the orange pop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_hruPvztwikjJCvStLh2lVWkZ\",\"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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\\\n    return m\\\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\\\n# helpers\\\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\\\n    if bevel:\\\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\\\n    return o\\\\n# body and head silhouette\\\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\\\n# subtle head cheek bulge\\\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\\\n# tail fin fan at rear x=-1.25, flowing S-ish\\\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\\\n# give thickness via duplicate y\\\\nv=[]\\\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\\\n# dorsal fin curved triangular mesh\\\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\\\nvv=[]\\\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\\\n# pectoral side fin visible near camera y=-\\\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\\\n# far fin\\\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\\\n# ventral small fin\\\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\\\n# eyes both sides, spheres + pupils oriented along y\\\\nfor side in [-1,1]:\\\\n    y=side*.68\\\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\\\n    # pupil outward\\\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\\\n# mouth lips as torus at head front along x\\\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\\\n# scale pattern material nodes on body\\\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\\\n# floor\\\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\\\n# backdrop vertical and side-ish\\\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\\\n# bubbles\\\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\\\n# lighting\\\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\\\ndef area(name,loc,energy,size,color):\\\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\\\n# 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/goldfish.png'; sc.render.film_transparent=False\\\\nsc.view_settings.look='AgX - Medium High Contrast'\\\\n# organize\\\\nfor o in bpy.context.scene.objects: o.select_set(False)\\\\n# save\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_hruPvztwikjJCvStLh2lVWkZ\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_hruPvztwikjJCvStLh2lVWkZ","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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}"}}]},{"role":"tool","tool_call_id":"call_hruPvztwikjJCvStLh2lVWkZ","content":"Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \"BLENDER_EEVEE_NEXT\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. 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The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. 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I might need to set the engine and save the render. The phrase about the floor being a shape a child could name instantly could imply an aquarium floor, or maybe the seabed. It seems I need to set cycles and render accordingly. Let's explore what steps to take next!\",\"tool_calls\":[{\"type\":\"function\",\"index\":0,\"id\":\"call_qYLnon6DHPEuvBOPo3C1RL5R\",\"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/goldfish.png'; sc.render.film_transparent=False\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Finalize and render the goldfish hero scene.\\\"}\"}}],\"reasoning_details\":[{\"type\":\"reasoning.summary\",\"summary\":\"**Considering object execution**\\n\\nI’m thinking about whether to rerun the whole code or just continue from where the error occurred since some objects already exist. I might need to set the engine and save the render. The phrase about the floor being a shape a child could name instantly could imply an aquarium floor, or maybe the seabed. It seems I need to set cycles and render accordingly. Let's explore what steps to take 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sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Finalize and render the goldfish hero scene.\"}", "parse_error": null}], "tokens_in": 6099, "tokens_out": 248, "cost_usd": 0.0048468, "cached_tokens": 3708}, "error": null}
{"ts": "2026-07-15T22:50:40.521279+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "call_qYLnon6DHPEuvBOPo3C1RL5R", "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/goldfish.png'; sc.render.film_transparent=False\ntry: sc.view_settings.look='AgX - Medium High Contrast'\nexcept: pass\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\nbpy.ops.render.render(write_still=True)\n", "user_prompt": "Finalize and render the goldfish hero scene."}, "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/goldfish.png'; sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Finalize and render the goldfish hero scene.\"}", "parse_error": null}]}
{"ts": "2026-07-15T22:50:40.521279+00:00", "type": "tool_call", "id": "call_qYLnon6DHPEuvBOPo3C1RL5R", "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/goldfish.png'; sc.render.film_transparent=False\ntry: sc.view_settings.look='AgX - Medium High Contrast'\nexcept: pass\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\nbpy.ops.render.render(write_still=True)\n", "user_prompt": "Finalize and render the goldfish hero scene."}, "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/goldfish.png'; sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Finalize and render the goldfish hero scene.\"}"}
