OpenVLM Q-Bench - How (In-context Distortion): leaderboard

Q-Bench accuracy on how questions about distortions of a particular object or region, from the OpenCompass OpenVLM Leaderboard (VLMEvalKit) on the Q-Bench dev split. Q-Bench probes low-level visual perception; each of its twelve question-type by concern cells holds 70-188 of the 1,495 dev questions. Accuracy in percent, higher is better.

Metric: Accuracy (%). Source: huggingface.co. Saturation forecast: Estimated already saturated. 160 models tracked.

Top models

#ModelScoreOverall rank
1GPT-4.1 (2025-04-14)75.9#257
2Qwen 2 VL 72B71.3#450
3InternVL3-78B70.1#361
4GPT-4.1 Mini69#363
5InternVL3-38B69#413
6Gemini 1.5 Pro69#454
7MiniCPM-V-2.667.8#827
8Qwen 2 VL 7B66.7#743
9Qwen 2.5 VL 7B66.7#641
10Grok 2 (1212)66.7#502
11Pixtral-12B65.5#805
12Aquila-VL-2B65.5#1216
13Qwen 2 VL 2B64.4#1191
14InternVL3-8B63.2#624
15Gemini 1.5 Flash63.2#620

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=openvlm-q-bench-how-in-context-distortion · How It Works · Data refreshed daily, snapshot 2026-11-05.