MM-Vet, with open-ended vision-language questions targeting at evaluating integrated capabilities, has become one of the most popular benchmarks for large multimodal model evaluation. MM-Vet assesses six core vision-language (VL) capabilities: recognition, knowledge, spatial awareness, language generation, OCR, and math. However, its question format is restricted to single image-text pairs, lacking the interleaved image and text sequences prevalent in real-world scenarios. To address this limitation, we introduce MM-Vet v2, which includes a new VL capability called "image-text sequence understanding", evaluating models' ability to process VL sequences. Furthermore, we maintain the high quality of evaluation samples while further expanding the evaluation set size. Using MM-Vet v2 to benchmark large multimodal models, we found that Claude 3.5 Sonnet is the best model with a score of 71.8, slightly outperforming GPT-4o which scored 71.0. Among open-weight models, InternVL2-Llama3-76B leads with a score of 68.4. The code, data, and leaderboard are accessible at https://github.com/yuweihao/MM-Vet.
@article{arxiv.2408.00765,
title = {MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities},
author = {Weihao Yu and Zhengyuan Yang and Lingfeng Ren and Linjie Li and Jianfeng Wang and Kevin Lin and Chung-Ching Lin and Zicheng Liu and Lijuan Wang and Xinchao Wang},
journal= {arXiv preprint arXiv:2408.00765},
year = {2024}
}
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Code, data and leaderboard: https://github.com/yuweihao/MM-Vet