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MiMo-V2.6-Flash is an open-source foundation model developed by Xiaomi. Built on a Mixture-of-Experts architecture with 309B total parameters and 15B activated per token, it employs a hybrid attention mechanism for...
MiMo-V2.6-Pro is the flagship foundation model developed by Xiaomi. Built at a scale of over 1T parameters, it is designed to push the ceiling of capability for the most demanding...
MiMo-V2.6-Pro-UltraSpeed is the fast speed edition of Xiaomi's flagship foundation model, MiMo-V2.6-Pro. Built from the same 1T MiMo-V2.6-Pro checkpoint, it matches the original model in quality while delivering roughly 10x...
MiMo-V2.5 is a native omnimodal model with strong agentic capabilities, supporting text, image, video, and audio understanding within a unified architecture. Built upon the MiMo-V2-Flash backbone and extended with dedicated vision and audio encoders, it delivers robust performance across multimodal perception, long-context reasoning, and agentic workflows. Key features include:
For post-training, MiMo-V2.5-Pro adopts the three-stage post-training paradigm introduced in MiMo-V2-Flash to achieve exceptional performance. The paradigm begins with Supervised Fine-Tuning (SFT) to build strong, foundational instruction-following skills using curated data pairs. Next, in the Domain-Specialized Training stage, diverse teacher models — ranging from math and safety to complex agentic tool-use — are individually optimized using domain-specific RL rewards. Finally, the process culminates in Multi-Teacher On-Policy Distillation (MOPD). Through dynamic on-policy RL, the single student model iteratively learns from its own outputs, continuously receiving precise token-level guidance from the expert teachers to seamlessly integrate broad capabilities.
**MiMo-V2-Flash** is a Mixture-of-Experts (MoE) language model with **309B total parameters** and **15B active parameters**. Designed for high-speed reasoning and agentic workflows, it utilizes a novel hybrid attention architecture and Multi-Token Prediction (MTP) to achieve state-of-the-art performance while significantly reducing inference costs.