[System]
You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.

Your 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.

Complete 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.

The 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.

The 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.

## Harness Instructions

You are writing a single Python script for Blender 5.1 using the bpy API. The scene starts empty. Your script must build everything the user's prompt asks for.

You 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.

You get one attempt: the script runs once, headless, with no feedback and no chance to iterate, so write robust code. Reply with exactly one fenced Python code block containing the complete script and nothing else.

[User]
## Task

Complete the following Blender task:

<task>

A single goldfish as the hero of the shot: rounded body, a big flowing tail fin,
dorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.

The floor is a shape any child would name instantly. The ceiling is life: a gentle
S-curve in the body as if mid-swim, translucency toward the fin edges, a subtle
scale pattern, and a calm watery backdrop that makes the orange pop.

</task>

[System]
You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.

Your 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.

Complete 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.

The 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.

The 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.

## Harness Instructions

You are writing a single Python script for Blender 5.1 using the bpy API. The scene starts empty. Your script must build everything the user's prompt asks for.

You 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.

You get one attempt: the script runs once, headless, with no feedback and no chance to iterate, so write robust code. Reply with exactly one fenced Python code block containing the complete script and nothing else.

[User]
## Task

Complete the following Blender task:

<task>

A single goldfish as the hero of the shot: rounded body, a big flowing tail fin,
dorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.

The floor is a shape any child would name instantly. The ceiling is life: a gentle
S-curve in the body as if mid-swim, translucency toward the fin edges, a subtle
scale pattern, and a calm watery backdrop that makes the orange pop.

</task>

payment=(self "https://checkout.stripe.com" "https://connect-js.stripe.com" "https://js.stripe.com" "https://*.js.stripe.com" "https://hooks.stripe.com")

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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[System]
You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.

Your 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.

Complete 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.

The 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.

The 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.

## Harness Instructions

You are writing a single Python script for Blender 5.1 using the bpy API. The scene starts empty. Your script must build everything the user's prompt asks for.

You 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.

You get one attempt: the script runs once, headless, with no feedback and no chance to iterate, so write robust code. Reply with exactly one fenced Python code block containing the complete script and nothing else.

[User]
## Task

Complete the following Blender task:

<task>

A single goldfish as the hero of the shot: rounded body, a big flowing tail fin,
dorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.

The floor is a shape any child would name instantly. The ceiling is life: a gentle
S-curve in the body as if mid-swim, translucency toward the fin edges, a subtle
scale pattern, and a calm watery backdrop that makes the orange pop.

</task>

payment=(self "https://checkout.stripe.com" "https://connect-js.stripe.com" "https://js.stripe.com" "https://*.js.stripe.com" "https://hooks.stripe.com")

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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[System]
You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.

Your 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.

Complete 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.

The 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.

The 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.

## Harness Instructions

You are writing a single Python script for Blender 5.1 using the bpy API. The scene starts empty. Your script must build everything the user's prompt asks for.

You 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.

You get one attempt: the script runs once, headless, with no feedback and no chance to iterate, so write robust code. Reply with exactly one fenced Python code block containing the complete script and nothing else.

[User]
## Task

Complete the following Blender task:

<task>

A single goldfish as the hero of the shot: rounded body, a big flowing tail fin,
dorsal and side fins, and an eye on each side, in an unmistakable goldfish orange.

The floor is a shape any child would name instantly. The ceiling is life: a gentle
S-curve in the body as if mid-swim, translucency toward the fin edges, a subtle
scale pattern, and a calm watery backdrop that makes the orange pop.

</task>

payment=(self "https://checkout.stripe.com" "https://connect-js.stripe.com" "https://js.stripe.com" "https://*.js.stripe.com" "https://hooks.stripe.com")

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

data: {"id":"gen-1784488643-ORCSMyO7U5nXSJA35rZ0","object":"chat.completion.chunk","created":1784488643,"model":"openai/gpt-5.6-sol","provider":"OpenAI","choices":[{"index":0,"delta":{"content":"","role":"assistant","reasoning":null,"reasoning_details":[{"type":"reasoning.encrypted","data":"gAAAAABqXSMeI6zScAthVC3eiQiWguDgcXOiRTMGmLFWGeEyGNpoDH3BuGdq8njE1wqMyh5WSxOEuRijYSbIEqGtZL67uQSUsuFyDLJSna2pypNE_N_6bTkFM2v3CzdD7gK7l2KFqjeHvy01rDPKv6REKC-E8JsewrNSfUdt8xHC70-QVaQsxMa20uxAgzXfqtgqLM2QRwnShPhduvHiBWIqzd5Neia_q_YoV3B5iLPoKHRC8hVoBuIqQlVlN1EN0M6raxx-Sn0FPLe2VZG3vWYw8L7P58C4x1fQ2dMX0jLMjVYUzJDkdeecIO6p4XEs6Zp-IIoxYkHtTTGEgYs8M1JgwhvsCgCI5QymYC-YWrgi8G5kkWL_QWY6uMBYHrMzqjAXLhyLe0wlBTCdaqqpFSyjg7T0Ki3_6fJR3SGZ_6KRQeoQNSTJhg1EofFM4ZRJb4e1HIOdVQxXHfRVDbKy96YelkNnclS4y_xGT5Vf5PQjnzJKFDnDO6BlepzdclzIgx9b5xpfOCxF8tb-46M78nVROIyZUT_04buhxJ_jjgBKXTravaLRF2K7TkBNpXmEE6-yJCGBmcNEws9WJh8bvDAU-3tIXr9gVspAzOKAJ6bc5eXBRhrPdXDf7v5GwVPole1jSVHdB7H76WyPCV0KgwRmS4UpuRABV_HD1Tu7V6-dXIgWbcogNuMgFeEEw28I3EgBHRjyg64ypABI7afkZvE4JhDKoRZcddqnURGo_Ct8x-6mOeqwMXQ-8D22Nf5Fe3HQwW7mptMcCdJqhPAAnWS772M7M8YEMaf80kdwQKdskj4B_n2p6HOlHBwlNxULLSKwoPHB3M0wIyzdozN-wzwyuIy96WQl4sbM2ZI-YKWDNIuYPXXEijqUJ06-DwCSBxKHYk5mNPKJyNn4g3PU-2XSMzwEC3cl4PVrgjJuTBMcxOix-8CCL8PT7fmujU99mPkwX3gsAm3iO9N_HzDDYo0SWfkHp8gPHfCeVLx5-Gtz5A4xYXQ6hnOZhTplG6LhWpyjHoYf5X_uwn8BPltHAEVxL1kOGgeyavFFPpowRr1y-jVB85OGB9R09fDY6DSorORguMg2xwfT0vaVU_1-ibC9727dHlSrTz1id1aGAHgdlct1v7ySD0i-PgnEMWuU4eldpZgoDPGoDvy0r76oDdqD-Vi5ejfx3EpTV4sxHytSAGGOzOP_vhWg09c00_EewNafuVCpgNJtp3LllTFozFb5sGwt3tCJEkhcJa25YwSI9DNOLm8lsXeCGsJo7BzliLkp2rVd3Pr-dxfRX9F0ev0IsYyEsqSbOngWNLQQ55UAYa_CCqRehoEeMBlO5JCiTlWBgXg9j7Uu3hS_C1d-n00GS0m6ZVOhsefJzbpak5S6-WinDRjqxsVMHZtzmDYvzrGGqmSqPtb7uxp4zOqdzhIyIaoa_dPdaZs2aYXEwvZ6TV61OYxOEgq6a5iJ-N4Ka9THnO0CX_K9L-KQtVvalti1lfmLwUF-mNusMO4u7QfOspqLzdEbC7aI-AmUpC32QCOfD1b56zJrAfRM_59YEFPG8vHGbE41saCMbCdywodxGsak51gxsDwLLUbH6tDqhoRHoeawSQWa8ZP78J0XODEfb7fYU6nJZOWpz-I4FD_RMgEg5WjZuHNSxzXMATc_C248jL8LV0f5F6kLrxBsUkqvtkKpWou8AOsT930toDpreP6N8L8JcfM_n3Ql1QYv5xzKoEz1zeu0QS7ADaB7rO6CDn8EsgqGOKhX9uZR4gYEEIEdwjBt52l4Mj8BypvMK2fQ5Cs5JIQ1DO_AtFXG5k5p2xZMVsN12mPWHC4v3r4Tmlu1Fl5r0e8_lBZh3zvPTE0HUWP_B6RisFIA9ujAcOh-3mvz43GjNHQogdFQGybKo8gRMigBEW4KtxJ_vsdVaVOledCq3f-zeDis1m1x9pxGOmucN7I-GON5RkW2CxphHMeL7U1HHv7B6yGk4QWAgjHg2I3uTlVDheR7VmRpvqscmBgceyb9DOdAQW_Zm-V0SRHkGY-GCeB2OkubZxIzhbHmkn05UZIHY15R0nJXg22RaSAGnluN11Ev77P53Lud6un2JoTberrTVfLIGcpZp3_CmfpOwps7KKsPwKFxw08xgw_gBwiR_PX-kU3_OLgQ8Uxg4CnDAkKV0JU7-yBiKosWyZKj434CQjNWTOheMKp2OBLnIxUN4NWu5SB2F4OPafkYQgj5TV5zBET0-4pcX0d_MUsnlr16ErTOStOFpXr_xHG7PvH89Usz6LHgRxPD9WYqJMqVaMAt1dKgVhUPxCa6MpEct5ttY13MObF4wyG6-rmDFxbBHZ8E63SClSIVNn5FV2NsAANKhFBeB8qD_jgJdwguWZhJaRmiv05dxvdULDSxwHxtECzVEmoQWNg3pT9P65Jyy5MS3uuFxhJ44n7HurzUVZM82fu3yiXgu3aWJWAyZIjZldY75U4F-0DL3ahnTC0BV_c7ti4N1al1mSAbvGJijF_qlKlC9dW1_f1PZFLPQ2eXvHzU2lpsjDJDj-Ob3VdlGODWdcTIDCkOMdVhnsqRM6EzuBG7Av0sIA_ZEMYuv8hSSQtEdiiZUZ_lOc0zcOhIyTh_cQT1w6uEP5d1yqPTmc0MMMZlSMSAv4osC6m9vxnLwYoYc7PUfXHPTo3vJRYW-vwOah_fBPV9Lbsh3rjaetluFyhrnUHre-gD4qQ19xZN0ZfJO4I3LDevDWCISrPjXHSVwFvmONU_kybu3dPC44OTZ1TarUl1hXO6iyCpBEPa_gToA5VY40tyPurko4GQ9_5r6SeDVaYlxN-NSAfqs2QF6R6SSjxCtRxRJKKFPX3MgvP6Qh7hYFhwMPwp9QWYhAUGRUYZ0cleUW7LHMAIEtZKozMDooWlezLSgCq9HzW1T9LscOUulO799ia5Ckj5125Lc8SmkK1A_kybrASIwWMRQqulAazP6iVJYb5B1WPWOa1k9_0VROCOJ8qS0G8UtD1SjOz3tZ05jzAO4z0ueKTkLlj7hPNjVzKfAflp-dcAMXKIXgEBQxvFb32Q7euOqxxG8RRTTQyd-V9HqSV5n0q8A2c4pR4LDBbhthx0AsknhX5HPPGkc_kZmzF6I8SXw_g4jqUcMt2fP6pjSMMvaMsBSTu8PTvORrw7yotx9W1DF0G98gKJ22k4c6zG0mauLKqijgeOVdtbvLFYg6yMOlDmUZhHuZfjOlh1UDEJDdwRXQVfcO0LcQ1zUW1fgGEzfAlw0eTDyDLNsRjOcUGLkvom1B7TnDC6as7NncCCSkkQwapy8Bv1NC0J6ILPUVFEKte68iyZHJz9UfZ6LeMjZnFNIAbmUBJR5v2-wrcmzzgrQoI7AFhGL8dKWS1wRRPGlA67OEYmQeLuwA5-saRnHrW7-G14cSSS1D04Fg5nbtzroYZTK_2hoJ0EL4sVYqw51sSuMhPQ-fG6JXQfN3_kWgp9","format":"openai-responses-v1","id":"rs_0bc170a603dbe4cd016a5d231699548193a942393587057582","index":15}]},"finish_reason":null,"native_finish_reason":null}]}

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

data: {"id":"gen-1784488643-ORCSMyO7U5nXSJA35rZ0","object":"chat.completion.chunk","created":1784488643,"model":"openai/gpt-5.6-sol","provider":"OpenAI","choices":[{"index":0,"delta":{"content":"","role":"assistant","reasoning":null,"reasoning_details":[{"type":"reasoning.encrypted","data":"gAAAAABqXSO7paCRSSdh-yc5bMyBqN4XBUlot8VWo_OVY-Hk09awlW15BxBbSoIuqdVZzogOY9Foy2Gx0O_Gyvpfz2bQw35isnXCIyCQbOS4weoTGdyKuIKdEZn1f_fx-AmQbzqsPgkCruPyZvfg1TL0FMP3mcoxqL86abEoKRqUjTXCk0aRcMaZHC3pJFMFhgnv5itbJ8FO6-4HSvOymEEyW_w4DGlfDUeY5wExNvf0il2gJuZiEIuQtAS_X_3PywwrV5J5_6jyjettrMSMzx-Lhq1GMsf-hCm-t6qHiYLGJ80Tuor-DloGQfaa-89HWAHVhbHqynCucdWKX3L1SS1pdD2TaEr5wxKmY7cTdOTDLCBo741fvakkM2irZ-C46xSWEJiALpRtdDyq08Gkc7O4w4xdGUNI0RIHs-yt_9hRU10S7pbYZmkpmPd8X087WpVRuA-u5nc0bN0esCcbJdtmttUBpN3Afj_2iRaWd5lOf1oENAvGFqS5eogvmuE0axOG_qI8E9tXT4CEUxFoScH5JodB2574dIXM0iPKtYLlYP8R_iQ9xSPyW5n94c9ntD2b4ISBdJegd7of9HDChZ0Nzzv097M-rN7AoEJ9eMLjNdIQGHn-5Ju6txLHc54db77qdzdFdtBf8Vh8mQ3fYz93Fwkw6zzCRP2oSJYV3eoXpKgHYlCqOY9gFnkP8Qn-FMSIuY7VMKlhJgFuknnDgi_2FO8NURE9inofTWrBhNpScEMjfb-G4dix0MooV0gDfs3KfEydaCpuXgQX0RU8_0spwUOrrd6p8tqHXtWZUvc--t9Mkd4kK5vhdQ70JLJXKwgR0zGcUAqdNtQBTNemdFmBwVfoFQhFlkzY2lyphRx861Patd93Yp2Ml4dCzLzBuCGMG24WFJhPrKwWEhMWTMOUfHqg75qAfWXv6isVEqW7DWLcBqtmbnVDUxSp01W_qVo9471elkyeD4diFGEnjdzKEryI9Ez67rCRU5saeJ3V2OBgoO1VIYCOZJdwc9WWeN1ZeSdYVPKNeERATmrk2ViETfWCG7L1rfJui4vpE7IMjzOjtFQwCGh9b0ZG3whER8gIgGSQnhASlhIztuU8-VYgbHbwMLSuWGxc39i8u9oDVwJR2j7FK-rb0hNLuR6Fm21hQLZwL2UP6wy9SXlLZACBR2OxWO6Uj8oZXAJKiPJuGVnSyKS137nHorKbItFD9xw6fBu7qUx4Te_Pcj5Yz9XqOfANaOd8_1bKVdYPo0NJWVsmaA_vrbOwRKlcqzTf1nFllySbE5OyFjVVTm4_In8UyNpNh7lTuRCzWjpvidKPY1t7LEE_YtValqp86lpPeqGcui-z3WkTuOf_PVaWgFFifNHWjahIiwThQJDkO3amkSAd7KJJR8epADTdAV4P1Cj_7OViODvYbpOR0zQg7I8PLuJLOu_4zHbc0UAPHNgVaAaWjqoQ29d4SjfNEDQJeeI6x6xru6FyAz1zjXbsgwdX2s-3bpvw8or1TsbJmAHJk6wwQRQ-Y8S3qDtYAYeWM-99mcirRbpPw4Jp77bMBa6rlKRGLNSRHOfaj0j8QnPWAJLEriNbHH5Gz7rnKNJljKe39eF8fA9g8LukEVtUCH93ziPWr5jZSLUocxiyjH6EcVbMcA07XAgYHcrwiLIFuNzWk9T7wtYPpGfWClg48Juo_kr5VebnbiavJDUU3yg44he_e3xlLitdeCBKlawRAGi5L5TcNdB4mzz6bEz8Vlt4u7KgPR2l5UU__41bQWFhPlmvFztVm72RJltyiTTTZEC8_oA7g6OpdmMyXdFXYOLpKQWeMR5IZZlQvY7NhhQ4wQEzX61meVmccANa2lt6nEkVa9EutWbZMBZxjopUGZ58l7ZCVuhY9A5XA0Jv20qYAPAqwsh9tQxAX1c0JR9Bl6x6ei9jqaT9V-Y2RdvPVbfRCcRLgxqPveimsFajdBbDxaXgaDJ6SFAg4l_cf0GSmcw3aPjouxasWNaHD-7i7hkI67WiLEncSAnInYYxoH4W2Z33iP_s4aEn-Ay9529_BYxJwUPfY8vvHp0Y2gK2x0m2N2BbHeVGSdz0XYUfNciqW70WcGbM7odPJv7aGMMG7yyHczI06lfJXiVNqTzs-PhBfIjBpA_m2zGKD3Vm87ExA_7SyzwhrvZ8CNseqxat0yi6mtjg-XLaXeNdI87wl6nGwx93ymVk7TAHU1vILp4v8H47zTfNXGbM4sQ7UikKGZX56z6eXPfucaJSGBHOfcE2bYFjTf7I8PRcTHLzs03maDT9uKie_OYbGG11wfdUFEV45aGzqK6lEwlm__u-NCS1bC9ydUmHHoj6lV_SAbtSSMKsu0EoAAa8ggZsZywczk7uCFAuZBzuTvT1f5TPzgZNnM83pSpfplUYVj4LnnEDQJsXIOZfhnYWhaLOsu5Am8-c2H9ARRROJUJ7DFoMGCUP7eLFJU-xjC-4nNTnjavwK4rP2DW86HPynNpeQhhRm2VAR090xhF51m-AAg0t4sPPi8sET09HiTLrHybsf20oCnX4M0Rf4JvwR4mSMGHYitdmZyvuVRWbIszx4jtVjfuD5uSDP28hty-XGyUNN6bsngL_6BODYGGszxDEyabzT55lxwLUJdKUcpwBPEQ6a39fgZxLSw8IeYNTjZAc52QyPwjAkPXYiyFru_m_A39dYZ3jqReRN0rkKEg-AxQcv1Z6fGcJARQG-unw9y5YMgeNH-NSnwRy_rH2im21_MGCMZJw_U1oXAWb9E9cX1cxpEgXsGPmmOoUnJKyUKYpZTPnKdE0qaodPBX9GpWgtg0VXlQPUgLI1UXtyIKFiyBD-lAQT_tzyUtvQw==","format":"openai-responses-v1","id":"rs_0bc170a603dbe4cd016a5d23b10a30819397a9233a7f82b5ed","index":32}]},"finish_reason":null,"native_finish_reason":null}]}

