MIBench (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 the full MIBench (multiple-choice image-text tasks; for vision- and text-centric samples the question is posed under several context variations and per-sample accuracy averages them); higher is better. Source: arxiv.org. Saturation forecast: Around January 2027. 22 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Qwen 3 VL 235B A22B Instruct | 68.48 | #264 |
| 2 | Qwen 2.5 VL 72B Instruct | 65.15 | #364 |
| 3 | Qwen 3 VL 8B Instruct | 64.08 | #401 |
| 4 | Qwen 2.5 VL 32B Instruct | 63.55 | #443 |
| 5 | InternVL3-8B | 61.93 | #606 |
| 6 | Qwen 2 VL 7B Instruct | 60.29 | #816 |
| 7 | Qwen 2.5 VL 7B Instruct | 59.61 | #643 |
| 8 | InternVL2-8B | 53.29 | #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-multimodal-interaction-vision-centric · How It Works · Data refreshed daily, snapshot 2026-10-11.