Spatial intelligence is essential for multimodal large language models (MLLMs) operating in the complex physical world. Existing benchmarks, however, probe only single-image relations and thus fail to assess the multi-image spatial reasoning that real-world deployments demand. We introduce MMSI-Bench, a VQA benchmark dedicated to multi-image spatial intelligence. Six 3D-vision researchers spent more than 300 hours meticulously crafting 1,000 challenging, unambiguous multiple-choice questions from over 120,000 images, each paired with carefully designed distractors and a stepwise reasoning process. We conduct extensive experiments and evaluate 37 open-source and proprietary MLLMs, observing a wide gap: the strongest open-source model attains roughly 30% accuracy and OpenAI's GPT-5 reasoning model reaches 40%, while humans score 97%. These results underscore the challenging nature of MMSI-Bench and the substantial headroom for future research. Leveraging the annotated reasoning processes, we also provide an automated error analysis pipeline that diagnoses four dominant failure modes, including (1) grounding errors, (2) overlap-matching and scene-reconstruction errors, (3) situation-transformation reasoning errors, and (4) spatial-logic errors, offering insights for advancing spatial intelligence. Project page: https://runsenxu.com/projects/MMSI_Bench .
@article{arxiv.2505.23764,
title = {MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence},
author = {Sihan Yang and Runsen Xu and Yiman Xie and Sizhe Yang and Mo Li and Jingli Lin and Chenming Zhu and Xiaochen Chen and Haodong Duan and Xiangyu Yue and Dahua Lin and Tai Wang and Jiangmiao Pang},
journal= {arXiv preprint arXiv:2505.23764},
year = {2026}
}
Comments
ICLR 2026 Camera ready. 38 pages. Project page: https://runsenxu.com/projects/MMSI_Bench