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

data: {"id":"gen-1784488643-ORCSMyO7U5nXSJA35rZ0","object":"chat.completion.chunk","created":1784488643,"model":"openai/gpt-5.6-sol","provider":"OpenAI","choices":[{"index":0,"delta":{"content":"","role":"assistant","reasoning":null,"reasoning_details":[{"type":"reasoning.encrypted","data":"gAAAAABqXSPPMjNaQEGk0ka5u_kyrAhewBEVa1I7x2IdDfAKmdanO2we3a9-qW_LUDb-NrvxdShSx2YedlLf8GN5QM201qruBy2cibajfAYCnFEyHl0AtPt94EMUIoEYOcPADLMz9VCFc_FFRdHO2SpArIO6iPo6reQpIUuf5lhnanVGBxLLWYQhilSE_5F9A-0GCpbHyvKccrnwjIYgj8If6h0vkzt82SZrAdAJZS34svap1xiogpeIlf_dCwdsT3PyqtrguA4yp2hG6WlcG_lVUFc9n4MAniXIpUSiNmTIev8wouTb9gLNumQoT_Vhzjp84WONurnHBV7Wlq--m2TmXzFkaIyysWTO8-xCVTWovrhgFtWXJejkHkN7SO79t8N3-rEZ8O05JEzCxlJuVwxqKUkhYBluzHpjAgAGV27YdXNBkuwUFe43Z5kzACZDp1oo7i7sPRUxDOAkMdIUcUyj4UW5pkjY6dyNTE4GgVGMEYg6ATd-XXHN0s3UfYKhOtWvQVr7031BfEhYUSecMJE2dC1JRoKyTx11lIsNeS8Z4uX80xaxcpaDXiuECwagkqpx9jx7oN37VT89Ur2fyT4tii5gRf_eof24rBSnSPsA40M6y_wEVs_psRWN01RG42OWgITc8pOSeiu2py-DFgQDnqXk2zLJokVnRb0voZlpHay5a9osoToVXBWZ3RDeDlwug12qYhfgOAFFpYrfCVQ0-alXfCsHNRHFs7n4y3AQEQg01jFD5IfksVqtH5K_JnHhnP07qoM89LaBAmJD1SniBX7wixo3TZwdpC6Yb69ZQcCN1VQHlAw-UyeC2sVwXCDGbLzvF9JjlCcjZ5gLJQPaMaGSKltA5-fXKosVQ-pgxKgg51Yv3gV9FE-c4B5ux98Dy5mH8k6Wu3MV03EbknQK9lemfYJkL9efE-ctD6fkpDu7BjIgjVVTZ6CXO2JckOS1ZPea7eUNXbRy3gsC_XZez-y-LaN5d__fFKvFLphpFYDRV5A7TGqURUPRd0X9c-HLfxn5DJ0RQBc3w-yI3Po5f0FdIT_byIkwfqrfdtZg39W2Abwxki1aB37aARgtdajzommy8xWaZna0lNzGVuwCgwn7AIyT_YzseZOIVqxd384-8_lt3O_j1LIyqBx55tsjwWsotqAHRIwEu4Lbkzb3a4Lm6wRuY7EvIgF94SC_Mjl6LJukY4f-lUsjC9cngO6Bj7idoFKLSS4zV8mA1g0KdXdS_-E_4GX8vXu1PPkqgVluDuY-hgJ-bHSGXqSBRZwGwL5hZBMa3ytl2wlFZJEja5rKbbUtBrS_CAhETs_IghTtXtQ6Qpzx_DYchOSkVGcFkRezxl43EiQ4DfF4zZyRz4TtPaXW2XWyrLh02daGotF2IvdtMTkQDxmoFyx8XsQuVLVgU6YV0e708mKi-4OESPjBuSDGMulAVr5D74LWw0vgRT9CvucJOadPR4eYhRPgQQrrKITqogCbl5TUuQC2Irbh2350A2-xeRd0cA_mQ3WFqjhM7NE-nxasOH3WEV3eaRQJExAJ25KtXmmiBNGXYjMRq-akGT2jDPWSec5vXu6xNjBO5ZqKonbIQm0eDzzNX6GMumD-CFXY6HW6wPAVf6JxZhvSi1JgdWTvHA-Qcjvq9L5EZuLJcmqAFz0pyMS6J9_uz20bvinrBPILkh9WduwwW2Kd6gfinyK4SlNp8fscEX7njQpYoZE0w4qYwNMijHzB3ljj1_mS5yaO0PfhbBLLLzBSBoUjJQrgNOAhN2Qd4lVgCp3--9iPZCd9LCc5W2mmluK09g6aecyhZdp-eGtQgI50HOtNlXClLKABz7hZJrAD7wtPi7ziaR7kPiK8Yf63g8wgQfjJlVOMm49ynkGptCSoXePekyMguZfw4_soMS1Jjlkhz_ocSKWGw6yk4cntbObpDkWvgwiQfsMFYh6xgkd1CDZtsAuqBxehxMO9GVTI5GqduvwsjE_OvhKJ2lSuDMHiCWW5fVS3FD4_fGq5yJ42q_y5aWrD_2ewv-lL_dDQSfC9k0PrMsTVEXwHD-5H01OS2a_wP_E_ONNb2przbsr_rZj1hFP4BTOm_KiD3lj9w0w474FMVfd9rGTDux_zJ89-AQ5fGEv6RE6EAIy6MaKFmYquuhEaofQIASWyTuMSjqlL0OK05lz03OVRswHbEiXM3eNBqeHYxtjoa6CnOUh2oAJxiZS5hQLVrPN7rZhLOQOBlymRhokhmuN3T5K4BU3pHEzRY3gVDzDNpy8kEapdsNgYezjyDJl_OYBTi9ICsGNm869LkrhquMbbgdmpkGGFCKUi2YFEQhkP_kfK3QEeNi-yE1YUN05T8YzSZl8TFN9RhIOKaZbVKyD65yrWQ6Rikav6wqbtwZJaufyysbQwGW7V7kE1y6fT2_Z6Cc0EoxNY51g-8TV7IvXy_8Kkf7P308NwDbiTCK93CPbASIz107HIsXOgl86XShf-Yy1l00nsV-q_B1eCC9LUh0dGa7ibZj-LLJRN4Lh_NjAjlb9e4-J8uWEEJbK2lTVtmsEqT_6NZ5GtHLbYegJ-HoUKZzZzG3QHuiwYsc_4h4hTY2TNvyaElFkn5eszL7fYDlD39wDSjbtZ-Tqwlxj_l0B5sZCQfCdI66Xq-M9OwPs86XK601YQ-05MBFv6_79zzbIpFEfDGlpnrxkKuw7HqQhsgnfxL18QqYn3SEUh14OBgXReY0HNfPhMQiOhW7fT2IW5aJXWFq5nhLbCI9xIfo0E7UCF4-WCvrZdLRiZ3-vFMMCuMOuLmZDKtPMLWzcCFh5YnGlycv2l4unmHZ1SNsIgnQPHeI4DM4X5bbHmHa7tiiguJKx7F0Ibyi1Rbe1BHEQ-xPb3wpW4yo8Q_5eGArDr5I62kRiqbrhAImEQCiWAkiGgFcvX08b3qiy5VmYQ7V20ARvRdFvCgCfyfoeopHKpU8_j1S2I3ZAFCmwvleQqGA0_v_3Sdv7LymjHDSttCPoJ9De2U9B98gbpSUnSBdt7qSD1FseSd5ZrumBRERamYVmOcsrUHcxq26Gtq-euph-2RMbxH53BAoz6Q-Rem2P1N8JBEBmdMq9ZMHBpGJmv7k92XZAOgrZBbA5QchEvRk1AU1TZ3gbKM-Vzgqw8aGiup_VyoLofDo46BD8J0FkhNVC-2ssDERqRjyA6_iMfosMpkTDZDHHCHK-mcOiBhiBASkqtXj0K8BLSYJGaa_wWxlhWoJa4BkZbEhZqE0at3PO1dy742xQAv2F3S6xKCwZULueyZJLWzQHsAhsRS9P0mK0CByilIBDrbiXKIsrWcRFlQswWiX5rc_JoiJmtTAFdqc2NU4ZiELHn5p8l5SCLRxxMW8EAI8yR-59_du0IKUNgU-Dpuu1FioVgm9FXyS8fo0tUQGxgiww586Mpu47zUbnhL6EeWzaiDyR5osgKvN3hNBxwMOfjFtoL0bfdtYw4PBdwkmwuX4SH1tzU9DqIRAlV61qMkbSvF3uKnuQLnmbuTjW8fr4T18LbDbvsrembA8ME","format":"openai-responses-v1","id":"rs_0bc170a603dbe4cd016a5d23c684a0819390fe38d8b0713b62","index":34}]},"finish_reason":null,"native_finish_reason":null}]}

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

data: {"id":"gen-1784488643-ORCSMyO7U5nXSJA35rZ0","object":"chat.completion.chunk","created":1784488643,"model":"openai/gpt-5.6-sol","provider":"OpenAI","choices":[{"index":0,"delta":{"content":"","role":"assistant","reasoning":null,"reasoning_details":[{"type":"reasoning.encrypted","data":"gAAAAABqXSRO_2__h0bvslfkS_0sM6yxn14oEqL4PMwJXkPhRvbP_pXt8NiL_hJfEYAY4Ro7bJ5cBhOm9Owal_nFk_jzUHevf6s__AjQCx7LT4F_B3tjKpL16FvVUYMpmKSng4KYBTgR6iWq71F6ZhNdORmC7id0r8zqiH2XrZ2mUEjOe9gX8I83PC98lKX1RQwYfbYFX6bIc3YZ28KjNLu17qsPciHXZOyonH80heKe-R8Cq8KbYhkJ2uMHxT3jieVb62uoKp5ylxGbzk1Souzn6dAWqUJY2qqlmfxIcEJ1WsOFlYIejISNq26D6kf_FN2uRiBGXp5bvNzToWPfb8ADl7wu-DM8p5YVBeTEsBd6NPjiISdwu51_gl0_NFwF7YnEvKmgul65L_F3aIjYwMsTp-R0J-ithsrkjJl6S7YqsPzc2ZIsBA5mmxWOUUSTrU912-nGOsL4AJREnheDqTik4cp9dTAKJ_EtYuaCG17q2UeJF3GqYosdQnecVSHw59Edm7UrREgswuvQktRrDYdbM9vOTua4riaAgq2O8MRwIH880uIrbX4ejKB5hTnHgmKmI944aHnDEomJGrYWsTXfRH6dIAbHUYmtfIjJhg1pXiC4YXgC2c-3z4zfXv6y79PQrdpF__9-5Cnga1vmU33LuhsDSzOJi5hTrg2_QFxDTxTBiOd8CHUO7wjrPoEwFg82mb-atwo0I4uh821DsR2SYgCwwcKuOzV5uuVBSUxxzr8TiPjX2DM3fdVY742uhNuKG0TW2Ws9XA8rLXdx_xTVs6iCB_1lKn1pgEKNcaWuYgjTYJ_84uxo3H3EGsFnpKd_VfZc-kMqY7nu332b31jUJk4VEwtYB1B6u4lAjw1BpcjHS-k4A3r0HGcNjCvK-fYkH4vSxZtwgZ9ilQZiuFzA3oLjZ3HvCx-S-n4XQgr_aBvVhTsjHGcv6Lk-PH60r2zayRBXKtIy2GwexQuUUUOxeEjklc_Y0gHJCRV2c7mYE4zWt4lMNpzLEhtv30c52mLJJlo22VGzEVt-t8T8IVo2hhlkIhfmNb_sz4D7ou1sfgiFtgfqqziQVp6yzKkdkowz6gaACX-fqisAdN2eHdUVgHa2mw5aOqiQsFuFReN93IobgvADxxrsd4coO56oPcadZ20g18RztRUabSKp2L7Elf7_sq4nX74mJXwRs4z8MAlvO1GwUvO3iNUoNLdbO8ZA9lnELiPLHEl5--WVdFTVmA_HC31jgt2NbtC99vZLI1HRXbxEhh0gyV8b-TooOs1xtslfPHHj_19aDPvquHgsNJ8ol06QSzZUJ2KSNZkxY46wQ4q4U3SHYH2ljgr-O-_OUXeEXYm4jlTV4acFhYX3j8WfciXS3f7jEQZUpgVdWoRg07T_0DcfcPAQ1zr6A9C_ul4SFhY8gEHxT0GNAxDSqskqrszoa-anyVOwmSp8P0y9T0JbUaa5mSzZNn7BV9DI_uXRn4BcDUQ2xbui7c6-zsG0mWKqSLmCxhjrHjAMTso6trp0mmV3AlcI_fbE-4_KUuEK8_crnhp_x5cyoUvO-Gt1dUFUgDmzq6S-AzZ7XGmbIb36ZkuTtJVSjxXbJQ1pVQhaWGoHJg7tlPx5l-Pr3vk-Yub1SJHIXwUwhvecd9RA5byi1KCrasFRdEhmgL24NM1DZWL0wYniLdzjavEUFMMa_QjtEFeGEJCaHv-5VyPDFE089nc5HaAGbXhbseaMofo3pIjIqsfG1Tc0b7ucX4T5Da-rQN40mcwYUVNQlYEdj-kncoZVoBc97kYzr0L5hvfg5FMBlbLytLy0i9wi2wSvqaER1fSxoGDdCLoAKIwaKvV-OVFDbQpQYxt1YwKfbMYnPbBtcPJ6pog8DU6UDklRMSoAbNeJ0no-Ka-kSR-zk7Jyqr62fm5mosiFgiF8DuUXAl2QkzbhkgM230l6kl5-20vr8RshADc1EERXYo0CJLAwJb-sg4UIpdWeH83XxIt6rdwGc76iYELVOPJgZ5EifWZErTa4sRgcSyCGCT7NGxbQl6vIGgwwx9wFaVExsIrI1VUdmmbbTNAGDGLevf0MQxXu7XwAZuCDhkxZNUxoIpAriKS6Lhjet8DSfTU4CVitl-fo2eZEhez0qJero0AmxEcDTpiPYg6gTjdvRgT1zxEfi_NH0c4wIYMMTwvjiCyBWCtPP43haIn9FBhWxrrpKT5XTZmCQA0t-d56hECH7vmakFvun6-Dv5tE2u_Y5-UdRe9cCBpX8YOb-xIgpRkTg89CnOu1YhcF-ePcEV1U0MbvORvRTWWShY68v-3zQ4hC-W9IK3P7KEwaZzILc43yH74Zu9K65wd5uenDtJK4Vm6Nt3xtYrVlN75mUgCqDjB4akunVsXeYnhHAMHH8uq548HuUM3MaEmmZbOSFZXTbU_3jryMHzu6SozAprXbs3WqlC49aAEMk3h9S223fs6VOBH65QrXNY0Cu98ba6EbwZVN1KEQZDVHknlwEl4-_BZvLv46dwSjDgsw4T79doGq1HGaYkUzmNTjdFp1WGyv8uE3vLNcl9v9c_bh_MkhQpeTH1FJ4z__0ODptuZ9kKMnGK6Gdel9gEmDiXSPVJQmnfKNCHyEfdNbJ5p_1e4DvUdytpRBIe_bP1ZPWKsAnx0MG5pRR-gcWPXP4pPMw2GdVjnn_fQr2IWtxXVbUtWB7rLqu88betR2BXFbzfm53hXzq-TKlYGNS_YWffwAWaI_2sRSGqC63Fp24lJxAAHikk5stzGkN0g_Tqn00ZhsHaL-Gl-odovZVM-p9NHX7S5DD24nEi46VcrVhIK_Q74zKzprlBu2YPXKMw_YQ_8CalhMfA8_OzA2KfPK_bdg6Nz2jUW4D3aWZs6B0YvZq7gVgadYR1WmSlULzeeEpJVJ5hmgU8u18qTpDuY608J4Ug9quJ-JvlxH8mPkLE6TviLndNE2UNcxydDGzp1iit8UtU7VKiI_TSeqUDV60-HxgmS4OcKh56Gm4LCReWR2QAEHa20qLuQneg8hQQjr4H1f0aj-tSwhr1sCwrEjycPtQcvrKRrPQtjFVEetW38x6O2qycg_0On5xCOh39f6ewvnyRdu8YcdVaZMXqmPAY7EeKhtHTFIruxZCwXJoogyoumcNZgxrVPx0jLqY_xZ2SVK9JTyAHeWV3maYc8Y0rhQr_x23l1i1CJe7GsxhzSi-nwQioEs5Oy7xViZSc3eYiTHGPJFDfD7C1W6VivAnUTwLIOhu6Gldtp5qx0FPkOL7Os86LVhqJVDO-G9C1dCfhzkvFQm2Rc3kM8qISRAOyEL5FoGIie38YlFwWPJFb9_YyV__yrWpdUNAyWGBJkAimILcQhOO9ivfjnvFOKMuy09aeV6-H-NeO5K_9TqhCaCJUReMMA7j8NGrdJtETEHW45hBDkuUYQSadDBoMplGoHxyHEj24Qn7NppOUmR51zuPPpvrEHII1d5mlpXe1LzWQ47zN5UUD3dlL6n_LU2iKL0n0J3VgiYDhT-Bwx26yisidb3YehizUNVeuvYCUglESO1ePL-PRasBs8PUVLcjeEjt2l_Jv3KthBid_NxXHBJB5lV5Qq4FSX6A9x0ekvsZIx59Be2Mgi-YAjYf8K71BlJeQmQUly2BUhLZn3D-ugBsLwSY5H8zfbAwf6-_09KhMYpB1PiUj7hx_V4eF4jqGxFR1VccQR0O0RR6s6ddIRJGerLcITGfcUG1OrX4nNhQsSYJksdFo14g05Vi2gFNMRUs1naZTRiVcYZC7dTo_NORE3z7Vh-8VVx","format":"openai-responses-v1","id":"rs_0bc170a603dbe4cd016a5d2445c42c819393411f135448c0cc","index":51}]},"finish_reason":null,"native_finish_reason":null}]}

