What it measures
Evaluates integrated vision-language capabilities combining recognition, OCR, spatial awareness, knowledge, math, and multi-image sequential reasoning.
Reading the measurement
Study what information the model receives and which inference the answer requires. A question may mix recall, visual perception, deduction and search. Use task breakdowns to find the mechanism behind a headline gain.
Published evaluation
MM-Vet total; MM-Vet · original integrated-capability evaluation. The source’s task split, model configuration and scoring units are retained.
Where this fits in an evaluation suite
Ask what the task actually requires: recall, deduction, search, or a mixture. Separate closed-book reasoning from retrieval and tool-assisted problem solving.
Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.
A reproducible comparison
MM-Vet total; MM-Vet · original integrated-capability evaluation. The source’s task split, model configuration and scoring units are retained.
Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.
PRIMARY EVIDENCE & FURTHER READING
- MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities — published result table ↗
All 8 authors
Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, Lijuan Wang
Reviewed 2026-10-26. Explanation and interpretation by RLTasks. Benchmark ownership remains with its original authors.