{"ts": "2026-07-15T22:50:40.632660+00:00", "type": "tool_result", "tool_call_id": "call_qYLnon6DHPEuvBOPo3C1RL5R", "name": "execute_blender_code", "text": "Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file <repo>\\apps\\console\\tmp\\goldfish_hero.blend@ for writing: No such file or directory\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T22:50:46.408286+00:00", "type": "llm_call", "call_index": 3, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T22:50:40.633671+00:00", "finished_at": "2026-07-15T22:50:46.408286+00:00", "duration_s": 5.764999999984866, "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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_hruPvztwikjJCvStLh2lVWkZ", "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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}"}}]}, {"role": "tool", "tool_call_id": "call_hruPvztwikjJCvStLh2lVWkZ", "content": "Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \"BLENDER_EEVEE_NEXT\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_qYLnon6DHPEuvBOPo3C1RL5R", "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/goldfish.png'; sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Finalize and render the goldfish hero scene.\"}"}}]}, {"role": "tool", "tool_call_id": "call_qYLnon6DHPEuvBOPo3C1RL5R", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file <repo>\\apps\\console\\tmp\\goldfish_hero.blend@ for writing: No such file or directory\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T22:50:40.633671+00:00", "finished_at": "2026-07-15T22:50:46.408286+00:00", "duration_s": 5.764999999984866, "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": "28915", "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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\\n\\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\\nscale pattern, and a calm watery backdrop that makes the orange pop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_hruPvztwikjJCvStLh2lVWkZ\",\"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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\\\n    return m\\\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\\\n# helpers\\\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\\\n    if bevel:\\\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\\\n    return o\\\\n# body and head silhouette\\\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\\\n# subtle head cheek bulge\\\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\\\n# tail fin fan at rear x=-1.25, flowing S-ish\\\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\\\n# give thickness via duplicate y\\\\nv=[]\\\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\\\n# dorsal fin curved triangular mesh\\\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\\\nvv=[]\\\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\\\n# pectoral side fin visible near camera y=-\\\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\\\n# far fin\\\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\\\n# ventral small fin\\\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\\\n# eyes both sides, spheres + pupils oriented along y\\\\nfor side in [-1,1]:\\\\n    y=side*.68\\\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\\\n    # pupil outward\\\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\\\n# mouth lips as torus at head front along x\\\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\\\n# scale pattern material nodes on body\\\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\\\n# floor\\\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\\\n# backdrop vertical and side-ish\\\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\\\n# bubbles\\\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\\\n# lighting\\\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\\\ndef area(name,loc,energy,size,color):\\\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\\\n# 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/goldfish.png'; sc.render.film_transparent=False\\\\nsc.view_settings.look='AgX - Medium High Contrast'\\\\n# organize\\\\nfor o in bpy.context.scene.objects: o.select_set(False)\\\\n# save\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_hruPvztwikjJCvStLh2lVWkZ\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_qYLnon6DHPEuvBOPo3C1RL5R\",\"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/goldfish.png'; sc.render.film_transparent=False\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Finalize and render the goldfish hero scene.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_qYLnon6DHPEuvBOPo3C1RL5R\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file <repo>\\\\apps\\\\console\\\\tmp\\\\goldfish_hero.blend@ for writing: No such file or directory\\n\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_hruPvztwikjJCvStLh2lVWkZ","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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}"}}]},{"role":"tool","tool_call_id":"call_hruPvztwikjJCvStLh2lVWkZ","content":"Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \"BLENDER_EEVEE_NEXT\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_qYLnon6DHPEuvBOPo3C1RL5R","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/goldfish.png'; sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Finalize and render the goldfish hero scene.\"}"}}]},{"role":"tool","tool_call_id":"call_qYLnon6DHPEuvBOPo3C1RL5R","content":"Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file C:\\Users\\gruku\\Files\\Projects\\Blender-Bench\\apps\\console\\tmp\\goldfish_hero.blend@ for writing: No such file or directory\n"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. 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If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. 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The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. 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{"ts": "2026-07-15T22:50:52.516483+00:00", "type": "llm_call", "call_index": 4, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T22:50:48.273606+00:00", "finished_at": "2026-07-15T22:50:52.515459+00:00", "duration_s": 4.235000000015134, "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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_hruPvztwikjJCvStLh2lVWkZ", "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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}"}}]}, {"role": "tool", "tool_call_id": "call_hruPvztwikjJCvStLh2lVWkZ", "content": "Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \"BLENDER_EEVEE_NEXT\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_qYLnon6DHPEuvBOPo3C1RL5R", "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/goldfish.png'; sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Finalize and render the goldfish hero scene.