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

data: {"id":"gen-1784488643-ORCSMyO7U5nXSJA35rZ0","object":"chat.completion.chunk","created":1784488643,"model":"openai/gpt-5.6-sol","provider":"OpenAI","choices":[{"index":0,"delta":{"content":"","role":"assistant","reasoning":null,"reasoning_details":[{"type":"reasoning.encrypted","data":"gAAAAABqXSSOQERHdZj6flf2L2QYO5qpgeTbQsWRG3SeWo4qYIW15x9Sg1BO3VyvPVZ3cLWPYyg68K2XO2tl7scy5Pw4C4VvCpG6QcnlKbCw6-tEzZSHl31fi3I9SwDn0q4pLn0PwbO6hdTZMVYBKhI5C3uJ2KIqV40OTz5BGreaHroKjbEZltia8AQcPEuA7AxofVScvRCP1DG-k-kqNms7qiUGiY0sNIs907LEu-BRJzrUOZT072eQA6vtAMwxP-piozZPY5UpU3lLTaBOvV16bEVspLJ6H-nZDEob2ltYJc8FPqRrwdTDdlZ_GnmF4SphrAh5wQjm0Exw2GWbMdT8p-yIuJrc-4PblwYY9RO7agl4kieS6hfAYN_QnJPBHLJbc4eaD0kEtagmWplIkeGfVbrLso3xZwE4Xyal4bdGywpy2vwjn8bCBG56qKg91JOhRDA2NWHKoMW2EqaeoXilVsjhPmJcMZBn8f-OVviBShNHhyTkaf8ZaQeAHYT_an-WzS7YKwkGlt73DvDrK-q0KnUNCUKi9AJn-LAs1RhPx1DBK7wpRFRVbmu8QZFmqU44xHegmptiSAbMOavR0HFLg5hzkTex6zHWCiffYXKPgQSEMPfxqVt2yfFv0HzUuvv5D4xwmIUyJjxYHuImSnBpBQ5dKhrLEOjNfc9FCRUFuZVaW9p7Gwk3qxvMr68w2TUN7AKexPPKYsMu3Xir7-CZvzPl598zmfunwnF8wOiuTtv264xL4whXv2GBIoYTZtEFqSyCljIIw5HDht-xQD3YgRovjT5KaaMAnTAAgyh7n-e2BVeZA0JzsBBtsxTRuUIUFypuHzU2aTa45GaH0bG-uCnNkjTvljmhD542JEa_f1kW2akjxOIUoUZ8dRhyb-c5nxUAE53BFKYSBrWfBnyBOt6KRn7dIgC1gH_FOWQO9l42056R0aXF2gS6NH55xgkOeisjNtwSYjBQfsDfrJhL_jafdGNMGGzR9XEiRAXg4nia9JkKpnR-BJPpuLPTpqfCXtW0oF0zcuegOdVxJwpNkClGzUKIlmbJuDMmwGc0zey6VkKBLSH7cDTgKFBWIUYLqk8QoCsxnAUsyZVDqT0mOVulc--NYGFz_rCa9GzqVmRPQUFJ2V6rxYRAE_AE47DHbDcqVPuioO1IJlz9iG_5hDiXKdwLOKkIvGT9ryYeEx0WHoz9PRgbjWKo4qUZ1na92QQG6OVlfKjtn6CXalcgBQnyJwQucLy_deFPoE5WwwWk2f-KKdEvjQdmnXN5aabKIp2tQozS3qtbJZclcVHZddeJ-o-U4HFsz_1-l9m3_mqx9pqo8QDidGQwrEcIdp-y9QYwc29rLuq8QJDng4l5wsD1BxQEMdkWnjodNY8Y1iMMs9oarD4-KzGeXHWly-QXVc3S4ToCFVGkvhLYIAc9KH2rMJ0jHLPGiiCO8kQTyX51NQFlPSDMgM6yJ-RaMb9Ea2y8oWjNqXeXe5gI8I-MiZ1rvbIrmOXm_Voi5sw91EFx-9LaV0tyzyw5SCvPHfMzb1q420r_odVo1lgj8boLZAX460eNKyabRiJw8blyiivTd7CmyhEZwRGPug9An394kF9eIi7aZpz16oNPPZU7KVMFLO2dlEQsWgm1vaGa5Hpk1Lnt_IQeNtEqqH6xCS3q_uBFV0xC9eSdqMsTJyArPo9LIqYDmL60YFA5W4ID56dF5pOOAegV1pVEAZxN99yfofHa1rtVbO_ADalVyNr_A5ubdfb7o9f1w94DYm_UqSuPY2tnFsa6I4DL00Kmv6PE4b8s041DIg-rTAsqQyM9dbCj9ftfG4rFGK1xevkP0KD4fb4ZjTIZAYciRpcikfg_h7y0-jCQ-iTmfcR0FD5B0RqtPE1r8jYyXrK0gClwO_P7IEDLJmjPq2ydYnX5OiVlYkTIbi_aL6S1xqvX6BSFgAbWk1RrY4SS7UlGmTbGd3rF4i6JaQxuoKnz4u2eAjcrdpY43NQblhFE2GoYB2K7GQsTwW-jjfg0u8Z6qhi34xxlGv-Re3A9TfCFgucARMkXOL0285EE3gF6P5fB_zvPIbCVpi9AjUqxIlRMO4BpGB2h455reHpaCcC-XuUShL-8PDTxOsGoK_5fDenx-bVLvziCXg6YBSnjCosgJ1Xn6a50KwFMNFvS3x3430UiZZkLJNRcwzxl2_KGAzsUsRTbAEqTDai83s6L96DiUEfsDfz2doFMdYzLlNDvnKfW8VxkOuBG0SXZkpMde_zUEiWWaRa5uVc5tiW7F10A2pQ_XvLEidVlolHbYB5Ta2RG1aAjhH6ax42FJrfoqOejT8mpgbJkas_Lgom9DiGdh-4hmesqoc6OQs7OoISs1RwWHfP_12-d1i3SAkNH5cnVSr6cRB1meIrJAvfgt6vv1mZT4DJaYYfkdv915dCVa91PhFJO8baOeNK_m5o8GSdBL0kCgP_1-zg1udxhSSGXSTNsiVPTj7XTXZ-AYoajQI2oHMeJaIngSYNtMvbeOC6KXwrdOQ2fCtGhJWiYT1xkJ2Xe3Wg6UnkIH-VeveX9gIKAermTAHGt75WHJdJZYyaaUXvB2ePwJHP0ku50CrvpAveApDJvolCTkLpDzybkfeqNCHpK_hgavwS5oHfXp-0UxU8O1bZOtAvz30iOlFkp_aQwR0CdlEosei-IvoPA5b5mg_BTJd_8t75HAkD7bWGEX2KmyXc7aJ16P6AxI8TyMFVM8IgY6fqGaJs__33mnu1SW3rtrZ6XcAfXcI9jDBZqPNDctWkgtkreitgG5dw6DgMMtIcdPOPbtAUCSy9znD8T-RefJGqEQMRAISJ-_qN9r_uMJB6jMzGwPPHSb_vd-Jr0VKZFdlzMfevtB4gzotUInAprtUGtwvPxLspG6xkY6gL2Gotwr7GDbwZ7vK-4ReGQnNWvAQ0KGU8yiS1onq9C4HdNIXEYDSxkQf-CCwiP6gybPEKYjV7konCKm4MzyAWmu26GhED5i8BPsT1y41tU5on-UUX68eNM17SK0fX9RBox3cqtv1wij3aaOC64dCmMDVsHpJxsdq8os7M21gNHB8iCMBFWNKOyD56yowZ4l5qGeUkiSMbLAegNlpCNLW1opxUWsIcETQgeycWHoEIXd5hIwXMtwq2VXh9bzVGZT2r131xfPfKPHmwcE9pg__bPj252ajXiAB6Z401701NyOqAszD8Z-xwRa9bvKR9FoqavZ4gKdMmuf5WtltAy6MFWaNwee1SuVJOb8OzEXEe6O4N2Q6fNnCseusndytCPtnCpurAxLEtAD3OWoFVW0ogkMHlphXcz9ynr0arx01Am2QPjZqghTiJnZzZbySPr57mUim20Vu5_3pPtz3x7wpP3y5_yPuHUXINKXUVLD7BPJFw4Dn13fUrnIkBQ_AXr2HwFPsLKP10O3N7u47IYR23PA0jVpkCUhhSAWrK2Tgv1Ln0Y7DR8Z77v35dh1s1rTwf12TOl0wx3otMK0niMZ_04EOeBMODU2wnyl7qmR4lRR3RE6JLFAQ1MtqJSeAyz9ggVBwY70ybC0gANcGAoeDF3PaarSK41it0rTQz332rGe4OX-KwUsAl8ox8SP5aCNlQOTqt_46TuPIwCunAzytnf0z80C6t9mw-kSH18Fpe4CNszmGcmEAmmZTen1DbDAY7WIt0VGWtvmw==","format":"openai-responses-v1","id":"rs_0bc170a603dbe4cd016a5d2482668481939617a4e1a86be2d4","index":58}]},"finish_reason":null,"native_finish_reason":null}]}

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

data: {"id":"gen-1784488643-ORCSMyO7U5nXSJA35rZ0","object":"chat.completion.chunk","created":1784488643,"model":"openai/gpt-5.6-sol","provider":"OpenAI","choices":[{"index":0,"delta":{"content":"","role":"assistant","reasoning":null,"reasoning_details":[{"type":"reasoning.encrypted","data":"gAAAAABqXSSk7r2fzdKCFf83yTHEGJV6ZG5JM1R__HiNaqW0KP4f2hR3kaAjNGSh4szCpV02_kMVAuz2KGI7-PN-Fgsg5bwBiHen36TSkXguA6ud9mFK08Xukoud-HGVr95_XN2j19wg7rIMNMoZSRdCXvx-FKw8xyw4H7Ud6I9FJXoo1_vYGuHAEUCbTO8mcbvEYHgse8lEYn2t_1abe3UNTsUp5zyRLN7elhjeeUIpM-rPxzceUbcCIBafWQhLOZUk-FN2cZLVNkJuEFMTeiORHNbnWO1AdhGR6IIKO5VtjXP4Ut-s8-VLHaRPFb1CykhgjnHE7pk6wtyKgCIae58heu2Wc1QRAwSqseB9s8GV831PG_BhGvT9PbSSnB4svyGbOgcs_EiJDi29oZTnxBw5uO1fzVBkDOEgVbyRI1mHfIY0rnqqafbOHkojCIOrBwH2NP1cS3y1C3CWaPTlPjX8OY2T6G0D_ZrmUdPfxZ3Iv0k0Xgjy5vH6fiG2LLPwOPAiJ0sdEzb_rm9Adx1_wqdZIylYKFhxn-pZcu6eW7hGNzmip6BV8l4KcQAXeA1N2QC9g2VOndVWRD-9cFz2SE-0ktB2erN0WZ16n8bJZzUg77Nr1JzsRTycMrSYnrthT9f9opfH1KJEEBjvnJ1zqLccRIJl-7MOj4vMvGXrC_VkuCO3tLp0ZbBZk09bqoz1zUvQ7TMo830OM9SdRwE_d8lx7sWzmnAUGXMFop8xRAsymFafuhjEjsIkWtEvr8uamnhezwb1V-0xWh6FhvLwPZvyUTqSXKOZCu0B68ejWx8VmSZ7GTgq6hVOZ7-lUzEx0Vc90Z6j89goiYNcdhJSooOyMFqKGUXbVvsuDlx3hP2E1TUmP2VLAA7ISNEb9IzGJ1IXdRuOwQGFiFrPJ6eUUjzGZRDAH4WpBBkcGRUtDgm5s48smiCGapeADBU3abqT1jwx0WD-WAny9zrJZU69t9UZ5NE1Ue5BO5Fb_W8ONCUlntvJsaLacjcsDYVcE77rs-9BFpZxVypEYHStX4aOMZYwI7ejbJt9uzHxCQQXRc7ACkNNpRwWBH6U5OET4-VoTF7U-cSRAg8lM2p3wK7qvPjD5rCaO36eDKA9YQ_ipE4pTRDFIv9ZpBmv7erZ18zyhQEopPfE3rQAqnbCH3iuC0eQwjWzheHYZ4F9eQqb4GK5CBqRpJOcjGMKfrunGzQnVqM9eSkd0JB_hNK2L_Ni4LSpQCKpcsapIwTwNkeIDIzswWZaORZ6ptXTa-Vz81CIYlmwQihcN1JMdV9SuNuWSqFPTiLBMbEa4-o_N6e8AFN3O58FcfF1j60rklmrKGLSCmivtelFyXqc9-IeQuwtfsov_7vJ6DzV3431nZbMraSk2MhRrMxMJhw_ecW6gAhKWBmwkq5gp8RIJgT9ref8WhecBPNGBFwX3sNf6Qa0x_V2WeemaQiHgE_puuEohmcFK6RlVwUaThScxOlTLahMHU0Sm61ZqCPSfY6TzOZzTbAFors-vqW4BlU0iqjtym_cfe1s_22eGN-2apVOHYaqKxYfK2WZbel5QpkhAHqOJH1RER2V5lZ-IpWmT3JwWBVVLYIdEnd4fa0Zj3-NEcGliJFQhZuzslHsoLaP7ly_0DOgyvzDEsdBUj9nOMM30A51nDFuLE0CYmbofZ4ssfFpB1ppd_Wh5XciELSgu5AShUhfqjzGrdhmvC0kbH2o0n3TISFlLx-fbiPWsYnD41I6JKrZ0M7-aSqnlyq4cS5_7EfKBz7NRraWdPVtWtNXm1FHAahT8d9wqz6Hrv-ZbbPTYaSCI6IEy9bw-nKlSHVwwnfWGoh7M3AjiXE-gVBe9FXAg-fv2K5fVogp2LKIGIOjatW0WIEJ8T3Rw_poaGyVXBjGY3xKlmZTKpuGvrcsILqxLhMM1dfyLXkh6plMAH2-jEVLDOIvN4wDWZF67mglo4XcOnA9iodge0kpxtWZoy4fBRA4DJit_fYtW3jlWexjB8hB-1SMAoiqisx9DAN2jiVO8O0F87k1hOhyEq1fGQwWJ-TT-XryAgz-5YsAgGWK_YFdteiTH_dtQf_rORqHtfkgctkDEPmMMgg8cnejqNjWLySHDthPnkYIuaBxtFS66Mitabn1JurnxFZqhEWXJSOZ9K6gR1ztcVZwQqXpTPriZe_4ggvJJHGBsMQ23lZrxYgYOWa0GDTQmQthNCr0Rc8h0dDzAZBTUQmxkAyZamKsdi4Rp5QKL1WQ41AjYthfh3D9yKFawf5z11Q6alx4t1JDBA_y0V0_q8zkW__BgWLMgDVb2fu3xeLmWSde9fGBJ6c1lAOvnltnz9PYTR8iELHc7F7sDB8W4rgaadjtDGpAKh0jb_agLVdaodGVreNPeyispFXa-oXz_uRHIDG_ArS7JPHyJzdAhi2othoUZ_IsYzKgXg2GMRp2vcDWhFMHdQFsimJjJudLlMiT388ZZOPuPiBJSU_JK0TrXQXOmeia1Pcy5JQt4gBJ3Ot7acgcJ2J4NMkT0QcZTBpHYMlv1MbB9LkS6ncr0GEbE3iAW4zfsikdduyTg7X1nD-o9PGhOAdddjRPGFS4DGmv_3wjI4jpfOnfbOSdvSUi1eo-2JLM7WFb86_6T4nhVq1fy_Pn6ptzjKUbHLbum21eiV_IiexlRsaFzQjB090zyqvf6TvLItg5gPJ0i5Fh4_3z7i_f-LrN3tz2ilGtcsvNyoNPQyXBxQuiTKVHZU9WPaGSI8Cr7sBAjYah3Rp-xO54u-HpMwbaNtOhanfDP65fcXouzHdvNCL7QI1qFTUCpgRG4fdUYELdrlMwc7xsjXnlv5eJFa0HZ20Hb7vx4zRaDpG8YQTDVpt5bGi-DxCd9tV5txSLnmpEnpCSp1ULso8g74G4zJiotqOCKEdPh1IqVbTlRqFyVS3cTbiMr0oYSkf_J4ctxIkCA-XNEqwIRyXwlzYOp2fNFZAYcNxDLp8qnCEUv1Nsq0MqZCvGx7Iy9GVWdvNOhzsHs9tWf3fYlg8kFU0yZd5m8qFBQqSg30uZki8BN2KtdcqjTBkJslWrHp2AIeukIivU3oJBPxcOTo0YnTBJvcj9oeGeLSX_HCiiaDt01I01XXUgpSJMWDEA-CnS_jy5GKTwZJYSg2FHHFIQ88m5Gmu3Msn9Mbr5DFXO4qSs8y-tFs4thUuTT7kYYNCkbFmGTDvagnNXFHv-MDb5CIh1DRTjTmBOm2U1Rpx4jrg4GgO77ikzaWXMr-T7v2orTybu8HRFMlUgmMmexaHbMTpmoROG9chB_btitg0xPy52ykF2u1d8lKP_af30MJz2voHKVx0xLd_Nj2rk9L-ndX-PaEH4oYbtTigHiM7urt-NCMdb5EZR-diBJLsEXYpsNUOWkZkEppfOiVWO4zu9ekE2AkqilFqoS7RnpSDLTFdnMdy_7OkHHlajQl9ucynwfP2P2yqU_6CVrPU_Xb5U_-1qL_yRyPxbTipXZ_RL_yVU3Iy_XLwIQ3zyAMkBq348q_9xg1Cb5yOcZ9IX","format":"openai-responses-v1","id":"rs_0bc170a603dbe4cd016a5d2499b4f08193a368de5708b7f694","index":60}]},"finish_reason":null,"native_finish_reason":null}]}

