This research evaluates MiniMax-H3, an omni-modal generative model utilizing a shared latent framework to unify text, image, video, and audio modeling. To assess whether multimodal alignment enhances physical world reasoning, the authors developed a novel evaluation framework consisting of 517 instances across four distinct reasoning scenarios: implicit prompts with multiple frames, audio-image integration, prefix-videos, and audio-video inputs. This methodology forces the model to synthesize complementary semantic cues from partial evidence across modalities to infer latent event states and future dynamics. Experimental results show that MiniMax-H3 achieved a total success rate of 41.97%. Performance varied significantly across the tested dimensions; the model demonstrated its strongest capability in Video-based Decision Reasoning, achieving a 56.00% success rate. Conversely, it struggled most with Audio-based Disambiguation Reasoning, which yielded a success rate of only 27.40%. These findings suggest that while unified architectures facilitate diverse content generation, achieving seamless multimodal integration for complex physical reasoning remains a significant technical challenge.