\"}"}}]}, {"role": "tool", "tool_call_id": "call_qYLnon6DHPEuvBOPo3C1RL5R", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file <repo>\\apps\\console\\tmp\\goldfish_hero.blend@ for writing: No such file or directory\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_O7UVzLGdZyU6OAhUw8q86v8z", "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='//goldfish.png'; sc.render.film_transparent=False\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Save and render the completed goldfish scene.\"}"}}]}, {"role": "tool", "tool_call_id": "call_O7UVzLGdZyU6OAhUw8q86v8z", "content": "Code executed successfully: Info: Saved as \"goldfish_hero.blend\"\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T22:50:48.273606+00:00", "finished_at": "2026-07-15T22:50:52.515459+00:00", "duration_s": 4.235000000015134, "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": "29690", "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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\\n\\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\\nscale pattern, and a calm watery backdrop that makes the orange pop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_hruPvztwikjJCvStLh2lVWkZ\",\"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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\\\n    return m\\\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\\\n# helpers\\\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\\\n    if bevel:\\\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\\\n    return o\\\\n# body and head silhouette\\\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\\\n# subtle head cheek bulge\\\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\\\n# tail fin fan at rear x=-1.25, flowing S-ish\\\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\\\n# give thickness via duplicate y\\\\nv=[]\\\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\\\n# dorsal fin curved triangular mesh\\\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\\\nvv=[]\\\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\\\n# pectoral side fin visible near camera y=-\\\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\\\n# far fin\\\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\\\n# ventral small fin\\\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\\\n# eyes both sides, spheres + pupils oriented along y\\\\nfor side in [-1,1]:\\\\n    y=side*.68\\\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\\\n    # pupil outward\\\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\\\n# mouth lips as torus at head front along x\\\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\\\n# scale pattern material nodes on body\\\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\\\n# floor\\\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\\\n# backdrop vertical and side-ish\\\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\\\n# bubbles\\\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\\\n# lighting\\\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\\\ndef area(name,loc,energy,size,color):\\\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\\\n# 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/goldfish.png'; sc.render.film_transparent=False\\\\nsc.view_settings.look='AgX - Medium High Contrast'\\\\n# organize\\\\nfor o in bpy.context.scene.objects: o.select_set(False)\\\\n# save\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_hruPvztwikjJCvStLh2lVWkZ\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_qYLnon6DHPEuvBOPo3C1RL5R\",\"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/goldfish.png'; sc.render.film_transparent=False\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Finalize and render the goldfish hero scene.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_qYLnon6DHPEuvBOPo3C1RL5R\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file <repo>\\\\apps\\\\console\\\\tmp\\\\goldfish_hero.blend@ for writing: No such file or directory\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_O7UVzLGdZyU6OAhUw8q86v8z\",\"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='//goldfish.png'; sc.render.film_transparent=False\\\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Save and render the completed goldfish scene.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_O7UVzLGdZyU6OAhUw8q86v8z\",\"content\":\"Code executed successfully: Info: Saved as \\\"goldfish_hero.blend\\\"\\n\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_hruPvztwikjJCvStLh2lVWkZ","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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}"}}]},{"role":"tool","tool_call_id":"call_hruPvztwikjJCvStLh2lVWkZ","content":"Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \"BLENDER_EEVEE_NEXT\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_qYLnon6DHPEuvBOPo3C1RL5R","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/goldfish.png'; sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Finalize and render the goldfish hero scene.\"}"}}]},{"role":"tool","tool_call_id":"call_qYLnon6DHPEuvBOPo3C1RL5R","content":"Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file C:\\Users\\gruku\\Files\\Projects\\Blender-Bench\\apps\\console\\tmp\\goldfish_hero.blend@ for writing: No such file or directory\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_O7UVzLGdZyU6OAhUw8q86v8z","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='//goldfish.png'; sc.render.film_transparent=False\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Save and render the completed goldfish scene.\"}"}}]},{"role":"tool","tool_call_id":"call_O7UVzLGdZyU6OAhUw8q86v8z","content":"Code executed successfully: Info: Saved as \"goldfish_hero.blend\"\n"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ","parameters":{"properties":{"job_id":{"default":null,"title":"Job Id","type":"string"}},"title":"poll_hunyuan_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset_hunyuan","description":"\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    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"}