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

data: {"id":"gen-1784488643-ORCSMyO7U5nXSJA35rZ0","object":"chat.completion.chunk","created":1784488643,"model":"openai/gpt-5.6-sol","provider":"OpenAI","choices":[{"index":0,"delta":{"content":"","role":"assistant","reasoning":null,"reasoning_details":[{"type":"reasoning.encrypted","data":"gAAAAABqXSSuf33d6KLqR-S59INvXMXc7PWfiM3cQtHiuHBaBToysG62Ow8UQgIp4jGdxLSPMsPV4yFqzlsN3hBDUWQDd201nPpn1SNWVlouv_ySqvX6z9kBHy_XVBC4cNTmtQkiQXC8g88recXGt-C--x2FbOtWZBXWM3plrxzJD_uOBCsgrY_nzXqnVO4wLnV3DJNXpZECcuQQqhGBwj9eyzehFA-_qMkT9OZ9HmyrWhy0O9lKZ0iqdW0L8mNrbvC6cznUcyR_UqVeZ39SV2EKKsvxJEBtf_IiT0w7VyyoQ9UkH-LA4C1M00YkBGJYAgoKlf5pFeZPQRaso_B4YSr597kzHokySvlMM6JfM6xkp35VVOM7r6siEXAX7VC-dRsjRogvNR51zhNwX_EjSjP_s2c-Y14a9Y_qxTw4XcMBNXtWALOJNwm9AZQRrjUIcXuagsZ81smhkmo4u0QIGIeWYbwLMgeSUdOJcsRfYzAdp9oIKJLyNUO92tBXzL4g1qXoPNwrJ1xCf0FwJ4q9wAFMYCQojQemD6ZGmPWHh33XnWPVvRzsBqKowe65Ig3e2MbDzkQM6IMsBHYy_MteTzVSNf15VSmwHVy-1DXTaxO4bgSd2rvKuV0RolIB4RkA0qTdxuHGWSS-y5-f0u05SwP94836ZpvUAQYn-HezYG-KR6RBbfHbQW1moViBFuRWlsGEvwx17gIqOUYs8MIUojVJFK2b6heTql6lA9QyqPeJnQZLaz6092ZEOztk3o5SXbhmJRmSH28HJMhoUcuP2dsAHhimuY1mcK9pqHn4v8nB1nVVxK9LNGSlB3j9LB_fqy9XoyFm9XwS9BbAh35wPZc8v4aTnZ16b61JMpfwsvWJCZIZuwCdvISz-6mS2qatfYhR41IViB2DY77upuuMLC5MyZpE-nan5sFfNIuemr8A5EDURO92FD5k3DZN0BPB-sDtg0wIPKbGjFKgD-ecn_YJtSwmHHuyzOjulVQ65JOqZ6KiFeuANd7xMoorfKS40YD5tcm5bzA7NPhhRc2PyCzLatHB6APhiPZa4zHPxZwdgcXGMZkU1iyLLCfZQEUVaM75szCo5LVPQp-fEia8l5hvYbcn2-iOezWwySUmj7Io9hNiAeXEKKVgbtHFu59KSU51BtB7HLxS3MmL819RJnICbixz_ELlE-MFD58dx_VbZkYFKH_LmNxE8bkzH7WNKBT5z7WS0yJ2--3bw_g52zH2QjkuxiO4l99LYctHHtEkDo-R8sZxQjXWBtccv4jgmMT7H-x3-AMEydyRCTQcWzvQnyrS6oMhyxTLe8ydNtfhcyu0449owGYP3GCH0opC4KiBejxiHzfu71VQPn7HhLamCwII3cxTvsOn29Tza6Qu7az8by0aD9aQEaIq3RItw16Q9RcaiiiMIsOKySconJEng9Wc3KxyOWuEjtmb19ISLOfJ87YWlId5To7VUcNuP9kw4jy4FRRNgtwgdgYKOBusaz3l1OyLjg6auhcCgZYqxqtf36ig2qj0MipMa5WyTRGszhkNphoiv5uO2mzly8jppviJc364Of6kJjrdEQHI_7umTn8dypsIyMt39ia3qaiXnIoh_M61kgY5K0xZ_OXgsHgQBv_RgE1ZvPn800CC_RUN92jsK_0oNPP0e29Q3n48Y3wm9IpjNo1OhtSYQAQuwjAAgzSwGPJ_8grq8naaTy7hg44PGA3Ao35j3oELNvXiO4xbSWgdc8_gWjhEcW_k1p0HkmNGJWq-SG0ckLakWEHrmXMD8DQcQNYO16sqZQebrZJ38Sd4Ncq-OfJOhDZkx8pQ0cmq7z7a4B0KanK4_tmOgXi9l0XYX4h4yU0TY8TJ0UnVKgKxHFWzR0-o3QU98nZZ-zeUiB57hbRbtLO2BRvpwxjotvSPcj4FCw_NsaEM67gepgueMPxZPjwtpzo8GNz6Dkl5HPZhnL7WEsSikNkCGVoHQC2qu2_LEzXUMclSIbAgKP55fGx4OiS_LQYsQuYBw12sCcxwN-l5wNA0EAOp9PcujDTvHB3t1WshkzINHqF7ohj-SI4R9M9T15ft3CzQWtPC2EYixTU5hTg99eYUeRc-6b6X_t6X4mYxGFj-CZAKSJ5SpBEeZH8iumUKsw2tvKG0FzT1wyBwazyzi6aPKxUbgUa6rLeh7a2pfcuNOwC72yLoPS3fv39lGb7DmwQyCt8Y0wFiZrc2f7uG3lIQExsyRQXEjsTnboO7BkDrlZaF51J830zyiYQ1Xacoq83mtOq5SknGAMP_5S7TYvBp-1FcQhsGAM3bugcIHOjsVhKili0eyOKxY5Hfb0AlrOv5YCe_ktYBJYFI_nFN__w69WAUCikfSBpvp2xayIlOm4EXxeyWoJ02U0EK9Ya0DhQrsp-DW3ewKYJcMuetYPEh1aPvIOcvwBkgEzHFxvoMzkWbwg713Gt6O3zTuDtc6RkeG3zdO9KKDc7bQL04s3JUDxUwQtyGYAy9z_MbbGmoupDVF8L_6kw8wA4Hmphom_l9362226sp_zZCALJCjbF3xndETmxB6kE6kn8MEcrYhi47KusLqOYablWs1CIDyPaUdgr841eUHtNJ-Mty1sCVDD4KPKd7C8ZKhqNV_82-W-N61zJwoK44BVxAqS3UPPZZmFQxaXI6fFJ7rX981tiwTFvgWSIJHizXGCzcN7BL7QuVJjdAaWSynFzr_W9xKf6RANmpQ6aH4DD844hiUmsel_nk2RXmYCrriSI8Po9mMQYJXwYGc7oUA8ZhZZ_FAhRDUqYVW-wk7TzuLorQ7hsc6CPMqBV9VYWns_MAFMQiRaLVglp4uSYkvANcJ4E_aAmq8iAbq6aFSP8fBDeTdsvbHwHPiqbuxXRccQtFTDHgfXBJTpjk3aOP6RQMiTEUq7H1TsauAfWDyCMtd6D4uKp7voObuFTC2VigvRnEsw35V8RjhxLAgzVfegOpRcTwyz_rZ0GtjJc7sj8SqVjSBozgyncj7h8EH2L0mbz9p3gm_gZAT3gEMcNZOKf4psPsYddv2IB12CATHWix_BxTZHEaEkccKr7GjAcQ5-_tpfXJAF2tOBE9YcEfC0a1oFe-GoPjuYWYrhL1U1wdeh4IrSYboLMxDGgqhbTKGrkeu9pNWVT7AgfoyS3S04KndTugAb85nmNC31tJ_zNJgUoc_wbV_CMrKXJvoqDylZvHqxAYNwKimVpqpu4eEOIWOi9m9ll4c7pL_GPZlSNYa0n0hcpRmHUeE-EU56BUw5WXSllBo30mHowrWzNXN6JI-2UzXXwxZCbzr2hZpXrWc9TiBZPS9PNZVklq0Fssb7a9pVWRSK1ivGB-YbRMShcMg96prHiWSjTiRsMWzaubRRav3mje61ty1aItEpUPDjVjc0CDqScb36IYNRyvz76azwkVmIDVCGq6rYVF_uNdXf08ZMTsZ_0zut0VLqRLnqV233MjEh8G74qWtP-F__7WtBb31vXeklhFxkj8J8fnOEznb4mMYDFKfQSr9aDaazK2tSvtewJK_vOeYS0P-hO57Rp1x4e-lh7LdpFyuYpjKyshUF1kMwMJ2cNA6-aD9CygyFXTnhI4QdDr8U8UNBamcF7HNpxIFVu28VizYgZ03TrhzNs-41Dba0UUI7UVLjl4Ndnj7st6Y7op645YJt6nGTjAgkYuBJVMqkYSIh0pomuKZrh0585z-tXajb7Gyhi4Dc_wyLVFFTc5I8PWMkgXgJ9wCIKkiWRSie3T_WI4fXGGZQZg2qc6PzONFFTWNbCBwB9qNE7RJ79ReZnCQc4W8G62IhYoyXSSbv0ZuBLdgn6GMsO1tOx7kpnRVpmYJMOVo-Rokya_jKxx","format":"openai-responses-v1","id":"rs_0bc170a603dbe4cd016a5d24a48b148193859314e25d4ae73a","index":61}]},"finish_reason":null,"native_finish_reason":null}]}

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

data: {"id":"gen-1784488643-ORCSMyO7U5nXSJA35rZ0","object":"chat.completion.chunk","created":1784488643,"model":"openai/gpt-5.6-sol","provider":"OpenAI","choices":[{"index":0,"delta":{"content":"","role":"assistant","reasoning":null,"reasoning_details":[{"type":"reasoning.encrypted","data":"gAAAAABqXSTxpKM9x79UCzcKTcqoDdWpU0Nk63NfrtDPhMJ0DkY2OtQwsPtivvdwXvAPMbGHHcWQOPA-Gz0dkkGwjft17nlV0ny5LV_1ZK4sY9Rt1njpqnsVt0MCdfPrSfv8ZqJccXVuLR_sTMe_diG5aDQovJR90PT9FiSHgL4lsc9UKycBtJdfexnSZ5z_8IVre7T9_YlQbI3G7khZrFh_-mMNkXYPlRiBsv06NdvGK2b0ISDw4t6npjoxBB4S5LI-AqNHJ82-4FMi6iRt-Cutob983fXOq438UoUYfC8kGuZlaoPhh6xnX4HlU8JwLDd-jVKGdaZ2UTLsCklGrF5OFmLIwTCFDKc3ek_MCD73viyBOgzDMXeEPjXxPXQRY7midji9CXp0vT2NUXssVVq7yTeUNueIH_A4XqDfB81jXMgKxI1RiO9hu5Jlmu5sg1nrEnLhqktM6CSay1XOHY7zDN-lTWf86OdwAmt2_OWnKxwVn47SR4FqcMT59kiGZCs1Vkzu97rkk39R7XaGho4Oy4puNtfLGab2s6Dkf0E0boUsTp84OAE5FJwXruN3lC3xh8cF44E9wSdnKkVT7YPYIuImK9yJQ6IgzNkuIyFiq9MKgEodPANm6mBBh8lFEOf7d8ZCdMoYS7zgGbjH6SRdgDEFkIgsl0eZDewINwk5Wgk7U6mYfmEd8j1riN7iQUKQwL9NHWQe9ic89ZWvzzfV10lRu9QWtyweZbBVqeHeboCXavm8e9ugt1qYXSU-NgR323_HqflG39XbCJyqm_nqzzkfCYapH08G43xoljpbh3p-dwkw-n6kzMXgGlTiGIQCdWkgBeAc0ynotVi6Q_NZeOG8eggCR-_608NU3x_VREijQsiL7ehx4_mVUTATQoMHy9LEq_RbLS_4OBr7GH66HeOBeEoPAQAMoRI7V5SLt2zwhj8H909UI7W4K4iKoUDdwRQ2Mhn0frhUpApij-qck3HWt7q95U0lOwJN8ZxZL6si5n_7AkxK1dNyDZc054rmpdP39UybwiM9of-6OFJoFurfsJ-j3X7By1idOt__7ZMtu0gzJKeOFEV-PlEtaslmzAEqx-J3oIJ-0cdcr_3XoPEdgkgoAv0u2Bq4lmQ3otFUlupo0EMNEw_-Xwmla6h9iXlEo46_ghzWes8PXhbPrfd6YdWtVo-eqsH5b78UpHD4-RFI-KvCvTrR6DrhlOqzPCJe9JahN2x_K0sMQZ6NjTQeQRqSdiBcZQyi4Y8ovplOOy1vbhjXCnLNREzU2GzR2cbmTMS4xq6mHw66D8xYEZjv11cVlRuSkMPkgEA88Xy7sKxGxzqfKBEfcxQbue2cIkTQD9MSa7C5M85iIwbtT3CoxwwgOCdniHpgocgw-KMOf6kTsnRkFnnOm9oTC6L0XlSiX0H9brING6lqmLahH8z4aUbbAwd_w9GvSjmMN-rp-n25zdrLSlJqVnxzMiqITuXc_-LEnQ-KhVcYrPaGPd6SB_3Ed5DW2IiFDHfEODk5YntOGRCesMrUEl9uqGS2Jl_wtyjsFxBUfop09KxZiSheDdSgF0fcOMQWV-sQDIZBuEZkoTfmKLujbytDpcoQO-tRhjWx_aUJQLK15s0sIlU_6xDHlpX8TNrFoDLF6MWgq1V1hYCmBR1CfyH_yi19-tTnkN0oL9MVSk14ys5bPtj5zv3Ehd2CXXGBdUhezQdSdcIllHodw6u0uLYpZmK_tdoszqFxsore70cx-J8m_D2cVvVHys1L6Y6_WpdycqxNndZN3T3LZ9LThDOXoj5sfUPnHjlLIatzo7CGOG1Ed56_WcCa4-Zfr3bu3XmbVgUdexYlq7qJOJc-dHWJTYJEt0eaPR-rIN-dP8DlNNyViKf7efmUSMT58-OPC9wIAJLn8TtwDZDF0rkv5GL4GJ9Gs9R32YglyyQ7h1zTHGA8LFCIU9Wvf8ziVq-EMGwYmpldQuPu_POMDyshB_-yHR22nNB5u6wujshasde8fiQNHNA6E03p4ILoTp7DjKg5Zn-IqS0uAAeESudN8EholDymZlsIuINl0Ojeph2i7endTbKMuTpkpFbF8LIfPRJQ5l5iiCy-iMoKB55IZ984b5KgVOzPqVMRw8O4L9kCkgJMdpm7F8ZQ8WrONYVUzlF2a-Wwg-Tbdw5wfPaFXaUnN86EzwJaURkqPe2UEfyBigwNqmGube160KdBrh6h3Y5Mb6GRtaIPLx-jJsOFP0sGeeq5nrmnFypP4_5DLPmHaqud4VVXdQGH3WlpVbw42iRq7pIpVasws5odGOmxhJPe01dnXErds1SeuSztAoRIqVThzyqygFh-9eFqNINEr7UZnZ7c8TWXLEKcZHILDnXzQjHulqabd1BJJ-DWbz8U11sdWgtaFww9RrQrdZVdJZQAgpigGEpSyW6IpITUojkesjOsLIOiDG_wZR17aSHoKR2D1B8J5FhTc23H6nUWQT2zHvbhFVYGJrqpiD6b43puPBk7lw-2--mkD6u6HMuAvCfW5nfqQjg6WVJJ2LL1HSfOf7nhx_Heg6Rrte7q618jwx6i-UVom6LOuHrHcbg2ws7nnixqqzvrb11ItPjpCDftVHTd6pKJgOqKNtDa747e2bZX8gJc0mdidKIaAVWigvMEW3vfV7J4LzK_TAD6o8Kyc3_-EGdUi8BCrD0U_M1qA5jum21r-GhrrfmL22Ah8wclU42RzUz250yj3PCtPbm9asJUtaktUPIdo45U35EkI5JsPcUCbRdzQshTOSIXVJYxW4q_pNtVCsKpAzWsRNsBEfPtatTwXVh7CAGgpyklFcUnxCvAQ2RIXm4UJUe_4bmXYS0bjQTklIyaDbqLMQQ5gyacoaxrihMr-VH6fvc6Q2SFkzN8RkAXyttDgB2siG_ARnJmnBc_aeII7pe4DYWsPDbzwdPFS3kxe3MQqQ65qLth2K2tSK1qfQK5IcuSyS7yZHWGPlJq3LyYHUgqt13nauYBbmDQKkv77Z2_7PzNRuIJWBGxXNQDQYV2moWc09sMiYLF74RVCZ7XWxDc8IJw0QQ6nBwPntxzID9ircQVYGJ1-22cdDkbNtBCguDg3YUGRGnOHN0Zd-bXcW8FOfoLSgDDtDP1ePfi_0jLEJNA8gaUGFBzyjsmGwXnc9ffY1ZrZ89Ydo0S4S7PqlNuxCjjANs1UoASMa-GpZ2ytGIK4dbFygx4RdCwWN48LF29GfBEyNv-XN8XGlOpKMVIlBB7K0MVc9SFpH__3YWI0LUf9fdNAfJeDyKRBjBqIR3WZ-CC-ZM_t_8A5e8-HhiYTN-JuRq_-FqCkTfSbMHaA8aFNgkvn_yDbQ6MYg2PYt-RKXY6OjvpiVhN323ujKTWdFpckD4OJsKbmEvLVLIjKDbSMTDQXGiE20iauqbwrYM22a1AdiACmCJpn4e9osEwnT70fkzMoFoxnv_h63lggwZElCVTAotEWXZUNYHNvfcUcka3bKmrEOxZM1-QT4iD55Ln5bnd8d80sjcsRVIvVyf-jdIQo6ckySo2bE7X4ek4OQeusQIuy14Cn1gxAwz_z_wgUKFLpACezdhk-YgLok2bXD92M0Zv21SHyQosaoBPTyPUkwBgehUU-0Qym766B4P-fzc1CuHoLtWDUdhbsuwWeKHhgdeZZAAkqUMTlNm67vAbK2l86lJsjSN_vlydUiV-7bfeZOYoinHOPg8kGt6eQLtlS51v_Fkw1u8GlIF8DJLfBZog-H9kppIBSc7Y8RfQKWACKOVhDFsBr9eTAKHhmeF1ioPYGY4UFdJpphyy1gjmYMOLgTP3eDgrhOpMgds1ZXtmiG5X89pwHF1nM4sfJccDORWNhuxHsuw05329qWoDBUfqKao1iUHfEkYvsfBmTB-6dzaS3ug=","format":"openai-responses-v1","id":"rs_0bc170a603dbe4cd016a5d24e7d4a081939a5f7b3e21c30832","index":68}]},"finish_reason":null,"native_finish_reason":null}]}

