Recent advances in large language models (LLMs) and vision-language models (LVLMs) have shown promise across many tasks, yet their scientific reasoning capabilities remain untested, particularly in multimodal settings. We present MMSciBench, a benchmark for evaluating mathematical and physical reasoning through text-only and text-image formats, with human-annotated difficulty levels, solutions with detailed explanations, and taxonomic mappings. Evaluation of state-of-the-art models reveals significant limitations, with even the best model achieving only \textbf{63.77\%} accuracy and particularly struggling with visual reasoning tasks. Our analysis exposes critical gaps in complex reasoning and visual-textual integration, establishing MMSciBench as a rigorous standard for measuring progress in multimodal scientific understanding. The code for MMSciBench is open-sourced at GitHub, and the dataset is available at Hugging Face.
View on arXiv@article{ye2025_2503.01891,
title={ MMSciBench: Benchmarking Language Models on Multimodal Scientific Problems },
author={ Xinwu Ye and Chengfan Li and Siming Chen and Xiangru Tang and Wei Wei },
journal={arXiv preprint arXiv:2503.01891},
year={ 2025 }
}