{"ts": "2026-07-15T22:50:54.230988+00:00", "type": "image", "tool": "get_viewport_screenshot"}
{"ts": "2026-07-15T22:51:03.539627+00:00", "type": "llm_call", "call_index": 5, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T22:50:54.231989+00:00", "finished_at": "2026-07-15T22:51:03.539627+00:00", "duration_s": 9.312999999994645, "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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_hruPvztwikjJCvStLh2lVWkZ", "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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}"}}]}, {"role": "tool", "tool_call_id": "call_hruPvztwikjJCvStLh2lVWkZ", "content": "Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \"BLENDER_EEVEE_NEXT\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_qYLnon6DHPEuvBOPo3C1RL5R", "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/goldfish.png'; sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Finalize and render the goldfish hero scene.\"}"}}]}, {"role": "tool", "tool_call_id": "call_qYLnon6DHPEuvBOPo3C1RL5R", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file <repo>\\apps\\console\\tmp\\goldfish_hero.blend@ for writing: No such file or directory\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_O7UVzLGdZyU6OAhUw8q86v8z", "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='//goldfish.png'; sc.render.film_transparent=False\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Save and render the completed goldfish scene.\"}"}}]}, {"role": "tool", "tool_call_id": "call_O7UVzLGdZyU6OAhUw8q86v8z", "content": "Code executed successfully: Info: Saved as \"goldfish_hero.blend\"\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_HQ0mejfYDwh8DYhhNM9EaMrw", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":700,\"user_prompt\":\"Inspect the final goldfish hero scene and composition.\"}"}}]}, {"role": "tool", "tool_call_id": "call_HQ0mejfYDwh8DYhhNM9EaMrw", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T22:50:54.231989+00:00", "finished_at": "2026-07-15T22:51:03.538091+00:00", "duration_s": 9.296999999991385, "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": "145162", "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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\\n\\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\\nscale pattern, and a calm watery backdrop that makes the orange pop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_hruPvztwikjJCvStLh2lVWkZ\",\"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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\\\n    return m\\\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\\\n# helpers\\\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\\\n    if bevel:\\\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\\\n    return o\\\\n# body and head silhouette\\\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\\\n# subtle head cheek bulge\\\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\\\n# tail fin fan at rear x=-1.25, flowing S-ish\\\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\\\n# give thickness via duplicate y\\\\nv=[]\\\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\\\n# dorsal fin curved triangular mesh\\\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\\\nvv=[]\\\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\\\n# pectoral side fin visible near camera y=-\\\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\\\n# far fin\\\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\\\n# ventral small fin\\\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\\\n# eyes both sides, spheres + pupils oriented along y\\\\nfor side in [-1,1]:\\\\n    y=side*.68\\\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\\\n    # pupil outward\\\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\\\n# mouth lips as torus at head front along x\\\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\\\n# scale pattern material nodes on body\\\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\\\n# floor\\\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\\\n# backdrop vertical and side-ish\\\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\\\n# bubbles\\\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\\\n# lighting\\\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\\\ndef area(name,loc,energy,size,color):\\\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\\\n# 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/goldfish.png'; sc.render.film_transparent=False\\\\nsc.view_settings.look='AgX - Medium High Contrast'\\\\n# organize\\\\nfor o in bpy.context.scene.objects: o.select_set(False)\\\\n# save\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_hruPvztwikjJCvStLh2lVWkZ\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_qYLnon6DHPEuvBOPo3C1RL5R\",\"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/goldfish.png'; sc.render.film_transparent=False\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Finalize and render the goldfish hero scene.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_qYLnon6DHPEuvBOPo3C1RL5R\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file <repo>\\\\apps\\\\console\\\\tmp\\\\goldfish_hero.blend@ for writing: No such file or directory\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_O7UVzLGdZyU6OAhUw8q86v8z\",\"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='//goldfish.png'; sc.render.film_transparent=False\\\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Save and render the completed goldfish scene.