3D-DefectBench - Geometry (Silver Labels): leaderboard

3D-DefectBench measures how well vision-language models spot defects in generated 3D assets from rendered multi-view images, marking each defect type present or absent. This board covers the five geometry defects, scored against crowd majority labels on the held-out asset split. Scores are the Matthews correlation coefficient, macro-averaged over defect types, so zero means chance agreement and one means perfect agreement.

Metric: Macro MCC (Matthews correlation from -1 to 1, macro-averaged over the five geometry defect types) of the model's binary per-defect verdicts against crowd majority labels on the 549-asset silver holdout; the fixed c004 design: six-view oblique RGB turntable renders of each text-to-3D asset and a rubric-guided checklist prompt. Source: arxiv.org. Saturation forecast: Around 2029. 12 models tracked.

Top models

#ModelScoreOverall rank
1GPT-5.40.24#123
2Gemini 3.1 Flash Lite0.23#364
3GPT-5 Mini0.21#348
4Gemini 2.5 Pro0.21#266
5Claude Opus 4.70.21#79
6Gemini 3.1 Pro (Preview)0.21#94
7Claude Sonnet 4.60.18#161
8GPT-4o0.17#741
9Qwen 3.5 397B A17B0.15#246
10Claude Haiku 4.50.12#568
11Mistral Small 3.10.09#1689
12Qwen 2.5 VL 7B0.03#1787

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=3d-defectbench-geometry-silver-labels · How It Works · Data refreshed daily, snapshot 2026-11-13.