Xiaomi MiMo-V2.6 Overtakes DeepSeek and Google in Open AI

An open release built for flagship AI workloads

Xiaomi has released MiMo-V2.6 under the MIT license, making this much more than a routine model launch. The family includes MiMo-V2.6-Pro, the lighter MiMo-V2.6-Flash, and MiMo-V2.6-Pro-UltraSpeed, a version designed for high-speed inference scenarios where latency matters.

The important detail is that Xiaomi has published both the weights and its full large-scale Reinforcement Learning training process on Hugging Face and ModelScope. For teams building agents, internal assistants, coding systems, or multimodal products, that provides stronger inspectability, easier adaptation, and less dependence on closed API platforms.

What the family includes

• MiMo-V2.6-Pro: the flagship model for complex reasoning, code, and multimodal tasks.
• MiMo-V2.6-Flash: a lighter 309 billion parameter option for more efficient deployment.
• MiMo-V2.6-Pro-UltraSpeed: a version optimized for rapid query serving.

A 46.32 score reshapes the DeepSeek and Google race

MiMo-V2.6-Pro reaches 46.32 on the Artificial Analysis Intelligence Index, placing it first among all open-weight models. On that benchmark, Xiaomi moves ahead of DeepSeek V4.1 Flash and Pro, xAI Grok 4.6, and Google Gemini 3.8 Flash, while closing in on leading closed commercial systems.

The ranking matters because it shifts attention from raw parameter counts to measurable capability. An open model is no longer automatically a compromise for long-document analysis, code generation, image-aware workflows, or specialized enterprise agents.

1.02 trillion parameters, with 42 billion active per token

MiMo-V2.6-Pro uses a Sparse Mixture-of-Experts architecture. Rather than activating its entire capacity for every input unit, it selects a relevant set of experts. That creates a useful balance between 1.02 trillion total parameters and 42 billion active parameters per token, balancing capability with inference cost.

Its native multimodality extends beyond text chat. MiMo-V2.6 handles text, code, images, audio, and video, while its 1 million token context window can accommodate large technical archives, long recordings, extensive codebases, and collections of internal documentation within one working session.

Where the practical value appears

• Developers can inspect large repositories while connecting code, documentation, and visual assets.
• Businesses can build local RAG systems on sensitive files without sending the full context to an external provider.
• Product teams can combine voice, images, video, and text in one AI workflow.

From the server rack to HyperOS, phones, and cars

MiMo-V2.6 is positioned as a shared intelligence layer for HyperOS. Xiaomi has an unusual advantage: it controls the route from models and cloud services to Xiaomi smartphones, HyperOS tablets, smart watches, smart home devices, and the Xiaomi SU7 electric vehicle. That can enable more connected voice controls, cross-device automations, and context-aware services on the move.

Open weights do not mean a 1.02T model will run on a phone. They do create deployment choices: Flash variants, quantized releases, and specialized serving stacks can be placed where they fit best. For local hosting of smaller variants or supporting models, fast NVMe SSD drives, sufficient system memory, cooling pads for sustained loads, and Wi-Fi 7 routers for quick access to a home AI server are relevant. In mobile workflows, a Xiaomi smartphone, GaN charger, and HyperOS tablet can become parts of one more fluid work setup.

Three practical takeaways

• For developers: the MIT license supports experimentation, fine-tuning, and commercial deployments.
• For organizations: a 1M token context window is valuable for contracts, knowledge bases, engineering files, and audit records.
• For Xiaomi users: the models provide a foundation for more capable HyperOS, smart home, and Xiaomi SU7 features.