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_O7UVzLGdZyU6OAhUw8q86v8z\",\"content\":\"Code executed successfully: Info: Saved as \\\"goldfish_hero.blend\\\"\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_HQ0mejfYDwh8DYhhNM9EaMrw\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":700,\\\"user_prompt\\\":\\\"Inspect the final goldfish hero scene and composition.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_HQ0mejfYDwh8DYhhNM9EaMrw\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_hruPvztwikjJCvStLh2lVWkZ","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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}"}}]},{"role":"tool","tool_call_id":"call_hruPvztwikjJCvStLh2lVWkZ","content":"Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \"BLENDER_EEVEE_NEXT\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_qYLnon6DHPEuvBOPo3C1RL5R","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/goldfish.png'; sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Finalize and render the goldfish hero scene.\"}"}}]},{"role":"tool","tool_call_id":"call_qYLnon6DHPEuvBOPo3C1RL5R","content":"Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file C:\\Users\\gruku\\Files\\Projects\\Blender-Bench\\apps\\console\\tmp\\goldfish_hero.blend@ for writing: No such file or directory\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_O7UVzLGdZyU6OAhUw8q86v8z","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='//goldfish.png'; sc.render.film_transparent=False\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Save and render the completed goldfish scene.\"}"}}]},{"role":"tool","tool_call_id":"call_O7UVzLGdZyU6OAhUw8q86v8z","content":"Code executed successfully: Info: Saved as \"goldfish_hero.blend\"\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_HQ0mejfYDwh8DYhhNM9EaMrw","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":700,\"user_prompt\":\"Inspect the final goldfish hero scene and composition.\"}"}}]},{"role":"tool","tool_call_id":"call_HQ0mejfYDwh8DYhhNM9EaMrw","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. 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"error": null}], "response": {"text": null, "tool_calls": [{"id": "call_vA4ihwebIJozXoHdFJyAA8Ee", "name": "execute_blender_code", "arguments": {"code": "import bpy\n# switch saved final to Cycles as required by benchmark, retain good settings\nsc=bpy.context.scene\nsc.render.engine='CYCLES'; sc.cycles.samples=64; sc.cycles.use_denoising=True\nsc.render.resolution_x=1536; sc.render.resolution_y=1536; sc.render.resolution_percentage=100\n# make camera view active in visible 3D areas\nfor a in bpy.context.screen.areas:\n    if a.type=='VIEW_3D':\n        a.spaces.active.region_3d.view_perspective='CAMERA'\n# save final\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\n", "user_prompt": "Set final benchmark render settings to Cycles, enable denoising, frame the hero camera, and save."}, "raw_arguments": "{\"code\":\"import bpy\\n# switch saved final to Cycles as required by benchmark, retain good settings\\nsc=bpy.context.scene\\nsc.render.engine='CYCLES'; sc.cycles.samples=64; sc.cycles.use_denoising=True\\nsc.render.resolution_x=1536; sc.render.resolution_y=1536; sc.render.resolution_percentage=100\\n# make camera view active in visible 3D areas\\nfor a in bpy.context.screen.areas:\\n    if a.type=='VIEW_3D':\\n        a.spaces.active.region_3d.view_perspective='CAMERA'\\n# save final\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\n\",\"user_prompt\":\"Set final benchmark render settings to Cycles, enable denoising, frame the hero camera, and save.\"}", "parse_error": null}], "tokens_in": 6887, "tokens_out": 538, "cost_usd": 0.011836, "cached_tokens": 0}, "error": null}
{"ts": "2026-07-15T22:51:03.544734+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "call_vA4ihwebIJozXoHdFJyAA8Ee", "name": "execute_blender_code", "arguments": {"code": "import bpy\n# switch saved final to Cycles as required by benchmark, retain good settings\nsc=bpy.context.scene\nsc.render.engine='CYCLES'; sc.cycles.samples=64; sc.cycles.use_denoising=True\nsc.render.resolution_x=1536; sc.render.resolution_y=1536; sc.render.resolution_percentage=100\n# make camera view active in visible 3D areas\nfor a in bpy.context.screen.areas:\n    if a.type=='VIEW_3D':\n        a.spaces.active.region_3d.view_perspective='CAMERA'\n# save final\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\n", "user_prompt": "Set final benchmark render settings to Cycles, enable denoising, frame the hero camera, and save."}, "raw_arguments": "{\"code\":\"import bpy\\n# switch saved final to Cycles as required by benchmark, retain good settings\\nsc=bpy.context.scene\\nsc.render.engine='CYCLES'; sc.cycles.samples=64; sc.cycles.use_denoising=True\\nsc.render.resolution_x=1536; sc.render.resolution_y=1536; sc.render.resolution_percentage=100\\n# make camera view active in visible 3D areas\\nfor a in bpy.context.screen.areas:\\n    if a.type=='VIEW_3D':\\n        a.spaces.active.region_3d.view_perspective='CAMERA'\\n# save final\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\n\",\"user_prompt\":\"Set final benchmark render settings to Cycles, enable denoising, frame the hero camera, and save.\"}", "parse_error": null}]}
