The MiMo-V2.5 series represents a significant architectural evolution, consolidating previously separate text, code, and multimodal capabilities into a unified, high-performance package. Designed specifically for intelligent agent scenarios, the model family is built to handle complex, long-horizon tasks that require deep reasoning and autonomous execution. By integrating native understanding of visual and auditory inputs, the model allows users to process diverse media—such as summarizing video tutorials, extracting action items from meeting recordings, or analyzing images for practical suggestions—within a single, streamlined workflow.

Developed under the leadership of Luo Fuli, the MiMo-V2.5 lineage emphasizes robust agentic performance, with the Pro variant specifically optimized for demanding software engineering and multi-step tool-calling workflows. The model has demonstrated competitive results on benchmarks such as SWE-bench Pro, ClawEval, and GDPVal, positioning it as a strong alternative for developers building persistent AI agents that require memory and reusable skill sets. With its expansive context window and focus on autonomous task completion, the series is engineered to support sophisticated agent frameworks, providing a scalable foundation for future-facing, cross-platform automation.