ReaLM - Clean Accuracy: leaderboard
ReaLM (2026) is a red-teaming benchmark for physical-world vision-language models on 832 driving, manipulation, grasping, physics, egocentric, diagnostic and scene cases, attacked black-box toward scenario-specific failure targets. This board is accuracy on the unperturbed cases, in percent; higher is better.
Metric: Accuracy (%) on the unperturbed images of the 832 ReaLM physical-world cases (driving, manipulation, grasping, physics, egocentric, diagnostics and scene QA; image, question and ground-truth answer), black-box one-shot evaluation at temperature 0, a Qwen3-8B judge extracting the answer; higher is better. Source: arxiv.org. Saturation forecast: Around February 2027. 13 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Qwen 3.5 122B A10B | 72.8 | #331 |
| 2 | Qwen 3.6 Flash | 71.7 | #424 |
| 3 | Qwen 3.5 27B | 71.2 | #359 |
| 4 | Qwen 3.5 9B | 68.9 | #803 |
| 5 | Gemini 3 Flash | 68.5 | #164 |
| 6 | GPT-4.1 Mini | 68.3 | #779 |
| 7 | Kimi K2.5 | 68.3 | #256 |
| 8 | Seed 2.0 Mini | 67.2 | #508 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=realm-clean-accuracy · How It Works · Data refreshed daily, snapshot 2026-11-13.