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This paper presents a systematic frozen-feature probing study comparing vision-language models (VLMs) and video generation models (VGMs) on spatial intelligence tasks. It finds that VLMs excel at semantic tagging and instance grouping, while VGMs provide better dense geometry and camera motion signals, and a naive fusion of both yields strong performance across all axes.