math benchmark
MMVetGPT4Turbo is an MM-Vet evaluation variant that uses GPT-4 Turbo for scoring.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score74.00% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis mmvetgpt4turbo AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Qwen2-VL-72B-Instruct currently leads MMVetGPT4Turbo with 74.00%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What MMVetGPT4Turbo measures and how its scores work.
MMVetGPT4Turbo is an evaluation of large multimodal models on complicated multimodal tasks.
It measures integrated capabilities in recognition, knowledge, spatial awareness, language generation, OCR, and math.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about MMVetGPT4Turbo.
Qwen2-VL-72B-Instruct is currently ranked first with 74.00%.
The current leaders are Qwen2-VL-72B-Instruct (74.00%).
No matched official input price is currently available.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures integrated capabilities in recognition, knowledge, spatial awareness, language generation, OCR, and math.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.