{"ts": "2026-07-15T22:51:03.544734+00:00", "type": "tool_call", "id": "call_vA4ihwebIJozXoHdFJyAA8Ee", "name": "execute_blender_code", "arguments": {"code": "import bpy\n# switch saved final to Cycles as required by benchmark, retain good settings\nsc=bpy.context.scene\nsc.render.engine='CYCLES'; sc.cycles.samples=64; sc.cycles.use_denoising=True\nsc.render.resolution_x=1536; sc.render.resolution_y=1536; sc.render.resolution_percentage=100\n# make camera view active in visible 3D areas\nfor a in bpy.context.screen.areas:\n    if a.type=='VIEW_3D':\n        a.spaces.active.region_3d.view_perspective='CAMERA'\n# save final\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\n", "user_prompt": "Set final benchmark render settings to Cycles, enable denoising, frame the hero camera, and save."}, "raw_arguments": "{\"code\":\"import bpy\\n# switch saved final to Cycles as required by benchmark, retain good settings\\nsc=bpy.context.scene\\nsc.render.engine='CYCLES'; sc.cycles.samples=64; sc.cycles.use_denoising=True\\nsc.render.resolution_x=1536; sc.render.resolution_y=1536; sc.render.resolution_percentage=100\\n# make camera view active in visible 3D areas\\nfor a in bpy.context.screen.areas:\\n    if a.type=='VIEW_3D':\\n        a.spaces.active.region_3d.view_perspective='CAMERA'\\n# save final\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\n\",\"user_prompt\":\"Set final benchmark render settings to Cycles, enable denoising, frame the hero camera, and save.\"}"}
{"ts": "2026-07-15T22:51:03.666304+00:00", "type": "tool_result", "tool_call_id": "call_vA4ihwebIJozXoHdFJyAA8Ee", "name": "execute_blender_code", "text": "Code executed successfully: Info: Saved as \"goldfish_hero.blend\"\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T22:51:05.655638+00:00", "type": "llm_call", "call_index": 6, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T22:51:03.666304+00:00", "finished_at": "2026-07-15T22:51:05.655638+00:00", "duration_s": 1.9839999999967404, "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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_hruPvztwikjJCvStLh2lVWkZ", "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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}"}}]}, {"role": "tool", "tool_call_id": "call_hruPvztwikjJCvStLh2lVWkZ", "content": "Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \"BLENDER_EEVEE_NEXT\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_qYLnon6DHPEuvBOPo3C1RL5R", "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/goldfish.png'; sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Finalize and render the goldfish hero scene.\"}"}}]}, {"role": "tool", "tool_call_id": "call_qYLnon6DHPEuvBOPo3C1RL5R", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file <repo>\\apps\\console\\tmp\\goldfish_hero.blend@ for writing: No such file or directory\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_O7UVzLGdZyU6OAhUw8q86v8z", "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='//goldfish.png'; sc.render.film_transparent=False\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Save and render the completed goldfish scene.\"}"}}]}, {"role": "tool", "tool_call_id": "call_O7UVzLGdZyU6OAhUw8q86v8z", "content": "Code executed successfully: Info: Saved as \"goldfish_hero.blend\"\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_HQ0mejfYDwh8DYhhNM9EaMrw", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":700,\"user_prompt\":\"Inspect the final goldfish hero scene and composition.\"}"}}]}, {"role": "tool", "tool_call_id": "call_HQ0mejfYDwh8DYhhNM9EaMrw", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_vA4ihwebIJozXoHdFJyAA8Ee", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n# switch saved final to Cycles as required by benchmark, retain good settings\\nsc=bpy.context.scene\\nsc.render.engine='CYCLES'; sc.cycles.samples=64; sc.cycles.use_denoising=True\\nsc.render.resolution_x=1536; sc.render.resolution_y=1536; sc.render.resolution_percentage=100\\n# make camera view active in visible 3D areas\\nfor a in bpy.context.screen.areas:\\n    if a.type=='VIEW_3D':\\n        a.spaces.active.region_3d.view_perspective='CAMERA'\\n# save final\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\n\",\"user_prompt\":\"Set final benchmark render settings to Cycles, enable denoising, frame the hero camera, and save.\"}"}}]}, {"role": "tool", "tool_call_id": "call_vA4ihwebIJozXoHdFJyAA8Ee", "content": "Code executed successfully: Info: Saved as \"goldfish_hero.blend\"\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T22:51:03.666304+00:00", "finished_at": "2026-07-15T22:51:05.654098+00:00", "duration_s": 1.9839999999967404, "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": "146151", "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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\\n\\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\\nscale pattern, and a calm watery backdrop that makes the orange pop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_hruPvztwikjJCvStLh2lVWkZ\",\"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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\\\n    return m\\\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\\\n# helpers\\\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\\\n    if bevel:\\\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\\\n    return o\\\\n# body and head silhouette\\\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\\\n# subtle head cheek bulge\\\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\\\n# tail fin fan at rear x=-1.25, flowing S-ish\\\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\\\n# give thickness via duplicate y\\\\nv=[]\\\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\\\n# dorsal fin curved triangular mesh\\\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\\\nvv=[]\\\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\\\n# pectoral side fin visible near camera y=-\\\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\\\n# far fin\\\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\\\n# ventral small fin\\\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\\\n# eyes both sides, spheres + pupils oriented along y\\\\nfor side in [-1,1]:\\\\n    y=side*.68\\\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\\\n    # pupil outward\\\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\\\n# mouth lips as torus at head front along x\\\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\\\n# scale pattern material