  Large language models (LLMs) and vision-language models (VLMs) have
demonstrated remarkable performance across a wide range of tasks and domains.
Despite this promise, spatial understanding and reasoning -- a fundamental
component of human cognition -- remains under-explored. We develop novel
benchmarks that cover diverse aspects of spatial reasoning such as relationship
understanding, navigation, and counting. We conduct a comprehensive evaluation
of competitive language and vision-language models. Our findings reveal several
counter-intuitive insights that have been overlooked in the literature: (1)
Spatial reasoning poses significant challenges where competitive models can
fall behind random guessing; (2) Despite additional visual input, VLMs often
under-perform compared to their LLM counterparts; (3) When both textual and
visual information is available, multi-modal language models become less
reliant on visual information if sufficient textual clues are provided.
Additionally, we demonstrate that leveraging redundancy between vision and text
can significantly enhance model performance. We hope our study will inform the
development of multimodal models to improve spatial intelligence and further
close the gap with human intelligence.
