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The rapid advancement of multi-modal language models (MLLMs) like GPT-4o has propelled the development of Omni language models, designed to process and proactively respond to continuous streams of multi-modal data. Despite their potential, evaluating their real-world interactive capabilities in streaming video contexts remains a formidable challenge. In this work, we introduce OmniMMI, a comprehensive multi-modal interaction benchmark tailored for OmniLLMs in streaming video contexts. OmniMMI encompasses over 1,121 real-world interactive videos and 2,290 questions, addressing two critical yet underexplored challenges in existing video benchmarks: streaming video understanding and proactive reasoning, across six distinct subtasks. Moreover, we propose a novel framework, Multi-modal Multiplexing Modeling (M4), designed to enhance real-time interactive reasoning with minimum finetuning on pre-trained MLLMs. Extensive experimental results reveal that the existing MLLMs fall short in interactive streaming understanding, particularly struggling with proactive tasks and multi-turn queries. Our proposed M4, though lightweight, demonstrates a significant improvement in handling proactive tasks and real-time interactions.