Einsia发布Frontier3DBench评测3D智能体
Einsia发布Frontier3DBench，包含9类145项任务，测试11种模型—工具框架配置能否生成可编辑的Blender项目和可运行的Unreal Engine世界。评测通过渲染视图、场景检查和运行时测试考察外观、结构与行为。Einsia称，GPT-6.1-sol总体领先，GPT-6-astra在交互式世界任务中领先，MiniMax-M3.1-Flash位列场景编辑第二。
该基准考察编程智能体能否从生成单个3D资产进一步构建包含协调交互和持久状态的完整环境，其结果还显示本地机制明显优于有状态工作流。
Can the same coding agent build a detailed 3D object, assemble a scene, and make an interactive world actually work?
Our team at Einsia released Frontier3DBench to test that progression: 145 tasks across 9 categories, with 11 model–harness configurations producing editable Blender projects and runnable Unreal Engine worlds.
Evaluation covers appearance, structure, and behavior through rendered views, scene inspection, and runtime tests.
A few results:
- GPT-6.1-sol leads overall and on objects and scenes, while GPT-6-astra leads interactive worlds.
- MiniMax-M3.1-Flash ranks sixth overall but second on scene editing.
- Every configuration scores lower on reference-based reconstruction than text-driven generation in both object and scene tasks.
- Across 165 world submissions, local mechanisms score much higher than stateful workflows: 83.08 vs. 30.26 out of 100.
The interesting question is how well an agent’s strengths carry over from individual assets to environments with coordinated interactions and persistent state.
Project and examples: https://lab.einsia.ai/frontier3dbench Paper: https://lab.einsia.ai/frontier3dbench/Frontier3DBench.pdf GitHub: https://github.com/Einsia/Frontier3D
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