MMBench
MMBench is a multimodal benchmark jointly released by Shanghai AI Laboratory and several universities, using about 3,000 multiple-choice questions across 20 fine-grained abilities to evaluate vision-language models.
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Ideal for researchers and developers who need fine-grained evaluation of vision-language models; not for text-only benchmarking or users without a technical setup.
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What is MMBench
MMBench is a multimodal benchmark jointly released by Shanghai AI Laboratory and several universities, using about 3,000 multiple-choice questions across 20 fine-grained abilities to evaluate vision-language models.
Key features of MMBench
- Fine-grained evaluation of multimodal models across perception and cognition
- Submitting test results to the official leaderboard for comparison
- Running standardized evaluation of new models with VLMEvalKit
- Supporting academic research and teaching on multimodal AI
Good for
- Twenty fine-grained ability dimensions for high-resolution evaluation
- Circular evaluation strategy reduces answer noise and position bias
- Bilingual Chinese-English datasets plus the official VLMEvalKit toolkit
Watch out
- Multiple-choice format cannot capture open-ended generation quality
- Requires setting up an evaluation environment, which raises the entry bar
- Leaderboard scores need careful interpretation in real task contexts
How to use MMBench
- Install the VLMEvalKit evaluation toolkit via pip
- Download the dataset in the needed format from the official GitHub repo
- Load and inspect samples with VLMEvalKit scripts
- Run model inference to generate prediction files
- Submit test-set predictions to the leaderboard to get per-dimension scores
Who MMBench is for
Difficulty: Intermediate
- Fine-grained evaluation of multimodal models across perception and cognition
- Submitting test results to the official leaderboard for comparison
- Running standardized evaluation of new models with VLMEvalKit
- Supporting academic research and teaching on multimodal AI
FAQ
How large is the MMBench dataset?
It contains about 3,000 multiple-choice questions covering 20 fine-grained ability dimensions, in both English and Chinese versions.
How does MMBench prevent lucky guesses?
It uses a circular evaluation strategy that shuffles answer options multiple times to verify consistency, and matches model replies to options with ChatGPT.
How do I submit results to the leaderboard?
Run inference on the test set to generate a prediction file, then upload it after logging in to the leaderboard; scores per dimension are computed automatically.
Sources and verification
Sources: mmbench.opencompass.org.cn (opens in a new tab)
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