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

data: {"id":"gen-1784488643-ORCSMyO7U5nXSJA35rZ0","object":"chat.completion.chunk","created":1784488643,"model":"openai/gpt-5.6-sol","provider":"OpenAI","choices":[{"index":0,"delta":{"content":"","role":"assistant","reasoning":null,"reasoning_details":[{"type":"reasoning.encrypted","data":"gAAAAABqXST6MdJbsSm8rBh13_HMcjpQimPrQjvZNA3647twNKoITJt2UZWkj759nW-1hd0TmZGYolFWT95M-oMr0hr0VLYObf6EwiJ8hZNFlEKIM3YRR6Gu23-D_7J61AdHP-GX8shzppls34k-FdFOkmLxvJKhD8D4qI8dQxJ-9DKpDBLTZw95vtRTkm5WqBDuHJMRu3odTe97I4wAqnT0BbXkLZ9kNkPcvvfVKD1siCo6Yn1V7xH45TitANVzodTFf56tSe2ZIEXkHmUAAIXeNKzB90fPb--H9Z7PFsQmUvDGMfLNLvjBpUtlAm8AFXRH33gKpKImN6Z8VjDrGCWr1-D6Cdjar9wou-siLo36EDS2q0zrUPlWe3nAplh3IyJsdTr8HvJzxFf_NKKE64d9dG2Cv3l75DUYUFxe-hNvLzgeOrU60ZhzFHs5GFzlBpXGLG4uP90YWGG3NP4TKqbeeWV0gLZwcu4zFBF1GiWd70-6mvE-L5XimHoqF7iLJWBEm60ZwoPhKI2FXxBpsCsrMDyTLpjfbF5RwfpOPtusCfQ6HLvMUKFZWtnZaVr0AkmXWs_9RTcsW2YKraHk-8PMZgrqWUY3J4yPk2ums297XpylPg9axUOqP2-SJoem9_qzLT-pVG-MJ-MHZWHRep3b8nVoVPjGmNTjGbUNfRQFa3QX_Wt8L9WmBVT2LPAglL2bv-_YBVHemLC9SQlq2GitYGVGW4wp2UYPmwp3glxcIXIQB_lePtrP4Zgo9aIoTDvUNO21ltWPEuRV30WcRGeah3IS4Hxw2Zy2uq9b7bx58mRCTxWhqxn8j5VrfP_TEIgvLuL58RAtRLzrtlMa9WSOTvxNoGCHePqW5JPVJ5tRUG6hpJBc4NK3pD3kn6qVwv-ryFdYaLxdoqi8IzyxpmDDyczrDKOpz8KfvI3Ubx85p-97H8he3i7Jvdc7FQNG2r86ENuN9ahTFF19bfLBUpY0GskMHwb6tKkRDZTE_2yX7IyKNJTURaZeRuUoMA605rUokHaHMGlHXE4oFD9PL3VT-CbDwr1kGHqqMbmF8vD84NhY47ArGwI9LXMCwmJ6we8fpEQjzTzs5eDGnLNURSUR_oiYS0KRto5C6fKSPjTOlUlI7AyJKMHI_2TBYOiS6HF0R1Qj8h63i-UtSiOE_WVJ6LMWBWHfjjmYt62bReZqjIZbQ3dicEgTLJChuioEzroMXw-OhjxF7oqXRmBAhwHAkHWZvTtdJPhj8ldCtNAuHnCv8BmlQ5n-EBP1JmaSx-ZU6Es2lLLVqCLWFdWdXvtg36mJedRXAWUJlkq4K4eFm24x42PjgD37jipnz0URxS1EGBewHuwQZduJ8zT3bQ6Xel53F1WMx_yV1oDEMVyVpm_86q_ey6Yo025xoM3zUTxQLPWvH3GEyxAVGSb2Or7WKlTXr3En73yGhx7ezNIefgSLPhlKekvGKcFC0p-Ujo2RLlrmvK5vh7WFWWcyPii2JgWy8Q7U6uAuGVHrk2Uie1IDBHKRDiHf0ufHYIIiX3VfPKWZQ1fzvm0XkiEg58EZQGfF7sRZkGRY_DoIejsHezA05-1_TloL0QV-X30_7FHaHY35R1rWbr2Bmw0jUL2G3rXpWeVhYkFJdFEKZJKigx5Xx7CiV8mJi46CpkBBTAruK8ZjVuVJf98uIsBF-K3lzjx-cf7p15ABxGCuLbbxWcRyZ7neesbiIpvP2b8IcauNkTYr8fWVc6VZiVat7Q7soZXRo4Wb1PvcLgK11gERs0n046go_STVu4ug8O7j9b6DYbP5kjBeC8hSq4zBYj3FJOm31NnIxJYgIlYMfNTHX5GjUlblHsIdB6JLnpqyyxQXOzpTXQnjASctUJ3itjL6Gvdvc-Ob3IkGtedCPkR-FflMiAy88k1hK0EaTW3h9bv3buibrTplw__0QnJW4Neol_-WG1WIdOS9iUSiVKxwIuLJuZNLPwyQ84G2oqyuYunRWQOUHMs6EvHggP5YpuJIb9hAWE32z7E0XWFrov7iS8FW8tftrj_6vxORtV4qNaQjSo9IXxjvVySpfMXFcrL7x1_3QU8Jdi8Mr6HX2_EHK1DdQvTDmXMYdvkJsSjJz4qKOwziGP5kwyB4gCWxBc9o4jWzfg5e73guUFHp_JAof_cGbBx0GzficUSyLYfIDt2A6NuoU3if5UY0oS_TAxL2oIbF26W4AkZcegdQO9IM0wA7YMRQPwthWgyZ69Ls0-eTMKKRdj1OP4hXuBIU2LN4u__aFR1vk6r27-9u0Nwwwss6u5eRRNC9DcocqnMiUXHyslD7IvqgkwasafOoJeJvTBjJHvieJR6_xqLvYavqoTNmTYkdirr62drKImSkilLnZkqZUAF5gOBGtfvYQdgYt-72rEKi9Itesk98Wf5KTaiY4PEr5Bk2fR5zsMyS6G03y7TknIkRv5xFmK77Nw9No6dAnH70HqxPvoQcTy0awxilKo48uKKZFzOCLWzllb59LFUM6NGDJBUaRMz27Nvv4uLUgRo6C6zKknR4TG1gJHtanKAEHJkbxIAcPlXZDF9ju_QRS31JKyVcWtLjycr7pLaisCmgfjN-TRGc2DSMIjENYzJReDTq8GGW1cwhZzNg3h8_rrwhPV0wDQVMfq0m2KMLyOi30zSq6-SPPeDY8VKkCZPd6SDT1hzu4fzmrW0eCz2vuHJjYRQl9trKXcW8IJj1qmGattBIytOqe1fn3JNUm9cqyUx2VUDcL_b_63XXMLGL6rvbU7nkL8ioqsWT2hXUr2DzMZ5Pk-KEKNj4FDuVKGm7IRcQ5pzS3Rd2Y8LbBRhO2_vm27H77sI9aEj5PcXTK9UalChpyJcTEgtuxbpQ-SosjNQAeN25ME1EfUsOCeroGjys2Dmox2Csfhu0Tj0l8W7Ltxvelsz1M6SgAU3cAfq8abAq17R2rT9Il0tjxsbzMnA_mp7Js4ZN4pchsTH83i1nOsD6E50fl0cl9FXPJQrb3OWe4ZzMQ3-qmBJfWEXblgTlx81xjHE8jhpR8v4kxs-8nje7-IAyhbYvuXykGzcn5CGqR9cbFcKWay0Xf45b9uUbMRCT37m_-qBdg9V-TCBmCcqk2RjWgyRP_2q9saIExw73pYBYdosjoNHy9hbzXHd2aH0zv6Kke0uEhAG2pzxXN6ROOHPNE6LQ6NlX1CRhRaE172d50wILXrHS6aA7FaEwpEd2_suCVYrOB8mkKlzUWyXKZ5euKz6QIKpZSomY8Cn4P0s-eWVd7jYpEtbH0w9FuaOGcpnQPhfpxYOEJU2xx5VgFnl8YpSZ98KVSM-AX6p0jECSh88EGmyT3NwS77R5P0ebLMqIJt6x-OVnNxWXAx4dR-zExug48AMapcKU_OaWso1S135b9tihZ7OLjkNLmLCk1oR7Vd5h7N_7Z3e9dU4Fn6aan0EWDZLnqAtD3MuSgfTRdjT9m_mvtncw6oHvpL5ZPNRR4WptI1eVbHgBdWhguc7ZxevguHdK-9FIJ8xsLDa5sKNvZRkdcN_w9XjcNGMd9Vs5TQLdDg1hOZbrLLPRPIGpL6cBV_UHABM5svBFe2kP1FajLdns7bw1HWkC_aRw9b0ajgwFrx6gRXvyzzfC1PhluB40lfj30-KL9ObQAXQOt2RmDTLIUhs_oDV1hgrQz0xFJfw1gLK5uxrZ3XAiWvjuuNZl7L1hNsjGKQL7LH4IFHb-GyuLyyomdf8uxKCWEDA1B8TLbZ1IAGaLLyZo4NEVhOsiJcFRC-Se8RH9gAYnyJOrBa1CjgIEkbIn0-Nj8RONlH0n2Yy6iBtpPX63u_oHOMw66QgQ7yDeVeWP8DsaNL90B5iEfaaSnG_gaKbUNqP1dVaJM7EUTHlsVtYlw_yz6oEiC-Hwyx5szJ92LTjyZAO0-LpthE_zbQSQ-FjCptM5hR5zjqo6Zlulnz311dQTwSnIe4ffaTofy_Qa6lQfHbe9TNtNGAcNtTATQSBg9Yx_RyyO4fc_tIu3HA==","format":"openai-responses-v1","id":"rs_0bc170a603dbe4cd016a5d24f191c88193996ea94783ddade3","index":69}]},"finish_reason":null,"native_finish_reason":null}]}

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

data: {"id":"gen-1784488643-ORCSMyO7U5nXSJA35rZ0","object":"chat.completion.chunk","created":1784488643,"model":"openai/gpt-5.6-sol","provider":"OpenAI","choices":[{"index":0,"delta":{"content":"","role":"assistant","reasoning":null,"reasoning_details":[{"type":"reasoning.encrypted","data":"gAAAAABqXSU7dUM2MMxnO-F6EHgUX7pLHwQPZeeekKnRT8rWyGGHusRPyJfOi_5eDbfQsZMVmA4vAlWvkjVFLFV7jFlmsHlA4ATebcdmilPVTaJGHM8FJc72gogEkXzP5DYNOv5iIhEViiwnDaPxgWZxyF7NG2nAQgjevFPowhtQpwMLxYP0Hf-_2z1bMKAxalRux6z_WLjytTTiys6pboD3uwMwKM0_Nz4askLkm51bFSJClloNDScnZA26VsH1qLz7qQZUgPMvYeuA17xqj7KSKvqbEAPtkueg2H7iuHwZsElZdFwcoraWkWfdwt86_-DlTVeN04NYvEeBRomz38xuVeatbbK8eKw1DtPGZdGXgzDxO_OvroNgHMl9Cng6J0mxIWAK15knA3jvftbSJ9hwo4x9y7hl6CKejUixTICqhl_M-En-BfJtAsfKrFSxwmbPTzdlMaJTX0nxOThBT8KOZWmrHOnxLT-7jnwHr_MX4wH4OrIDCPbr_lNCCFyROuM-DiJXFXe_3daYUXFSYXuMruUJT0H8xRYaEZSq2RenAiyLYp9Fmgcx4gx9v3XANKNEp5dM_zYQS3gSdFzqM2LVUMv5tblsBcpYRRkpAqyl4DPB-zkU7muvl9-PHbZKOXc2Ku-RTBehNQbsfy6FPDMCBCjPnAuvgKD3kUNfrpIJAKG2kxCg0J4wZsHr7moLs72MDD0BbG3GFnB0F7lo6hs-cv8nMIu3F46vtiiAtU6Elh5QEg_OKQFFFOKGBZd74hfhD2xcfw9MJDmi6i-V7DTfOe03MOvcfGCiNdrtmckX1SQ5QZb44GYoF8LLRkcu9OfxdcycXOQEXu0oC6ZEOfC8f34_-s6hc_ce_2p7dLT8s3DkLdYo5ryPpVTWM9G_HPiD6VMuc0s0suFpMfMX3SSxslDgflN5UIpmM2KT_yJ1J2QJf8vYRSs8hx1dChFqXdYmYYmDUcI_X4Gw6s7kQqVgNUhLt_QCVBccJPVcFsSFeyF9j0htpc02tVUlNq6OZV__kTdg5_QZgwBTutIif2MFBeXAAzHoNHGr_HA52IwMTbJbCuNFEe5-tOn_HAC8_rKb6EvGldRRJWMJK91vez5Gul1KWR-QYiwWNwP_uJnOhBciVwEwU1RDl3jGPgzdNqklYJlECvWchxg6c__yVIVx3VXipP7BqZJv5Mf4Zz0yXdYdux_FUhPMiC4i__k3DaX0At77mABpQ0QaJqj0-0AI4Z2auPNjSz2oiz1AGDo4s-ZBrlq76ri0ciAodic4hdpIcb-d9hyslzCkxXUeWzL9-kcwj5-6qqoJbWCNpoZ1u5aMIu0u7b49yC6pmSg6UrQzlnfGj80f6Vw61hhAqW_zXFe_QLGczsgKb9u-X3mwtqaErI9L4Ae0lyGYXrQEzo0zS0gWrTmBYJSyYr_LDkisAHH8jaCRjM5jWPRgKmbg9NJ2LCmzfvl2IVjbK0EpNAOF7A3BXvOAXbQiXdYx-XXQB8C4Xl5-AxWewF_AYYZavkL5PLw6_NvTVNWUdmSyTjvU4YRnLkFaYmNNNW6gPfJN70D_naju5PJswmZZlhta7rtqf9tEPelL-9_nc4qoF8EtVkNe40kRVq84f0vwSCrqiSxvsP3nawQ2mun0DxW5Gmx97PtVinPgrD5oLs-8ehHzSUoCjRKeESMFX6L8BOSDuEPhf7krx3zHHz9wta9EjDQaisgcQT78V8aJG8Sas6-bjy2txe0dCqOJUh9hZar9WQsCrWAiPXyJe2wbFCfiYq3AqzqHPGEzl-ECpPa3bJFGVzB8sE_bKqieZYVweQ1BHLe8qZfpoz2EL-zLKgNzlPu-E3ZK5QaDwxiRp9Ygtwum5LC6BxQHJLetAO2iht_pQtuB-c4cy2We4g8VPuhZPfQ1A10UdO9OWxV95Y9Xx9ebDlad-obrQNGncNyTyJJFXx3tdfjoqcsU_QVWp8C9EVq3uox6QkuM7qdLQdoYSQvNyYGNmabe9P5MvAfyilRiJKzkhHvhHMDYQ72fqnyPLc4WDpa0388QNDUeQwiz7FbW5l-ylNtlImbxtLuSZIwQ7cAtWj3M1DX6yPGlMIYzdve_4eOqikApATDtk2ghk9vtIYF7qI9ijqCtNHzRBk0JrvySUJ19tstceHWMs-uGL8JXZuXW47AvUYFdMfo0H_isXEqv4gWLeQRHiUqSg2jO7kGnMpoaC1keQCnkvZkCtZtxVPbRuXT1hr52PphE9DUyTkqpgr9L-3QxTTmX8vG74R-T37zHrpAMG5Yn3AzrxH35ONbowCV7gHXi4_q-N8Pg7BKnNQKfTMZ3_BPZKaPt_bLOclC54vnPYvn2M7YaICRL-fWPcNLlB3A_vdMANtE3gMBrOcwaoRBoh8MkE4Bdl5OZQLQ5jxsEjfA4lJ1kwlHPHn0yvw9GvsO2_Tk1wzVOKs68dsE6OAuSZJY6_cC_ctCVmvGga-NUzMGmwRk3uU9diDcXUWM9jqzfmzyVtzVPLCcnH6yT3elTZuvyfP7_PQXTywKK26SJAaz7M80UQ6nZqxexlBwGY9FqTmgTsDHhxiFKCdddQJ9VeFJbY-biSoo6MZLN9HCABKMlyP8QhyG9f_OqFm_YLlutqFbQ8bPWSHO8quERIV6o3wyEYL9pBe5njL0rYTeLzJGBV9CQdn7XLJvQfvkaHogZ4D2UhtdFdNhoNecGv1lfFh3xdSJaFeQwJ--xlkE_COg686oop3-75tEb3mmDgViu3083xPEmM7noN4ai-BRM0SJWPzYYh1o2zTQWaKNjpbOOy7vzScAs-wcaPArpazAFbiQCtORNnyTI-zkW8CR1VhjznVX4Uekp9GJV2WMryvZkXqk1BhRl2OdUOPvtvP_yDUfvMBTXRKH9143b-h7X5tvwc81gybtv4dyoRxvsdRZ4ZiTl4z1IiVjLO0euwUitXzA35LvpEA9AFc6RWff-I_NR1jxZOEZyH76uFMifFXwqBiiJUQFKpAswE9dJpFy6KKPg_qejV5ZYfb7h6rju2WmmODNQCmab2RArzllK2M--MtIXnX-LoSBllEIfKW4jN1i9kMxJSnvdsK_JiJCFsUALUj4go8CtEDNbh6i312fuJ-ZGsq-m8OJirrw5bKPUz66MroTawncZHe33AjdeEE0Gwzyg5QxZMVieCShTcvM5AZ6hmM05IlkI1gce65VLzdaPYRE8dI4T_GR9Oxqo0BK2hiCCA1SE6XAH6xSrSonPZlB_BdLfscnkKlJLfxKaYoUOIAcJHr0kkfM5XEd860Qotw7aqENPfOu3XZ11Hi9LpZHEAmgos-LdcOKGbQatYhOd9Yp4F3FVVXNyfBxOv7rbD9exo6XbxgF5WhA6Wt8Rl0qQEPe7y2OmiEg=","format":"openai-responses-v1","id":"rs_0bc170a603dbe4cd016a5d252f2c6081939de17c53e7421b51","index":77}]},"finish_reason":null,"native_finish_reason":null}]}

