Spatial intelligence is essential for multimodal large language models (MLLMs) operating in the physical world. Existing benchmarks, however, probe only single-image relations and thus fail to assess the multi-image spatial reasoning that real deployments demand. We introduce **MMSI-Bench**, a VQA benchmark dedicated to multi-image spatial intelligence. Six 3D-vision researchers spent more than 300 hours 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 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 insights for advancing multi-image spatial intelligence.

Spatial intelligence is essential for multimodal large language models (MLLMs) operating in the physical world. Existing benchmarks, however, probe only single-image relations and thus fail to assess the multi-image spatial reasoning that real deployments demand. We introduce**MMSI-Bench**, a VQA benchmark dedicated to multi-image spatial intelligence. Six 3D-vision researchers spent more than 300 hours 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 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.


MMSI-Bench systematically categorizes multi-image spatial reasoning tasks into ten basic types and one multi-step reasoning category, covering three fundamental spatial elements: camera (the agent), object (entities in the environment), and region (semantic areas like rooms). The six positional relationship categories include camera-camera, camera-object, camera-region, object-object, object-region, and region-region. In addition, there are two types of attribute reasoning (measurement and appearance), two types of motion reasoning (camera motion and object motion), and a multi-step reasoning category for more complex tasks. Each question requires information from multiple images, aiming to comprehensively evaluate a model’s ability to understand and reason about spatial relationships, attributes, and movements across images.

MMSI-Bench systematically categorizes multi-image spatial reasoning tasks into ten basic types and one multi-step reasoning category, covering three fundamental spatial elements: camera (the agent), object (entities in the environment), and region (semantic areas like rooms). The six positional relationship categories include camera-camera, camera-object, camera-region, object-object, object-region, and region-region. In addition, there are two types of attribute reasoning (measurement and appearance), two types of motion reasoning (camera motion and object motion), and a multi-step reasoning category for more complex tasks.