MiMo-V2.5 is Xiaomi's open-weights entry in the MiMo family, distributed on Hugging Face and repositioned as a foundation for multimodal coding agents and long-context automation. The architecture is a hybrid-attention design with over one trillion total parameters and roughly 42 billion active per inference, a mixture-of-experts-style footprint that keeps per-request compute moderate while supporting a million-token context window. That unusually wide context, paired with native understanding of text, image, audio, and video inputs mapped to text outputs, lets the model ingest long documents, screen recordings, or mixed-media traces and reason across them in a single pass, which is well suited to agent scenarios that combine perception, code, and tool execution.

In practice the model fits workflow builders who want a single backbone for agentic coding, multimodal grounding, and extended retrieval, taking advantage of reasoning, tool calling, and temperature control exposed across providers. The free routing tier preserves the same context and output ceiling as the paid variants, making it a low-friction way to evaluate the model on automation pipelines or prototype multimodal assistants before committing to a metered deployment. Its open weights also make MiMo-V2.5 attractive for self-hosted experimentation, where teams can fine-tune or distill the active-parameter subset for specialized coding or agent tasks while keeping the broader trillion-parameter base available for further scaling.