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math benchmark

MMVet

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

Models2
Model coverage2
MetricScore
EvidenceB

On this page

  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

MMVet Ranking

Higher score ranks better on this benchmark.

2 rows
Columns

Show columns

01ACQwen2.5 VL 72B InstructAlibaba Cloud / Qwen Team76.2%100.0%2CAug 11, 2026
02ACQwen2.5 VL 7B InstructAlibaba Cloud / Qwen Team67.1%0.0%2CAug 11, 2026

MMVet Score Distribution

A closer view of the leading scores on this benchmark.

MMVet

MMVet Highlights

The leading models and scores on this benchmark.

Rank #1Qwen2.5 VL 72B Instruct76.2%Rank #2Qwen2.5 VL 7B Instruct67.1%

What is MMVet?

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.

Family
MMVet
Modality
multimodal
Primary category
math
Score direction
higher
LLMBoard eligible
No
Evaluation key
mmvet|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about MMVet.

Which model scores highest on MMVet?

Qwen2.5 VL 72B Instruct is currently ranked first with 76.2%.

What does MMVet measure?

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.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

2 model results are currently shown.

Does this benchmark affect the overall score?

No. This benchmark is shown for reference but does not contribute to the overall score.