MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence
Authors: name: Sihan Yang, name: Runsen Xu, name: Yiman Xie, name: Sizhe Yang, name: Mo Li, name: Jingli Lin, name: Chenming Zhu, name: Xiaochen Chen, name: Haodong Duan, name: Xiangyu Yue, name: Dahua Lin, name: Tai Wang, name: Jiangmiao Pang

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 step-by-step reasoning process. We
conduct extensive experiments and thoroughly evaluate 34 open-source and
proprietary MLLMs, observing a wide gap: the strongest open-source model
attains roughly 30% accuracy and OpenAI's o3 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 valuable insights for
advancing multi-image spatial intelligence. Project page:
https://runsenxu.com/projects/MMSI_Bench .