MMErroR - Error Presence Detection - Chemistry & Materials: leaderboard
MMErroR (Shi et al., ACL 2026) error presence detection on its Chemistry & Materials samples: the model must flag an image-grounded reasoning chain as erroneous and name the correct type of its single injected error. Accuracy (%); higher is better.
Metric: Error Presence Detection Accuracy (%). Source: arxiv.org. Saturation forecast: Around February 2027. 12 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Gemini 3 Pro (Preview) | 66.67 | #111 |
| 2 | GPT-5.2 (xHigh) | 61.64 | #172 (GPT-5.2) |
| 3 | Seed 2.0 Pro | 56.6 | #209 |
| 4 | Kimi K2.5 | 49.69 | #252 |
| 5 | Qwen 3 VL 32B (Thinking) | 49.69 | #557 |
| 6 | Claude Opus 4.5 | 47.8 | #152 |
| 7 | Grok 4 | 45.91 | #306 |
| 8 | Qwen-VL-Max | 42.14 | #1226 |
| 9 | Qwen 3 VL 32B Instruct | 27.04 | #582 |
| 10 | GLM-4.6V | 23.27 | #698 |
| 11 | Llama 4 Scout Instruct | 15.09 | #1532 |
| 12 | Llama 4 Maverick Instruct | 7.55 | #1125 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=mmerror-error-presence-detection-chemistry-materials · How It Works · Data refreshed daily, snapshot 2026-11-12.