math benchmark
MM-Vet is an evaluation benchmark that examines large multimodal models on complicated multimodal tasks requiring integrated capabilities. It assesses six core vision-language capabilities: recognition, knowledge, spatial awareness, language generation, OCR, and math through questions that require one or more of these capabilities.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | AC | 76.2% | 100.0% | 2 | C | |
| 02 | AC | 67.1% | 0.0% | 2 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What MMVet measures and how its scores work.
MM-Vet is an evaluation benchmark that examines large multimodal models on complicated multimodal tasks requiring integrated capabilities. It assesses six core vision-language capabilities: recognition, knowledge, spatial awareness, language generation, OCR, and math through questions that require one or more of these capabilities.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about MMVet.
Qwen2.5 VL 72B Instruct is currently ranked first with 76.2%.
MM-Vet is an evaluation benchmark that examines large multimodal models on complicated multimodal tasks requiring integrated capabilities. It assesses six core vision-language capabilities: recognition, knowledge, spatial awareness, language generation, OCR, and math through questions that require one or more of these capabilities.
Yes. Higher values rank better for this benchmark.
2 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.