Imagine an AI that can seamlessly weave together text, images, speech, and video, understanding and generating content across all these modalities. That's the promise of MIO, a groundbreaking foundation model that's pushing the boundaries of what's possible in multimodal AI. Unlike models that specialize in just one or two modalities, MIO is a true generalist. It can process and create a rich tapestry of multimedia content, opening doors to exciting new applications. How does MIO achieve this feat? It's built upon a unique foundation of multimodal tokens, discrete units of information representing different modalities. These tokens allow MIO to treat images, speech, and video much like text, enabling seamless integration and interaction. MIO is trained in four stages, starting with aligning multimodal representations with language and then progressively building more complex interleaved understanding and generation. A final fine-tuning stage polishes its performance on a range of tasks, such as image captioning, visual question answering, speech-to-text, text-to-speech, video understanding, and even interleaved video-text generation. What sets MIO apart is its ability to not just understand but also generate multimodal interleaved sequences. It can create dynamic narratives that blend text, images, and video, offering a glimpse into the future of visual storytelling. This capability also unlocks powerful reasoning abilities, allowing MIO to perform chain-of-visual-thought reasoning where it solves complex problems using a combination of visual and textual cues. Though still under development, MIO has already demonstrated competitive performance compared to existing models, and in some cases exceeding them. The open-source nature of MIO makes it an exciting platform for the research community to build upon, paving the way for truly generalist AI that can interact with and generate the world's diverse media.