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

data: {"id":"gen-1784488643-ORCSMyO7U5nXSJA35rZ0","object":"chat.completion.chunk","created":1784488643,"model":"openai/gpt-5.6-sol","provider":"OpenAI","choices":[{"index":0,"delta":{"content":"","role":"assistant","reasoning":null,"reasoning_details":[{"type":"reasoning.encrypted","data":"gAAAAABqXSVQ23A9y5xXEvuCfa6K6RZdRdzE3vuHy8WKE_NEKWLh_JluUOSVRsuPLw659yrwi68M4J-1vYAwgkOX6pPQcRyk2MnT3ZDhWqJJmMEa9QuQRNExE1CPU8MLFQ06QfHb08XEywpDZnPUo493WBL0UyJvdifVwJ08ykTp7i2dGxBTZ2jh8YkqIb912wGE_DXGPK3IazthYR0DiZBi2r4c2JwfEyJFVVf9qo7rlyTMZB4HE5gC2a4Ly24QoBNtTvO-rFfYchBEGYQ-wC-9F84Gd5oCvVP9Xh_muf2muefK3vvJZd2ozh56eMNX6pdhtc-z_n9aMu8-57sXmX71oGsFIZeH8BA3yklDdOYqmfTN6u-ykApzBtwdWwbiCAmUwdBNuvNUQQWOPbDhOT1yKP7sWAM7HYbm7uV7nfqKj2Np6oca2V4jsVIBwIDAIs_LLXp72ixpDn70Y0hllQ3NU76bGYhSAqX2qXUkjJswpdS4dL9AjJVCBfNt14DRn8a3mskZWcmJsM1Hsmi98VEXr0LRnq9M5Bt5e_7V_rtQCpV_LWSh80EX4j_KLsW0GT7v7_ZiIduHez35Nhm4wgttevlWdbGBWC0bb5aidiGroHJx17YbTksyDrvB6i03YEhaDRGYqyifh36vRhQK7-gsJQdLefCqGox9utXXltJ33hq7O4wknzY4El4IakpIAwCzkW_36to0iMHgxr6OcEYcd-BAirhgkNm6kvIZT1uAbq0MsYHXDLPbEwoiyJJKjj3hvxy5DclM_qhdvgj4lvvK-od2TVWe905etFtVluUdYCdjfSoF54ezvl9YD4o187OAwTlshOhvV1XfO798UnylI56UfCYzb3wpzA0DmIieQgqsocs1IkBiL3ctxDXkTHZStInVzMw35x1hAQmNi-yK0VeEcGxh4NzSwf0Wt2BWr1Ux9XsL7BFETgGNT1g4QwAMOcuBgnPH7IhijMbT8VtySjdk7yzu4E5FpofslPxTDWEf_x0KNFm69-nAMiHIltJf17lpSkfqtf9nykkGN-PW5w4MXC2KJv8Mf9l9L6147eV9yb2-KRhTy3VRQGGijfMwOpKlNQg33vaV2oR9PuSwiIcNcwlmMkLMDl-x8gB3uPUAm1RgcMXebH8adjjRhZVfZEIC4tCvsE8LNHe6185fPOCy3zt9_esG92Ijfq-AhJw2q3Qca_BA32FACEr73Jrnh3mhoZBoyUPN-Ac843tdR_95YBnKg016naFPk9M97Il3LBn_dSN6nnd_Wu9MuHb10Oc_4UDBmJh1NrDq2oSj2xYii_z1igF6UbiK-UzB2nCG3dmsOORagfewen_xfzF_N17BcLSmDY1fnuj_-enarOrq4ezCbDVH5acjszq-qVJ0rmDSYGFa-SPxnyt7ZAXmslKA1ulwhVrNRFwQ92buQCW2hlIzA-HVVVgZiRHm-NrAgdg_qn1oGlcYyU7ZyhMXj_hM-2iqUCCp86bBzLMY8IzpUqjFX7FBYDAjfZ3NPj5S7k9c_-TxURA5yfheH120EHBdE3QCHSDsBHyf7rYrUCUudWII1LPOs_3-UMnyilRtE1h5ZSA_6WChcq5tVBYU3tf00yCAj3rQv2tfxJfBVB1os9KjVzsTN-HB1RombPOkWBwwAclvfKQYl_m6hXzcbWHged3ZW0B9_zoFjk8N6Q0qvmRvewSMFj4EOyA6m8mlI66GA7FSCs4pB50xNCLxwpChjYEXRKDPlOrSRq9cc6Ft3EY1ygcgMkrR_JlbgurWkUUagRif9ykGZFr2knWDjz6GcDS4upNGUMO8TmnwMrmX7JCASWYxlL8Jf3e75WmPKsI9m3jqCQTsYSr8Ltqm1J4EEoKkQgfGpmau-PiHGMgN2x_2BA3mTMMH-s3BgbpjekObzrxRKSMAF9gkkUUs4x76aT5ogCFAYYlJxnqLOT0coBz9aT0PT9slHogAhdd86EGUfEt-sem3Y2vHYpVlIBcMb6TPpaaPOC3BpdWMBhuRCXVkhO0AGAXS6aJQDJhlAkaPvz-9htSQ8p-C8qGMxdKMD5CkNRvv5uvBl_-UMzRLMa-bnAgF28pJowKwzW825TPkXiwr5WqiM8vw1S87wNL2Zwkcj9wLvU13WD20K-u6gSwfLaon421gMRRLKyQm5JTbblDnnLURLo_74HbOxI3bKail4hPc0m1s9ktvKe5PNButPcaeoZRgMc1Bq-mRB8tRJlgvYvTA0yxwsvzTYmcuq4I32ZfpzVYTU_X0cPsGY4iRYFGufE1WVeJm8USMrnmCM8gHnfIUTYkR0ruyz9_R_8Ra7pnax_ZUNGaaHHm-ULrhSVRrt8x87TnemJcd7kM4jEk-xfsTHI0CD3heBAFGaO8M94crJGsu9aPGUcrLJoPbx2EVAYVQ5IiOhFK-nwYvbV3FPkxyK0U0OlsRYMSlsWm0z32rrphEjpO3md6jRnfMG9aso8TfqSw9WODvGzYFTRWUwefOJN2-TgqyJor6xDf3TGDrzo5XJJPm-TJ3VN6gUXg4tFkbEZnIZAWT_N7sukwyVh4igHYyqIqZWE6A602Ji5gLOQ0t1GKRq2FvRSCqaFQAAP2RXAwWrOZnS78Mkm0DLP8DlEVSaJTYkmrVLt8Mxtin6ZtGxLiXeGoDNrkKuvDvmDoDd9Eunl6MJlkXUpGGztna7TWVxuj8DV6xeGUAadBOrtK4kc8fGR9eMkld_5o7c8VAM02ODaFsvui-VJol3LuXNTaNHParuzWJPfFH2o-E_NfmDnwVOWkaVU10oCHqL3aKQs2Tprq5q_-TyvjtfzQuhDJELOdFlsGulL8iImgVHidERDph0uVLlkHOplBr6-IfcY3UgN0LTSgjgmjmQ3kLeu5Ea9znl1CFiF4mSU98myIcWr7Qlgkxm9wH6o1Bz7XaXN-u0X3MczFFSGbzYMwas8-wPNi_5XdDQbcUzVfiTuzbL5a3BSbLQzETVtJ6lOn6HvYLKP11nFBAqwtdMx_oYDvPZzKs6w-oq4-EIOpDMVG9oIUXaSfekOwYeGEQWLOPHf3i3QUGF0cjj6xfugF884Pr1XIeZewEWP4Hye3ZKreGisk_5c5ocnJeYGn-vzlSVjd50KEZKLhtgywavhcVGwrXzo_LFs3FcsBh9WnALLiw5OB2U9MiFy8fcEsiQJOOZmZXW6kXmFC3wG6mCYBZ0zyMywXxAY-8MT2uHvzMSoveEG5ynJ3f4ySPZOyaSDbfpCRfEODhNCQ4dO5vKTsxiMZyIcyCtDstvskt9FU71Rin_F1mYGPqb2DwWuLW-MZ5G4xIN4LXWTxXCU6Ze4xgZHBoDZAjaqBtLoH4oustYLmaiQjt5iavDePR7QR-9KszxO_Fc8P0RTyegUa8VqcdOQDfuySj01CCYsIsSlrAyw3fTjVHhZVk85b_gkPAFHnkrhEN9k4FtRUj7WqZZmQsY6abqXAy-035lNV3aFaXGQRSUql6z5ojsoWHO6HXucOd7OwbRrYqzRNMUM4c04loc-P0kkQHiRAWOKPf47ow10LWUwMM-9pglUj5lWJyBDSBrTvGwk4xs3IpgsU=","format":"openai-responses-v1","id":"rs_0bc170a603dbe4cd016a5d2545f27481939bac6abf9b1d990a","index":79}]},"finish_reason":null,"native_finish_reason":null}]}