nodes on body\\\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\\\n# floor\\\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\\\n# backdrop vertical and side-ish\\\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\\\n# bubbles\\\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\\\n# lighting\\\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\\\ndef area(name,loc,energy,size,color):\\\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\\\n# 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/goldfish.png'; sc.render.film_transparent=False\\\\nsc.view_settings.look='AgX - Medium High Contrast'\\\\n# organize\\\\nfor o in bpy.context.scene.objects: o.select_set(False)\\\\n# save\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_hruPvztwikjJCvStLh2lVWkZ\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_qYLnon6DHPEuvBOPo3C1RL5R\",\"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/goldfish.png'; sc.render.film_transparent=False\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Finalize and render the goldfish hero scene.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_qYLnon6DHPEuvBOPo3C1RL5R\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file <repo>\\\\apps\\\\console\\\\tmp\\\\goldfish_hero.blend@ for writing: No such file or directory\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_O7UVzLGdZyU6OAhUw8q86v8z\",\"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='//goldfish.png'; sc.render.film_transparent=False\\\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\\\nbpy.ops.render.render(write_still=True)\\\\n\\\",\\\"user_prompt\\\":\\\"Save and render the completed goldfish scene.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_O7UVzLGdZyU6OAhUw8q86v8z\",\"content\":\"Code executed successfully: Info: Saved as \\\"goldfish_hero.blend\\\"\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_HQ0mejfYDwh8DYhhNM9EaMrw\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":700,\\\"user_prompt\\\":\\\"Inspect the final goldfish hero scene and composition.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_HQ0mejfYDwh8DYhhNM9EaMrw\",\"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_vA4ihwebIJozXoHdFJyAA8Ee\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n# switch saved final to Cycles as required by benchmark, retain good settings\\\\nsc=bpy.context.scene\\\\nsc.render.engine='CYCLES'; sc.cycles.samples=64; sc.cycles.use_denoising=True\\\\nsc.render.resolution_x=1536; sc.render.resolution_y=1536; sc.render.resolution_percentage=100\\\\n# make camera view active in visible 3D areas\\\\nfor a in bpy.context.screen.areas:\\\\n    if a.type=='VIEW_3D':\\\\n        a.spaces.active.region_3d.view_perspective='CAMERA'\\\\n# save final\\\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\\\n\\\",\\\"user_prompt\\\":\\\"Set final benchmark render settings to Cycles, enable denoising, frame the hero camera, and save.\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_vA4ihwebIJozXoHdFJyAA8Ee\",\"content\":\"Code executed successfully: Info: Saved as \\\"goldfish_hero.blend\\\"\\n\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"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 single goldfish as the hero of the shot: rounded body, a big flowing tail fin,\ndorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.\n\nThe floor is a shape any child would name instantly. The ceiling is life: a gentle\nS-curve in the body as if mid-swim, translucency toward the fin edges, a subtle\nscale pattern, and a calm watery backdrop that makes the orange pop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_hruPvztwikjJCvStLh2lVWkZ","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.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name, color, metallic=0, rough=.4, transmission=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 'Transmission Weight' in bs.inputs: bs.inputs['Transmission Weight'].default_value=transmission\\n    return m\\norange=mat('Goldfish Orange',(0.95,.18,.025),0,.28)\\norange2=mat('Fin Orange',(1.0,.25,.035),0,.3,.18)\\nwhite=mat('Eye White',(.95,.95,.82),0,.18)\\nblack=mat('Deep Black Pupil',(.005,.002,.001),.1,.12)\\ngold=mat('Iris Gold',(1.0,.48,.03),.2,.2)\\nwater=mat('Calm Aqua Floor',(0.025,.22,.28),0,.24)\\nback=mat('Watery Backdrop',(0.018,.11,.18),0,.32)\\nbubble=mat('Bubbles',(.25,.8,.9),0,.1,.65)\\n# helpers\\ndef uv(name, loc, scale, material, seg=64, 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 mesh_obj(name, verts, faces, material, bevel=0.0):\\n    me=bpy.data.meshes.new(name+'Mesh'); me.from_pydata(verts,[],faces); me.materials.append(material); me.update(); o=bpy.data.objects.new(name,me); bpy.context.collection.objects.link(o)\\n    if bevel:\\n        mod=o.modifiers.new('Soft edges','BEVEL'); mod.width=bevel; mod.segments=3\\n    return o\\n# body and head silhouette\\nbody=uv('Rounded Goldfish Body',(0,0,1.65),(1.52,.72,.9),orange)\\n# subtle head cheek bulge\\nuv('Soft Cheek',(1.05,-.02,1.72),(.62,.68,.7),orange)\\n# tail fin fan at rear x=-1.25, flowing S-ish\\nverts=[(-1.15,0,1.7),(-1.75,0,2.45),(-2.45,0,2.72),(-2.72,0,2.35),(-2.35,0,1.72),(-2.75,0,1.15),(-2.5,0,.62),(-1.75,0,.88),(-1.15,0,1.42)]\\n# give thickness via duplicate y\\nv=[]\\nfor y in [-.045,.045]: v += [(x,y,z) for x,_,z in verts]\\nfaces=[tuple(range(9)),tuple(range(17,8,-1))]\\nfor i in range(9): faces.append((i,(i+1)%9,(i+1)%9+9,i+9))\\ntail=mesh_obj('Large Flowing Tail Fin',v,faces,orange2,.025)\\n# dorsal fin curved triangular mesh\\nv=[(-.55,0,2.28),(.05,0,3.05),(.55,0,2.45),(.35,0,2.22),(-.4,0,2.2)]\\nvv=[]\\nfor y in [-.035,.035]: vv += [(x,y,z) for x,_,z in v]\\nf=[(0,1,2,3,4),(9,8,7,6,5)]+[(i,(i+1)%5,(i+1)%5+5,i+5) for i in range(5)]\\nmesh_obj('Dorsal Fin',vv,f,orange2,.02)\\n# pectoral side fin visible near camera y=-\\nv=[(.65,-.48,1.85),(1.25,-1.12,1.45),(.35,-1.02,1.05),(.12,-.5,1.48)]\\nmesh_obj('Near Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# far