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.
Qwen2.5 VL 72B Instruct from Alibaba Cloud / Qwen Team currently leads the MMVet leaderboard with a score of 0.762 across 2 evaluated AI models.
What MMVet measures
MMVet is a multimodal benchmark that evaluates large language models on math, multimodal, reasoning, spatial reasoning, general, and vision tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.7, with the leader reaching 0.8.
Compare leaders on the best AI for math, best AI for multimodal, best AI for reasoning, best AI for spatial reasoning, best AI for general and best AI for vision leaderboards.
Publication
- Paper
- MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
- Authors
- Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, and 4 others
- Published
- arXiv
- 2308.02490
Abstract
We propose MM-Vet, an evaluation benchmark that examines large multimodal models (LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing abilities, such as solving math problems written on the blackboard, reasoning about events and celebrities in news images, and explaining visual jokes. Rapid model advancements pose challenges to evaluation benchmark development. Problems include: (1) How to systematically structure and evaluate the complicated multimodal tasks; (2) How to design evaluation metrics that work well across question and answer types; and (3) How to give model insights beyond a simple performance ranking. To this end, we present MM-Vet, designed based on the insight that the intriguing ability to solve complicated tasks is often achieved by a generalist model being able to integrate different core vision-language (VL) capabilities. MM-Vet defines 6 core VL capabilities and examines the 16 integrations of interest derived from the capability combination. For evaluation metrics, we propose an LLM-based evaluator for open-ended outputs. The evaluator enables the evaluation across different question types and answer styles, resulting in a unified scoring metric. We evaluate representative LMMs on MM-Vet, providing insights into the capabilities of different LMM system paradigms and models.
Qwen2.5 VL 72B Instruct leads with 76.2%, followed by Qwen2.5 VL 7B Instruct at 67.1%.
Progress Over Time
Interactive timeline showing model performance evolution on MMVet
MMVet Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 72B | — | — | ||
| 2 | Alibaba Cloud / Qwen Team | 8B | — | — |
FAQ
Common questions about MMVet.
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