MIBench-mini (Multimodal Interaction) - Vision-Centric: leaderboard

Metric: Accuracy (%) on the vision-centric samples (the answer must come from the image while the text context varies) on MIBench-mini, the paper's subset on which the closed-source models were also evaluated (multiple-choice image-text tasks; vision- and text-centric samples average several context variations); higher is better. Source: arxiv.org. Saturation forecast: Around 2028. 29 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 3 Pro (Preview)71.7#64
2Gemini 2.5 Pro69.4#145
3Qwen 3 VL 235B A22B Instruct68#264
4Qwen 2.5 VL 72B Instruct65.4#364
5GPT-5.1 (2025-11-13)64.4#97
6GPT-5.264#105
7O3 (2025-04-16)63.6#117
8Qwen 3 VL 8B Instruct63.2#401
9Qwen 2.5 VL 32B Instruct62.8#443
10Claude Sonnet 4.559.6#138
11Qwen 2 VL 7B Instruct59.4#816
12InternVL3-8B59.2#606
13GPT-4o (2024-11-20)57.4#369
14Qwen 2.5 VL 7B Instruct55.8#643
15InternVL2-8B49.6#826

No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #8 Claude Fable 5.1.

Interactive version: theaggregate.ai/benchmark?slug=mibench-mini-multimodal-interaction-vision-centric · How It Works · Data refreshed daily, snapshot 2026-10-11.