math benchmark
MM-Vet evaluation using GPT-4 Turbo for scoring. This variant of MM-Vet examines large multimodal models on complicated multimodal tasks requiring integrated capabilities across six core vision-language abilities: recognition, knowledge, spatial awareness, language generation, OCR, and math.
Updated Aug 7, 2026
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | AC | 74.0% | 100.0% | 1 | C |
The top published results on this benchmark's own scale.
Definition and scoring fields from the benchmark registry.
MM-Vet evaluation using GPT-4 Turbo for scoring. This variant of MM-Vet examines large multimodal models on complicated multimodal tasks requiring integrated capabilities across six core vision-language abilities: recognition, knowledge, spatial awareness, language generation, OCR, and math.
Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
Common questions about MMVetGPT4Turbo.
Qwen2-VL-72B-Instruct is currently ranked first with 74.0%.
MM-Vet evaluation using GPT-4 Turbo for scoring. This variant of MM-Vet examines large multimodal models on complicated multimodal tasks requiring integrated capabilities across six core vision-language abilities: recognition, knowledge, spatial awareness, language generation, OCR, and math.
Yes. Higher values rank better for this benchmark.
1 unique published model results are currently shown.
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.