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

: OPENROUTER PROCESSING

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data: {"id":"gen-1784488643-ORCS

```python
import bpy
import math
import random
from mathutils import Vector

# -----------------------------------------------------------------------------
# Scene setup
# -----------------------------------------------------------------------------

random.seed(17)

bpy.ops.object.select_all(action='SELECT')
bpy.ops.object.delete(use_global=False)

for datablocks in (
    bpy.data.meshes,
    bpy.data.curves,
    bpy.data.materials,
    bpy.data.cameras,
    bpy.data.lights,
):
    for datablock in list(datablocks):
        if datablock.users == 0:
            datablocks.remove(datablock)

scene = bpy.context.scene
scene.render.engine = 'CYCLES'
scene.render.resolution_x = 1536
scene.render.resolution_y = 1536
scene.render.resolution_percentage = 100
scene.render.image_settings.file_format = 'PNG'
scene.render.image_settings.color_mode = 'RGBA'
scene.render.film_transparent = False

try:
    scene.cycles.samples = 64
    scene.cycles.use_denoising = True
    scene.cycles.max_bounces = 10
    scene.cycles.transparent_max_bounces = 10
except Exception:
    pass

try:
    scene.view_settings.view_transform = 'AgX'
    scene.view_settings.look = 'AgX - Medium High Contrast'
    scene.view_settings.exposure = 0.15
except Exception:
    pass

scene.world.use_nodes = True
world_nodes = scene.world.node_tree.nodes
world_links = scene.world.node_tree.links
world_nodes.clear()
world_out = world_nodes.new('ShaderNodeOutputWorld')
world_bg = world_nodes.new('ShaderNodeBackground')
world_bg.inputs['Color'].default_value = (0.002, 0.018, 0.032, 1.0)
world_bg.inputs['Strength'].default_value = 0.32
world_links.new(world_bg.outputs['Background'], world_out.inputs['Surface'])

fish_collection = bpy.data.collections.new("Goldfish")
environment_collection = bpy.data.collections.new("Watery Environment")
bubble_collection = bpy.data.collections.new("Bubbles")
lighting_collection = bpy.data.collections.new("Lighting and Camera")
scene.collection.children.link(fish_collection)
scene.collection.children.link(environment_collection)
scene.collection.children.link(bubble_collection)
scene.collection.children.link(lighting_collection)

def move_to_collection(obj, collection):
    for old_collection in list(obj.users_collection):
        old_collection.objects.unlink(obj)
    collection.objects.link(obj)

def set_socket(node, names, value):
    if isinstance(names, str):
        names = [names]
    for name in names:
        socket = node.inputs.get(name)
        if socket is not None:
            socket.default_value = value
            return socket
    return None

def make_principled_material(
        name,
        color,
        roughness=0.35,
        metallic=0.0,
        alpha=1.0,
        transmission=0.0,
        coat=0.0,
        emission_strength=0.0,
        emission_color=None,
        ior=1.45):
    mat = bpy.data.materials.new(name)
    mat.use_nodes = True
    mat.diffuse_color = (color[0], color[1], color[2], alpha)
    mat.use_backface_culling = False

    nodes = mat.node_tree.nodes
    links = mat.node_tree.links
    nodes.clear()

    out = nodes.new('ShaderNodeOutputMaterial')
    bsdf = nodes.new('ShaderNodeBsdfPrincipled')
    bsdf.location = (-100, 0)

    set_socket(bsdf, "Base Color", (color[0], color[1], color[2], 1.0))
    set_socket(bsdf, "Roughness", roughness)
    set_socket(bsdf, "Metallic", metallic)
    set_socket(bsdf, ["IOR"], ior)
    set_socket(bsdf, ["Alpha"], alpha)
    set_socket(bsdf, ["Transmission Weight", "Transmission"], transmission)
    set_socket(bsdf, ["Coat Weight", "Clearcoat"], coat)
    set_socket(bsdf, ["Coat Roughness", "Clearcoat Roughness"], 0.18)
    set_socket(bsdf, ["Specular IOR Level", "Specular"], 0.5)

    if emission_color is None:
        emission_color = color
    set_socket(
        bsdf,
        ["Emission Color", "Emission"],
        (emission_color[0], emission_color[1], emission_color[2], 1.0)
    )
    set_socket(bsdf, "Emission Strength", emission_strength)

    links.new(bsdf.outputs['BSDF'], out.inputs['Surface'])

    if alpha < 0.999:
        try:
            mat.surface_render_method = 'DITHERED'
        except Exception:
            try:
                mat.blend_method = 'BLEND'
            except Exception:
                pass
        try:
            mat.use_transparency_overlap = False
        except Exception:
            pass

    return mat, bsdf

def make_body_material():
    mat, bsdf = make_principled_material(
        "Living Goldfish Orange",
        (1.0, 0.19, 0.008),
        roughness=0.27,
        metallic=0.0,
        coat=0.28,
        ior=1.43
    )
    nodes = mat.node_tree.nodes
    links = mat.node_tree.links

    tex = nodes.new('ShaderNodeTexCoord')
    tex.location = (-720, -180)

    vor = nodes.new('ShaderNodeTexVoronoi')
    vor.location = (-520, -180)
    vor.voronoi_dimensions = '3D'
    vor.feature = 'DISTANCE_TO_EDGE'
    vor.inputs['Scale'].default_value = 13.0

    ramp = nodes.new('ShaderNodeValToRGB')
    ramp.location = (-330, -180)
    ramp.color_ramp.interpolation = 'EASE'
    ramp.color_ramp.elements[0].position = 0.008
    ramp.color_ramp.elements[0].color = (0.05, 0.05, 0.05, 1.0)
    ramp.color_ramp.elements[1].position = 0.070
    ramp.color_ramp.elements[1].color = (0.88, 0.88, 0.88, 1.0)

    bump = nodes.new('ShaderNodeBump')
    bump.location = (-120, -150)
    bump.inputs['Strength'].default_value = 0.11
    bump.inputs['Distance'].default_value = 0.055

    links.new(tex.outputs['Generated'], vor.inputs['Vector'])
    links.new(vor.outputs['Distance'], ramp.inputs['Fac'])
    links.new(ramp.outputs['Color'], bump.inputs['Height'])
    links.new(bump.outputs['Normal'], bsdf.inputs['Normal'])

    return mat

def make_backdrop_material():
    mat = bpy.data.materials.new("Calm Teal Water Backdrop")
    mat.use_nodes = True
    mat.diffuse_color = (0.008, 0.12, 0.16, 1.0)
    mat.use_backface_culling = True

    nodes = mat.node_tree.nodes
    links = mat.node_tree.links
    nodes.clear()

    out = nodes.new('ShaderNodeOutputMaterial')
    mix_shader = nodes.new('ShaderNodeMixShader')
    transparent = nodes.new('ShaderNodeBsdfTransparent')
    geometry = nodes.new('ShaderNodeNewGeometry')
    bsdf = nodes.new('ShaderNodeBsdfPrincipled')

    texcoord = nodes.new('ShaderNodeTexCoord')
    separate = nodes.new('ShaderNodeSeparateXYZ')
    gradient = nodes.new('ShaderNodeValToRGB')
    noise = nodes.new('ShaderNodeTexNoise')
    noise_ramp = nodes.new('ShaderNodeValToRGB')
    color_mix = nodes.new('ShaderNodeMixRGB')
    bump = nodes.new('ShaderNodeBump')

    texcoord.location = (-900, 120)
    separate.location = (-700, 180)
    gradient.location = (-510, 220)
    noise.location = (-690, -80)
    noise_ramp.location = (-500, -70)
    color_mix.location = (-285, 150)
    bump.location = (-80, -130)
    bsdf.location = (-40, 180)
    geometry.location = (10, -250)
    transparent.location = (180, -150)
    mix_shader.location = (350, 120)
    out.location = (550, 120)

    gradient.color_ramp.interpolation = 'EASE'
    gradient.color_ramp.elements[0].position = 0.0
    gradient.color_ramp.elements[0].color = (0.002, 0.032, 0.052, 1.0)
    gradient.color_ramp.elements[1].position = 1.0
    gradient.color_ramp.elements[1].color = (0.014, 0.22, 0.25, 1.0)
    middle = gradient.color_ramp.elements.new(0.43)
    middle.color = (0.004, 0.105, 0.145, 1.0)

    noise.inputs['Scale'].default_value = 3.8
    noise.inputs['Detail'].default_value = 2.4
    noise.inputs['Roughness'].default_value = 0.55
    noise.inputs['Distortion'].default_value = 0.18

    noise_ramp.color_ramp.elements[0].color = (0.28, 0.42, 0.45, 1.0)
    noise_ramp.color_ramp.elements[1].color = (0.75, 0.91, 0.92, 1.0)

    color_mix.blend_type = 'SOFT_LIGHT'
    color_mix.inputs[0].default_value = 0.23

    set_socket(bsdf, "Base Color", (0.005, 0.11, 0.15, 1.0))
    set_socket(bsdf, "Roughness", 0.82)
    set_socket(bsdf, ["Emission Color", "Emission"], (0.003, 0.09, 0.13, 1.0))
    set_socket(bsdf, "Emission Strength", 0.065)

    bump.inputs['Strength'].default_value = 0.06
    bump.inputs['Distance'].default_value = 0.3

    links.new(texcoord.outputs['Generated'], separate.inputs['Vector'])
    links.new(separate.outputs['Z'], gradient.inputs['Fac'])
    links.new(texcoord.outputs['Generated'], noise.inputs['Vector'])
    links.new(noise.outputs['Fac'], noise_ramp.inputs['Fac'])
    links.new(gradient.outputs['Color'], color_mix.inputs[1])
    links.new(noise_ramp.outputs['Color'], color_mix.inputs[2])
    links.new(color_mix.outputs['Color'], bsdf.inputs['Base Color'])
    links.new(color_mix.outputs['Color'], bsdf.inputs.get('Emission Color', bsdf.inputs.get('Emission')))
    links.new(noise.outputs['Fac'], bump.inputs['Height'])
    links.new(bump.outputs['Normal'], bsdf.inputs['Normal'])

    links.new(geometry.outputs['Backfacing'], mix_shader.inputs[0])
    links.new(bsdf.outputs['BSDF'], mix_shader.inputs[1])
    links.new(transparent.outputs['BSDF'], mix_shader.inputs[2])
    links.new(mix_shader.outputs['Shader'], out.inputs['Surface'])

    return mat

body_mat = make_body_material()
orange_plain_mat, _ = make_principled_material(
    "Caudal Orange", (1.0, 0.18, 0.006), roughness=0.29, coat=0.2
)
belly_mat, _ = make_principled_material(
    "Warm Golden Belly", (1.0, 0.29, 0.018), roughness=0.32, coat=0.16
)
scale_mat, _ = make_principled_material(
    "Raised Golden Scale Arcs", (1.0, 0.37, 0.025),
    roughness=0.32, metallic=0.12, coat=0.18
)
gill_mat, _ = make_principled_material(
    "Burnished Gill Line", (0.55, 0.045, 0.002),
    roughness=0.38, coat=0.12
)

fin_core_mat, _ = make_principled_material(
    "Fin Membrane Core", (1.0, 0.17, 0.008),
    roughness=0.28, alpha=0.80, transmission=0.08,
    coat=0.14, emission_strength=0.018
)
fin_mid_mat, _ = make_principled_material(
    "Fin Membrane Mid", (1.0, 0.27, 0.020),
    roughness=0.25, alpha=0.58, transmission=0.20,
    coat=0.12, emission_strength=0.028
)
fin_edge_mat, _ = make_principled_material(
    "Translucent Fin Edge", (1.0, 0.49, 0.075),
    roughness=0.22, alpha=0.34, transmission=0.40,
    coat=0.1, emission_strength=0.055
)
fin_ray_mat, _ = make_principled_material(
    "Fine Orange Fin Rays", (0.78, 0.075, 0.003),
    roughness=0.3, alpha=0.75, transmission=0.05,
    coat=0.1, emission_strength=0.012
)
fin_outline_mat, _ = make_principled_material(
    "Luminous Fin Rims", (1.0, 0.38, 0.025),
    roughness=0.22, alpha=0.67, transmission=0.18,
    emission_strength=0.06
)

eye_base_mat, _ = make_principled_material(
    "Deep Glossy Eye", (0.018, 0.005, 0.001),
    roughness=0.12, coat=0.62
)
iris_mat, _ = make_principled_material(
    "Amber Iris", (1.0, 0.34, 0.015),
    roughness=0.16, metallic=0.16, coat=0.48
)
pupil_mat, _ = make_principled_material(
    "Black Pupil", (0.001, 0.001, 0.001),
    roughness=0.10, coat=0.65
)
eye_highlight_mat, _ = make_principled_material(
    "Eye Catchlight", (1.0, 0.94, 0.72),
    roughness=0.08, emission_strength=2.2
)
eye_socket_mat, _ = make_principled_material(
    "Eye Socket Orange", (0.72, 0.065, 0.002),
    roughness=0.32, coat=0.2
)
lip_mat, _ = make_principled_material(
    "Puckered Coral Lips", (0.95, 0.045, 0.003),
    roughness=0.33, coat=0.22
)
mouth_dark_mat, _ = make_principled_material(
    "Mouth Interior", (0.012, 0.0015, 0.001),
    roughness=0.5
)

bubble_mat, _ = make_principled_material(
    "Clear Blue Bubbles", (0.32, 0.82, 1.0),
    roughness=0.07, alpha=0.20, transmission=0.78,
    coat=0.28, ior=1.333
)
bubble_highlight_mat, _ = make_principled_material(
    "Bubble Sparkles", (0.50, 0.95, 1.0),
    roughness=0.08, alpha=0.82, emission_strength=1.0
)

plant_mat, _ = make_principled_material(
    "Distant Teal Grass", (0.008, 0.19, 0.15),
    roughness=0.72, alpha=0.82
)
stone_mat_a, _ = make_principled_material(
    "Blue Green Pebbles", (0.018, 0.11, 0.13), roughness=0.78
)
stone_mat_b, _ = make_principled_material(
    "Cool Slate Pebbles", (0.032, 0.075, 0.105), roughness=0.82
)
backdrop_mat = make_backdrop_material()

goldfish_root = bpy.data.objects.new("Goldfish Hero Root", None)
fish_collection.objects.link(goldfish_root)
goldfish_root["description"] = (
    "A rounded orange goldfish in a gentle S-curve, with translucent flowing fins, "
    "raised scales, two complete eyes, and a forked veil tail."
)

def make_mesh_object(name, verts, faces, collection, materials=None, face_materials=None, parent=None):
    mesh = bpy.data.meshes.new(name + " Mesh")
    mesh.from_pydata(verts, [], faces)
    mesh.validate(verbose=False)
    mesh.update()

    obj = bpy.data.objects.new(name, mesh)
    collection.objects.link(obj)

    if parent is not None:
        obj.parent = parent

    if materials:
        for mat in materials:
            mesh.materials.append(mat)

    if face_materials:
        for poly, material_index in zip(mesh.polygons, face_materials):
            poly.material_index = material_index

    for poly in mesh.polygons:
        poly.use_smooth = True

    return obj

def add_solidify(obj, thickness):
    modifier = obj.modifiers.new("Delicate Membrane Thickness", 'SOLIDIFY')
    modifier.thickness = thickness
    modifier.offset = 0.0
    modifier.use_even_offset = True
    return modifier

def curve_tube(name, points, radius, material, collection=fish_collection, parent=goldfish_root):
    if len(points) < 2:
        return None

    curve_data = bpy.data.curves.new(name + " Curve", 'CURVE')
    curve_data.dimensions = '3D'
    curve_data.resolution_u = 1
    curve_data.bevel_depth = radius
    curve_data.bevel_resolution = 2
    curve_data.resolution_u = 1

    spline = curve_data.splines.new('POLY')
    spline.points.add(len(points) - 1)
    for point, co in zip(spline.points, points):
        point.co = (co[0], co[1], co[2], 1.0)

    obj = bpy.data.objects.new(name, curve_data)
    collection.objects.link(obj)
    obj.data.materials.append(material)
    if parent is not None:
        obj.parent = parent

    bpy.ops.object.select_all(action='DESELECT')
    obj.select_set(True)
    bpy.context.view_layer.objects.active = obj
    bpy.ops.object.convert(target='MESH')
    obj = bpy.context.object
    obj.name = name

    for poly in obj.data.polygons:
        poly.use_smooth = True

    return obj

def add_uv_sphere(name, location, scale, material, collection, parent=None, segments=32, rings=20):
    bpy.ops.mesh.primitive_uv_sphere_add(
        segments=segments,
        ring_count=rings,
        radius=1.0,
        location=location
    )
    obj = bpy.context.object
    obj.name = name
    obj.scale = scale
    move_to_collection(obj, collection)
    if parent is not None:
        obj.parent = parent
    obj.data.materials.append(material)
    for poly in obj.data.polygons:
        poly.use_smooth = True
    return obj

# -----------------------------------------------------------------------------
# Parametric rounded body with an actual lateral S-curve
# -----------------------------------------------------------------------------

body_x_min = -2.60
body_x_max = 1.15

profile_controls = [
    (0.00, 0.035),
    (0.035, 0.48),
    (0.09, 0.92),
    (0.18, 1.28),
    (0.32, 1.48),
    (0.46, 1.52),
    (0.60, 1.38),
    (0.72, 1.15),
    (0.84, 0.82),
    (0.94, 0.56),
    (1.00, 0.43),
]

def smoothstep(value):
    return value * value * (3.0 - 2.0 * value)

def body_parameters_from_t(t):
    t = max(0.0, min(1.0, t))

    rz = profile_controls[-1][1]
    for index in range(len(profile_controls) - 1):
        t0, r0 = profile_controls[index]
        t1, r1 = profile_controls[index + 1]
        if t0 <= t <= t1:
            f = smoothstep((t - t0) / max(t1 - t0, 1.0e-8))
            rz = r0 * (1.0 - f) + r1 * f
            break

    ry = rz * (0.63 + 0.045 * math.sin(math.pi * t))
    center_y = 0.16 * math.sin(2.0 * math.pi * (t - 0.10)) + 0.14 * (t - 0.5)
    center_z = 0.10 * math.sin(2.0 * math.pi * (t + 0.05))
    return center_y, center_z, ry, rz

def body_parameters_from_x(x):
    t = (x - body_x_min) / (body_x_max - body_x_min)
    cy, cz, ry, rz = body_parameters_from_t(t)
    return t, cy, cz, ry, rz

def body_surface_y(x, z, side_sign):
    if x < body_x_min or x > body_x_max:
        return None
    _, cy, cz, ry, rz = body_parameters_from_x(x)
    local_z = (z - cz) / max(rz, 1.0e-6)
    if abs(local_z) >= 0.985:
        return None
    radial = math.sqrt(max(0.0, 1.0 - local_z * local_z))
    return cy + side_sign * ry * radial

body_verts = []
body_faces = []
body_face_mats = []
body_rings = 44
body_sides = 52

for ring in range(body_rings):
    t = ring / (body_rings - 1)
    x = body_x_min + (body_x_max - body_x_min) * t
    cy, cz, ry, rz = body_parameters_from_t(t)
    for side in range(body_sides):
        angle = 2.0 * math.pi * side / body_sides
        body_verts.append((
            x,
            cy + ry * math.cos(angle),
            cz + rz * math.sin(angle)
        ))

for ring in range(body_rings - 1):
    for side in range(body_sides):
        next_side = (side + 1) % body_sides
        a = ring * body_sides + side
        b = ring * body_sides + next_side
        c = (ring + 1) * body_sides + next_side
        d = (ring + 1) * body_sides + side
        body_faces.append((a, b, c, d))

        angle_mid = 2.0 * math.pi * (side + 0.5) / body_sides
        body_face_mats.append(1 if math.sin(angle_mid) < -0.78 else 0)

nose_cy, nose_cz, _, _ = body_parameters_from_t(0.0)
tail_cy, tail_cz, _, _ = body_parameters_from_t(1.0)
nose_center_index = len(body_verts)
body_verts.append((body_x_min - 0.02, nose_cy, nose_cz))
tail_center_index = len(body_verts)
body_verts.append((body_x_max + 0.02, tail_cy, tail_cz))

for side in range(body_sides):
    next_side = (side + 1) % body_sides
    body_faces.append((nose_center_index, next_side, side))
    body_face_mats.append(0)

    a = (body_rings - 1) * body_sides + side
    b = (body_rings - 1) * body_sides + next_side
    body_faces.append((tail_center_index, a, b))
    body_face_mats.append(0)

body_obj = make_mesh_object(
    "Rounded S-Curved Goldfish Body",
    body_verts,
    body_faces,
    fish_collection,
    [body_mat, belly_mat],
    body_face_mats,
    goldfish_root
)

# Curving caudal peduncle continues the S through the tail root.
ped_verts = []
ped_faces = []
ped_rings = 28
ped_sides = 32

start_x = 0.74
end_x = 1.78
_, ped_start_y, ped_start_z, _, _ = body_parameters_from_x(start_x)

for ring in range(ped_rings):
    u = ring / (ped_rings - 1)
    eased = smoothstep(u)
    x = start_x + (end_x - start_x) * u
    cy = ped_start_y * (1.0 - eased) + 0.34 * eased + 0.065 * math.sin(math.pi * u)
    cz = ped_start_z * (1.0 - eased) - 0.055 * eased + 0.035 * math.sin(math.pi * u)
    rz = (
        0.64 * (1.0 - smoothstep(min(u / 0.58, 1.0))) +
        0.36 * smoothstep(min(u / 0.58, 1.0))
    )
    if u > 0.58:
        f = smoothstep((u - 0.58) / 0.42)
        rz = 0.36 * (1.0 - f) + 0.30 * f
    ry = rz * 0.70

    for side in range(ped_sides):
        angle = 2.0 * math.pi * side / ped_sides
        ped_verts.append((
            x,
            cy + ry * math.cos(angle),
            cz + rz * math.sin(angle)
        ))

for ring in range(ped_rings - 1):
    for side in range(ped_sides):
        ns = (side + 1) % ped_sides
        a = ring * ped_sides + side
        b = ring * ped_sides + ns
        c = (ring + 1) * ped_sides + ns
        d = (ring + 1) * ped_sides + side
        ped_faces.append((a, b, c, d))

peduncle_obj = make_mesh_object(
    "Curved Caudal Peduncle",
    ped_verts,
    ped_faces,
    fish_collection,
    [orange_plain_mat],
    None,
    goldfish_root
)

# -----------------------------------------------------------------------------
# Large forked, rippling veil tail
# -----------------------------------------------------------------------------

tail_root = Vector((1.55, 0.34, -0.055))

def tail_point(u, v):
    half_height = 0.20 * (1.0 - u) + 2.08 * (u ** 0.82)
    x = tail_root.x + u * (2.00 + 0.85 * (abs(v) ** 1.55))
    y = (
        tail_root.y
        + 0.35 * math.sin(math.pi * u) * math.sin(math.pi * v)
        - 0.26 * u * v
        + 0.09 * (u ** 1.6) * math.sin(5.0 * math.pi * v)
    )
    z = (
        tail_root.z
