Benchmarks
326 sources · 1,256 models × 2,956 benchmarks · 154,484 scoresFrontier3DBench (Navers Lab, Einsia.AI and Tsinghua University) tests coding agents on building editable 3D projects from text, images or briefs: objects and scenes in Blender, and interactive open worlds in Unreal Engine. Each submission is rendered, inspected and run, then scored for visual fidelity, structural consistency and task fulfillment. Its 145 tasks span 9 categories, and Total weights objects 30%, scenes 30% and open worlds 40%. Each model runs in its developer's harness, or in Claude Code where it has none, at its highest available effort.
134th strongest of 779 fitted benchmarks
Signal-to-noise +11.88
253rd hardest of 779 fitted benchmarks
Capability needed 159.8
| # | Model | Setting | Score | Source |
|---|---|---|---|---|
| 1 | GPT-6.1 Sol | max · Codex | 66.0 | |
| 2 | GPT-6 Astra | max · Codex | 63.9 | |
| 3 | Claude Opus 5.5 | max · Claude Code | 62.3 | |
| 4 | Kimi K3.1 Pro | max · Kimi CLI | 56.0 | |
| 5 | MiniMax M3.1 Flash | max · Claude Code | 52.5 | |
| 6 | Kimi K3 | max · Kimi CLI | 49.2 | |
| 7 | GPT-6 Luna | max · Codex | 48.6 | |
| 8 | GLM-5.3 Flash | max · ZCode | 47.6 |