MMT-Bench
MMT-Bench is a comprehensive multimodal benchmark for evaluating Large Vision-Language Models towards multitask AGI. It comprises 31,325 meticulously curated multi-choice visual questions from various multimodal scenarios such as vehicle driving and embodied navigation, covering 32 core meta-tasks and 162 subtasks in multimodal understanding.
DeepSeek VL2 from DeepSeek currently leads the MMT-Bench leaderboard with a score of 0.636 across 4 evaluated AI models.
DeepSeek VL2 leads with 63.6%, followed by 
Qwen2.5 VL 7B Instruct at 63.6% and DeepSeek VL2 Small at 62.9%.
Progress Over Time
Interactive timeline showing model performance evolution on MMT-Bench
MMT-Bench Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | DeepSeek | 27B | — | — | ||
| 1 |  Alibaba Cloud / Qwen Team | 8B | — | — | ||
| 3 | DeepSeek | 16B | — | — | ||
| 4 | DeepSeek | 3B | — | — |
FAQ
Common questions about MMT-Bench.
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