        + v * half_height
        + 0.12 * (u ** 1.55) * math.sin(3.0 * math.pi * v)
    )
    return Vector((x, y, z))

tail_verts = []
tail_faces = []
tail_mats = []
tail_u_count = 24
tail_v_count = 43

for iu in range(tail_u_count):
    u = iu / (tail_u_count - 1)
    for iv in range(tail_v_count):
        v = -1.0 + 2.0 * iv / (tail_v_count - 1)
        tail_verts.append(tuple(tail_point(u, v)))

for iu in range(tail_u_count - 1):
    umid = (iu + 0.5) / (tail_u_count - 1)
    if umid < 0.53:
        mat_index = 0
    elif umid < 0.82:
        mat_index = 1
    else:
        mat_index = 2

    for iv in range(tail_v_count - 1):
        a = iu * tail_v_count + iv
        b = (iu + 1) * tail_v_count + iv
        c = (iu + 1) * tail_v_count + iv + 1
        d = iu * tail_v_count + iv + 1
        tail_faces.append((a, b, c, d))
        tail_mats.append(mat_index)

tail_obj = make_mesh_object(
    "Big Forked Flowing Tail Fin",
    tail_verts,
    tail_faces,
    fish_collection,
    [fin_core_mat, fin_mid_mat, fin_edge_mat],
    tail_mats,
    goldfish_root
)
add_solidify(tail_obj, 0.022)

curve_tube(
    "Tail Trailing Translucent Rim",
    [tail_point(1.0, -1.0 + 2.0 * i / 50.0) for i in range(51)],
    0.026,
    fin_outline_mat
)
curve_tube(
    "Tail Upper Flowing Edge",
    [tail_point(i / 32.0, 1.0) for i in range(33)],
    0.022,
    fin_outline_mat
)
curve_tube(
    "Tail Lower Flowing Edge",
    [tail_point(i / 32.0, -1.0) for i in range(33)],
    0.022,
    fin_outline_mat
)

for index, v in enumerate((-0.86, -0.66, -0.46, -0.24, 0.0, 0.24, 0.46, 0.66, 0.86)):
    curve_tube(
        "Tail Fin Ray %02d" % index,
        [tail_point(0.04 + 0.96 * i / 28.0, v) for i in range(29)],
        0.014 if abs(v) > 0.01 else 0.017,
        fin_ray_mat
    )

# -----------------------------------------------------------------------------
# Dorsal and anal fins
# -----------------------------------------------------------------------------

def make_midline_fin(name, x0, x1, direction=1.0, maximum_height=1.0, ray_count=6):
    def point(s, v):
        x_base = x0 + (x1 - x0) * s
        _, cy, cz, _, rz = body_parameters_from_x(x_base)
        base_z = cz + direction * rz
        envelope = (
            0.055 +
            maximum_height *
            (math.sin(math.pi * s) ** 0.58) *
            (1.0 - 0.28 * s if direction > 0.0 else 1.0 - 0.12 * s)
        )
        x = x_base + 0.16 * v * s
        y = cy + 0.105 * v * math.sin(2.0 * math.pi * s) + 0.025 * v
        z = base_z + direction * v * envelope
        return Vector((x, y, z))

    verts = []
    faces = []
    face_mats = []
    s_count = 22
    v_count = 7

    for si in range(s_count):
        s = si / (s_count - 1)
        for vi in range(v_count):
            v = vi / (v_count - 1)
            verts.append(tuple(point(s, v)))

    for si in range(s_count - 1):
        for vi in range(v_count - 1):
            a = si * v_count + vi
            b = (si + 1) * v_count + vi
            c = (si + 1) * v_count + vi + 1
            d = si * v_count + vi + 1
            faces.append((a, b, c, d))
            vmid = (vi + 0.5) / (v_count - 1)
            face_mats.append(0 if vmid < 0.52 else (1 if vmid < 0.82 else 2))

    obj = make_mesh_object(
        name,
        verts,
        faces,
        fish_collection,
        [fin_core_mat, fin_mid_mat, fin_edge_mat],
        face_mats,
        goldfish_root
    )
    add_solidify(obj, 0.018)

    curve_tube(
        name + " Translucent Rim",
        [point(i / 36.0, 1.0) for i in range(37)],
        0.020,
        fin_outline_mat
    )

    for index in range(ray_count):
        s = 0.10 + 0.78 * index / max(ray_count - 1, 1)
        curve_tube(
            name + " Ray %02d" % index,
            [point(s, 0.06 + 0.94 * j / 16.0) for j in range(17)],
            0.0115,
            fin_ray_mat
        )

    return obj

make_midline_fin(
    "Tall Flowing Dorsal Fin",
    -1.02,
    0.88,
    direction=1.0,
    maximum_height=1.05,
    ray_count=7
)
make_midline_fin(
    "Small Lower Anal Fin",
    0.12,
    1.04,
    direction=-1.0,
    maximum_height=0.62,
    ray_count=4
)

# -----------------------------------------------------------------------------
# Pectoral side fins, complete on both sides and slightly asymmetric in motion
# -----------------------------------------------------------------------------

def make_pectoral_fin(side_sign):
    side_name = "Near" if side_sign < 0 else "Far"
    root_x = -1.16
    root_z = -0.08
    root_y = body_surface_y(root_x, root_z, side_sign)
    far_lift = 0.16 if side_sign > 0 else 0.0
    far_sweep = 0.10 if side_sign > 0 else 0.0

    def point(u, v):
        root = Vector((
            root_x + 0.045 * v,
            root_y,
            root_z + 0.065 * v
        ))
        end = Vector((
            -0.08 + 0.36 * v + far_sweep,
            root_y + side_sign * (0.74 + 0.08 * math.cos(math.pi * v)),
            -0.82 + 0.58 * v + far_lift
        ))
        eased = u ** 0.92
        pos = root.lerp(end, eased)
        pos.y += side_sign * 0.11 * math.sin(math.pi * u) * math.sin(math.pi * v)
        pos.z += 0.055 * math.sin(math.pi * u) * math.cos(math.pi * v)
        return pos

    u_count = 14
    v_count = 17
    verts = []
    faces = []
    face_mats = []

    for ui in range(u_count):
        u = ui / (u_count - 1)
        for vi in range(v_count):
            v = -1.0 + 2.0 * vi / (v_count - 1)
            verts.append(tuple(point(u, v)))

    for ui in range(u_count - 1):
        umid = (ui + 0.5) / (u_count - 1)
        mat_index = 0 if umid < 0.50 else (1 if umid < 0.80 else 2)
        for vi in range(v_count - 1):
            a = ui * v_count + vi
            b = (ui + 1) * v_count + vi
            c = (ui + 1) * v_count + vi + 1
            d = ui * v_count + vi + 1
            faces.append((a, b, c, d))
            face_mats.append(mat_index)

    obj = make_mesh_object(
        side_name + " Pectoral Side Fin",
        verts,
        faces,
        fish_collection,
        [fin_core_mat, fin_mid_mat, fin_edge_mat],
        face_mats,
        goldfish_root
    )
    add_solidify(obj, 0.016)

    curve_tube(
        side_name + " Pectoral Trailing Rim",
        [point(1.0, -1.0 + 2.0 * i / 28.0) for i in range(29)],
        0.019,
        fin_outline_mat
    )

    for index, v in enumerate((-0.72, -0.35, 0.0, 0.35, 0.72)):
        curve_tube(
            side_name + " Pectoral Ray %02d" % index,
            [point(0.05 + 0.95 * i / 18.0, v) for i in range(19)],
            0.0105,
            fin_ray_mat
        )

    return obj

make_pectoral_fin(-1.0)
make_pectoral_fin(1.0)

# -----------------------------------------------------------------------------
# Raised scale arcs and curved gill covers on both flanks
# -----------------------------------------------------------------------------

def make_surface_ribbons(name, paths, side_sign, width, material, outward_offset=0.018):
    verts = []
    faces = []

    for path in paths:
        if len(path) < 2:
            continue

        valid = True
        for x, z in path:
            if body_surface_y(x, z, side_sign) is None:
                valid = False
                break
        if not valid:
            continue

        base_index = len(verts)

        for index, (x, z) in enumerate(path):
            if index == 0:
                tx = path[1][0] - path[0][0]
                tz = path[1][1] - path[0][1]
            elif index == len(path) - 1:
                tx = path[-1][0] - path[-2][0]
                tz = path[-1][1] - path[-2][1]
            else:
                tx = path[index + 1][0] - path[index - 1][0]
                tz = path[index + 1][1] - path[index - 1][1]

            length = math.sqrt(tx * tx + tz * tz)
            if length < 1.0e-6:
                length = 1.0
            px = -tz / length
            pz = tx / length

            x0 = x - px * width * 0.5
            z0 = z - pz * width * 0.5
            x1 = x + px * width * 0.5
            z1 = z + pz * width * 0.5
            y0 = body_surface_y(x0, z0, side_sign)
            y1 = body_surface_y(x1, z1, side_sign)

            if y0 is None:
                y0 = body_surface_y(x, z, side_sign)
            if y1 is None:
                y1 = body_surface_y(x, z, side_sign)

            verts.append((x0, y0 + side_sign * outward_offset, z0))
            verts.append((x1, y1 + side_sign * outward_offset, z1))

        for index in range(len(path) - 1):
            a = base_index + index * 2
            b = base_index + (index + 1) * 2
            faces.append((a, b, b + 1, a + 1))

    if not faces:
        return None

    return make_mesh_object(
        name,
        verts,
        faces,
        fish_collection,
        [material],
        None,
        goldfish_root
    )

scale_paths = []
scale_rows = (-0.72, -0.39, -0.06, 0.27, 0.60, 0.90)

for row_index, z_center in enumerate(scale_rows):
    stagger = 0.17 if row_index % 2 else 0.0
    for column in range(7):
        x_center = -1.13 + column * 0.34 + stagger
        radius = 0.17
        path = []
        for sample in range(14):
            angle = -1.28 + 2.56 * sample / 13.0
            x = x_center + radius * math.cos(angle)
            z = z_center + radius * math.sin(angle)
            path.append((x, z))
        scale_paths.append(path)

make_surface_ribbons(
    "Near Flank Raised Scale Pattern",
    scale_paths,
    -1.0,
    0.022,
    scale_mat
)
make_surface_ribbons(
    "Far Flank Raised Scale Pattern",
    scale_paths,
    1.0,
    0.022,
    scale_mat
)

gill_path = []
for sample in range(28):
    angle = -1.18 + 2.36 * sample / 27.0
    gill_path.append((
        -1.53 + 0.39 * math.cos(angle),
        0.04 + 0.65 * math.sin(angle)
    ))

make_surface_ribbons(
    "Near Curved Gill Cover",
    [gill_path],
    -1.0,
    0.052,
    gill_mat,
    outward_offset=0.025
)
make_surface_ribbons(
    "Far Curved Gill Cover",
    [gill_path],
    1.0,
    0.052,
    gill_mat,
    outward_offset=0.025
)

# -----------------------------------------------------------------------------
# Two complete expressive eyes
# -----------------------------------------------------------------------------

eye_x = -1.86
eye_z = 0.47

for side_sign, side_name in ((-1.0, "Near"), (1.0, "Far")):
    surface_y = body_surface_y(eye_x, eye_z, side_sign)

    bpy.ops.mesh.primitive_torus_add(
        major_segments=40,
        minor_segments=10,
        major_radius=0.35,
        minor_radius=0.065,
        location=(eye_x, surface_y + side_sign * 0.020, eye_z),
        rotation=(math.pi / 2.0, 0.0, 0.0)
    )
    socket = bpy.context.object
    socket.name = side_name + " Raised Eye Socket"
    move_to_collection(socket, fish_collection)
    socket.parent = goldfish_root
    socket.data.materials.append(eye_socket_mat)
    for poly in socket.data.polygons:
        poly.use_smooth = True

    add_uv_sphere(
        side_name + " Glossy Eyeball",
        (eye_x, surface_y + side_sign * 0.115, eye_z),
        (0.325, 0.215, 0.345),
        eye_base_mat,
        fish_collection,
        goldfish_root
    )
    add_uv_sphere(
        side_name + " Amber Iris",
        (eye_x, surface_y + side_sign * 0.305, eye_z),
        (0.230, 0.055, 0.238),
        iris_mat,
        fish_collection,
        goldfish_root
    )
    add_uv_sphere(
        side_name + " Black Pupil",
        (eye_x - 0.010, surface_y + side_sign * 0.350, eye_z - 0.005),
        (0.132, 0.040, 0.153),
        pupil_mat,
        fish_collection,
        goldfish_root,
        segments=28,
        rings=16
    )
    add_uv_sphere(
        side_name + " Eye Catchlight",
        (
            eye_x - 0.060,
            surface_y + side_sign * 0.392,
            eye_z + 0.080
        ),
        (0.046, 0.018, 0.046),
        eye_highlight_mat,
        fish_collection,
        goldfish_root,
        segments=20,
        rings=12
    )

# Puckered circular goldfish mouth.
mouth_location = (body_x_min - 0.045, nose_cy, nose_cz - 0.13)

bpy.ops.mesh.primitive_cylinder_add(
    vertices=32,
    radius=0.105,
    depth=0.032,
    location=(mouth_location[0] - 0.012, mouth_location[1], mouth_location[2]),
    rotation=(0.0, math.pi / 2.0, 0.0)
)
mouth_inner = bpy.context.object
mouth_inner.name = "Small Dark Mouth Opening"
move_to_collection(mouth_inner, fish_collection)
mouth_inner.parent = goldfish_root
mouth_inner.data.materials.append(mouth_dark_mat)

bpy.ops.mesh.primitive_torus_add(
    major_segments=40,
    minor_segments=10,
    major_radius=0.175,
    minor_radius=0.060,
    location=mouth_location,
    rotation=(0.0, math.pi / 2.0, 0.0)
)
mouth_lips = bpy.context.object
mouth_lips.name = "Puckered Goldfish Lips"
move_to_collection(mouth_lips, fish_collection)
mouth_lips.parent = goldfish_root
mouth_lips.scale.z = 0.82
mouth_lips.data.materials.append(lip_mat)
for poly in mouth_lips.data.polygons:
    poly.use_smooth = True

# -----------------------------------------------------------------------------
# Calm underwater cyclorama, pebbles, and distant water grass
# -----------------------------------------------------------------------------

profile = [
    (-5.5, -3.0),
    (-2.0, -3.0),
    (0.0, -3.0),
    (2.0, -3.0),
]

for index in range(1, 13):
    theta = -math.pi / 2.0 + (math.pi / 2.0) * index / 12.0
    y = 2.0 + 2.5 * math.cos(theta)
    z = -0.5 + 2.5 * math.sin(theta)
    profile.append((y, z))

profile.extend([
    (4.5, 2.0),
    (4.5, 5.7),
])

backdrop_verts = []
backdrop_faces = []
backdrop_half_width = 6.2

for y, z in profile:
    backdrop_verts.append((-backdrop_half_width, y, z))
    backdrop_verts.append((backdrop_half_width, y, z))

for index in range(len(profile) - 1):
    a = index * 2
    b = a + 1
    c = (index + 1) * 2 + 1
    d = (index + 1) * 2
    backdrop_faces.append((a, b, c, d))

backdrop = make_mesh_object(
    "One Sided Calm Water Cyclorama",
    backdrop_verts,
    backdrop_faces,
    environment_collection,
    [backdrop_mat]
)
backdrop["note"] = "Front-facing in presentation, backface-culled for unobstructed rear inspection."

# Pebbles sit low enough not to compete with the hero.
pebble_data = [
    (-3.85, 1.10, -2.86, 0.48, 0.33, 0.16, 0.20),
    (-3.15, 1.75, -2.88, 0.30, 0.24, 0.13, -0.25),
    (-1.65, 2.00, -2.90, 0.36, 0.26, 0.11, 0.35),
    (2.75, 1.75, -2.87, 0.43, 0.30, 0.15, -0.20),
    (3.65, 1.10, -2.89, 0.34, 0.25, 0.12, 0.38),
    (4.25, 1.90, -2.88, 0.50, 0.31, 0.14, 0.12),
]

for index, (x, y, z, sx, sy, sz, rot) in enumerate(pebble_data):
    bpy.ops.mesh.primitive_ico_sphere_add(
        subdivisions=2,
        radius=1.0,
        location=(x, y, z)
    )
    pebble = bpy.context.object
    pebble.name = "Smooth Underwater Pebble %02d" % index
    pebble.scale = (sx, sy, sz)
    pebble.rotation_euler.z = rot
    move_to_collection(pebble, environment_collection)
    pebble.data.materials.append(stone_mat_a if index % 2 == 0 else stone_mat_b)
    for poly in pebble.data.polygons:
        poly.use_smooth = True

def make_grass_blade(name, base_x, base_y, height, bend, phase):
    steps = 11
    verts = []
    faces = []

    for index in range(steps):
        t = index / (steps - 1)
        center_x = (
            base_x
            + bend * (t ** 1.4)
            + 0.07 * math.sin(2.0 * math.pi * t + phase) * t
        )
        center_y = base_y + 0.08 * math.sin(math.pi * t + phase) * t
        center_z = -2.96 + height * t
        width = 0.095 * ((1.0 - t) ** 0.62) + 0.008

        verts.append((center_x - width, center_y, center_z))
        verts.append((center_x + width, center_y, center_z))

    for index in range(steps - 1):
        a = index * 2
        b = (index + 1) * 2
        faces.append((a, b, b + 1, a + 1))

    blade = make_mesh_object(
        name,
        verts,
        faces,
        environment_collection,
        [plant_mat]
    )
    add_solidify(blade, 0.018)
    return blade

for cluster_index, cluster_x in enumerate((-4.35, 4.55)):
    for blade_index in range(6):
        make_grass_blade(
            "Distant Water Grass %d %d" % (cluster_index, blade_index),
            cluster_x + (blade_index - 2.5) * 0.13,
            1.65 + 0.05 * blade_index,
            0.72 + 0.16 * ((blade_index * 3 + cluster_index) % 4),
            (-0.28 + 0.11 * blade_index) * (-1.0 if cluster_index else 1.0),
            blade_index * 0.65
        )

# -----------------------------------------------------------------------------
# Bubbles: a small breath trail plus quiet ambient bubbles
# -----------------------------------------------------------------------------

bubble_specs = [
    (-2.92, -0.12, 0.20, 0.060),
    (-3.18, 0.02, 0.58, 0.085),
    (-3.38, 0.17, 1.04, 0.125),
    (-3.02, 0.75, 2.15, 0.085),
    (-1.80, 1.30, 2.86, 0.125),
    (0.75, 2.10, 3.10, 0.070),
    (2.75, 1.10, 2.68, 0.155),
    (3.45, 0.15, 1.52, 0.085),
    (3.82, 1.00, -0.35, 0.110),
    (2.10, -0.85, -2.28, 0.070),
    (-3.78, 1.80, -1.28, 0.115),
    (0.05, 2.65, 2.35, 0.055),
]

for index, (x, y, z, radius) in enumerate(bubble_specs):
    bubble = add_uv_sphere(
        "Water Bubble %02d" % index,
        (x, y, z),
        (radius, radius, radius),
        bubble_mat,
        bubble_collection,
        None,
        segments=24,
        rings=14
    )

    if radius >= 0.08:
        add_uv_sphere(
            "Bubble Glint %02d" % index,
            (
                x - radius * 0.30,
                y - radius * 0.60,
                z + radius * 0.35
            ),
            (radius * 0.17, radius * 0.07, radius * 0.17),
            bubble_highlight_mat,
            bubble_collection,
            None,
            segments=16,
            rings=10
        )

# -----------------------------------------------------------------------------
# Cinematic lighting
# -----------------------------------------------------------------------------

def aim_object(obj, target):
    direction = Vector(target) - obj.location
    obj.rotation_euler = direction.to_track_quat('-Z', 'Y').to_euler()

def add_area_light(name, location, target, energy, size, color, shape='DISK'):
    light_data = bpy.data.lights.new(name + " Data", 'AREA')
    light_data.energy = energy
    light_data.shape = shape
    light_data.size = size
    light_data.color = color

    obj = bpy.data.objects.new(name, light_data)
    lighting_collection.objects.link(obj)
    obj.location = location
    aim_object(obj, target)
    return obj

add_area_light(
    "Warm Upper Key",
    (-4.4, -5.6, 7.1),
    (-0.25, 0.0, 0.05),
    1050.0,
    4.2,
    (1.0, 0.78, 0.58)
)
add_area_light(
    "Cool Underwater Fill",
    (4.6, -4.4, 1.1),
    (0.4, 0.0, -0.15),
    620.0,
    3.8,
    (0.26, 0.67, 0.82)
)
add_area_light(
    "Cyan Translucent Fin Rim",
    (2.7, 3.0, 5.4),
    (0.8, 0.15, 0.0),
    1350.0,
    3.4,
    (0.34, 0.88, 1.0)
)
add_area_light(
    "Soft Overhead Water Light",
    (-0.2, 0.5, 8.0),
    (0.3, 0.0, -0.2),
    720.0,
    4.8,
    (0.68, 0.91, 1.0)
)

# -----------------------------------------------------------------------------
# Presentation camera
# -----------------------------------------------------------------------------

focus = bpy.data.objects.new("Goldfish Camera Focus", None)
lighting_collection.objects.link(focus)
focus.location = (0.45, 0.0, -0.05)

camera_data = bpy.data.cameras.new("Goldfish Hero Camera Data")
camera = bpy.data.objects.new("Goldfish Hero Camera", camera_data)
lighting_collection.objects.link(camera)

camera.location = (-5.8, -14.5, 3.8)
camera_data.lens = 70.0
camera_data.sensor_width = 36.0
camera_data.clip_start = 0.05
camera_data.clip_end = 200.0
camera_data.dof.use_dof = True
camera_data.dof.focus_object = focus
camera_data.dof.aperture_fstop = 5.6
camera_data.dof.aperture_blades = 8
aim_object(camera, focus.location)

scene.camera = camera

# Slightly lift the whole living subject off the exact mechanical axes.
goldfish_root.rotation_euler = (
    math.radians(1.4),
    math.radians(0.8),
    math.radians(0.5)
)
goldfish_root.location.z = 0.03

# Tidy final selection and preserve the complete scene.
bpy.ops.object.select_all(action='DESELECT')
camera.select_set(True)
bpy.context.view_layer.objects.active = camera

scene.render.filepath = "/tmp/goldfish_hero.png"

try:
    bpy.ops.wm.save_as_mainfile(filepath="/tmp/goldfish_hero.blend")
except Exception:
    pass
```