OpenVLM Q-Bench - What (In-context Others): leaderboard

Q-Bench accuracy on what and which questions about other low-level attributes 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 Mini92.2#363
2GPT-4.1 Nano92.2#730
3Gemma 3 27B92.2#606
4InternVL3-8B91.1#624
5GPT-4.1 (2025-04-14)91.1#257
6Pixtral-12B91.1#805
7Gemini 1.5 Flash91.1#620
8InternVL3-38B90#413
9Qwen 2 VL 72B90#450
10Gemma 3 12B90#674
11InternVL3-78B88.9#361
12Qwen 2 VL 7B88.9#743
13InternVL3-14B88.9#506
14Grok 2 (1212)88.9#502
15Gemini 1.5 Pro87.8#454

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-what-in-context-others · How It Works · Data refreshed daily, snapshot 2026-11-05.