fin\\nv=[(.45,.48,1.75),(1.05,1.0,1.35),(.25,1.0,1.12),(.1,.5,1.45)]\\nmesh_obj('Far Side Fin',v,[(0,1,2,3)],orange2,.035)\\n# ventral small fin\\nv=[(-.1,-.1,.9),(.45,-.1,.35),(.85,-.1,.75),(.35,-.1,1.0)]\\nmesh_obj('Ventral Fin',v,[(0,1,2,3)],orange2,.02)\\n# eyes both sides, spheres + pupils oriented along y\\nfor side in [-1,1]:\\n    y=side*.68\\n    eye=uv(('Near' if side<0 else 'Far')+' Eye',(1.12,y,1.98),(.23,.16,.23),white,32,16)\\n    # pupil outward\\n    uv(('Near' if side<0 else 'Far')+' Pupil',(1.12,side*.825,2.0),(.115,.045,.125),black,24,12)\\n    uv(('Near' if side<0 else 'Far')+' Iris Ring',(1.12,side*.795,2.0),(.155,.025,.16),gold,24,12)\\n# mouth lips as torus at head front along x\\nbpy.ops.mesh.primitive_torus_add(major_radius=.16,minor_radius=.045,major_segments=32,minor_segments=10,location=(1.62,-.02,1.52),rotation=(0,math.pi/2,0)); mouth=bpy.context.object; mouth.name='Small Curious Mouth'; mouth.data.materials.append(orange2)\\n# scale pattern material nodes on body\\nm=orange; nt=m.node_tree; bs=nt.nodes.get('Principled BSDF'); tex=nt.nodes.new('ShaderNodeTexNoise'); tex.inputs['Scale'].default_value=13; tex.inputs['Detail'].default_value=2; bump=nt.nodes.new('ShaderNodeBump'); bump.inputs['Strength'].default_value=.12; bump.inputs['Distance'].default_value=.045; nt.links.new(tex.outputs['Fac'],bump.inputs['Height']); nt.links.new(bump.outputs['Normal'],bs.inputs['Normal'])\\n# floor\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,0,0)); floor=bpy.context.object; floor.name='Aquarium Floor'; floor.data.materials.append(water)\\n# backdrop vertical and side-ish\\nbpy.ops.mesh.primitive_plane_add(size=30, location=(0,4,8), rotation=(math.pi/2,0,0)); wall=bpy.context.object; wall.name='Calm Watery Backdrop'; wall.data.materials.append(back)\\n# bubbles\\nfor i,(x,y,z,s) in enumerate([(-2,-.4,2.9,.09),(-1.8,-.3,3.35,.055),(-1.55,-.2,3.75,.035),(2,-.25,2.8,.07),(2.2,-.1,3.1,.04),(-.3,.2,3.5,.045)]):\\n    uv('Bubble_%02d'%i,(x,y,z),(s,s,s),bubble,24,12)\\n# lighting\\nworld=bpy.context.scene.world or bpy.data.worlds.new('World'); bpy.context.scene.world=world; world.use_nodes=True; world.node_tree.nodes['Background'].inputs['Color'].default_value=(.008,.025,.05,1); world.node_tree.nodes['Background'].inputs['Strength'].default_value=.28\\ndef area(name,loc,energy,size,color):\\n    d=bpy.data.lights.new(name,'AREA'); d.energy=energy; d.shape='DISK'; d.size=size; d.color=color; o=bpy.data.objects.new(name,d); bpy.context.collection.objects.link(o); o.location=loc; return o\\narea('Key Light',(2,-4,6),950,5,(1.0,.72,.48)); area('Rim Light',(-3,2,4),1100,4,(.15,.5,1)); area('Fill',(4,1,2),500,3,(.3,.7,1))\\n# camera\\nbpy.ops.object.camera_add(location=(5.3,-8.0,3.6)); cam=bpy.context.object; cam.name='Hero Camera'; bpy.context.scene.camera=cam\\ndef track(obj,pt): obj.rotation_euler=(Vector(pt)-obj.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(-.25,0,1.7)); cam.data.lens=54\\n# 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/goldfish.png'; sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast'\\n# organize\\nfor o in bpy.context.scene.objects: o.select_set(False)\\n# save\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Create a single goldfish hero scene with rounded orange body, flowing tail, dorsal and side fins, eyes on both sides, aquarium-like calm watery backdrop and child-friendly simple floor shape.\"}"}}]},{"role":"tool","tool_call_id":"call_hruPvztwikjJCvStLh2lVWkZ","content":"Error executing code: Communication error with Blender: Code execution error: bpy_struct: item.attr = val: enum \"BLENDER_EEVEE_NEXT\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_qYLnon6DHPEuvBOPo3C1RL5R","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/goldfish.png'; sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Finalize and render the goldfish hero scene.\"}"}}]},{"role":"tool","tool_call_id":"call_qYLnon6DHPEuvBOPo3C1RL5R","content":"Error executing code: Communication error with Blender: Code execution error: Error: Cannot open file C:\\Users\\gruku\\Files\\Projects\\Blender-Bench\\apps\\console\\tmp\\goldfish_hero.blend@ for writing: No such file or directory\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_O7UVzLGdZyU6OAhUw8q86v8z","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='//goldfish.png'; sc.render.film_transparent=False\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\nbpy.ops.render.render(write_still=True)\\n\",\"user_prompt\":\"Save and render the completed goldfish scene.\"}"}}]},{"role":"tool","tool_call_id":"call_O7UVzLGdZyU6OAhUw8q86v8z","content":"Code executed successfully: Info: Saved as \"goldfish_hero.blend\"\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_HQ0mejfYDwh8DYhhNM9EaMrw","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":700,\"user_prompt\":\"Inspect the final goldfish hero scene and composition.\"}"}}]},{"role":"tool","tool_call_id":"call_HQ0mejfYDwh8DYhhNM9EaMrw","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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bpy\\n# switch saved final to Cycles as required by benchmark, retain good settings\\nsc=bpy.context.scene\\nsc.render.engine='CYCLES'; sc.cycles.samples=64; sc.cycles.use_denoising=True\\nsc.render.resolution_x=1536; sc.render.resolution_y=1536; sc.render.resolution_percentage=100\\n# make camera view active in visible 3D areas\\nfor a in bpy.context.screen.areas:\\n    if a.type=='VIEW_3D':\\n        a.spaces.active.region_3d.view_perspective='CAMERA'\\n# save final\\nbpy.ops.wm.save_as_mainfile(filepath='//goldfish_hero.blend')\\n\",\"user_prompt\":\"Set final benchmark render settings to Cycles, enable denoising, frame the hero camera, and save.\"}"}}]},{"role":"tool","tool_call_id":"call_vA4ihwebIJozXoHdFJyAA8Ee","content":"Code executed successfully: Info: Saved as \"goldfish_hero.blend\"\n"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - 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