MiMo-V2.6-Pro

Overview. MiMo-V2.6-Pro is Xiaomi's flagship reasoning model in the MiMo-V2.6 line, released in September 2026 as an open-weight checkpoint under MIT. It is a sparse Mixture-of-Experts model with roughly 1.02T total parameters and about 42B activated per token, and it accepts text, image, video and audio input while returning text. Developers reach for it when they need a long-context reasoning model they can also self-host, since the weights are published openly.

Key strengths. The model's design centre is reasoning depth paired with a 1,000,000-token context window, which makes it suitable for long source sets rather than single-shot prompts. Its released checkpoint is the '-RL' variant, trained with scaled mixed-GRPO reinforcement learning, and Xiaomi's API exposes a thinking toggle with deep thinking on by default. On the Artificial Analysis Intelligence Index it scores 46, placing it in the upper-mid band rather than at the frontier. The architecture routes each token through 8 of 384 experts, which keeps active computation far below its total parameter count.

When to use it. Use this model for analysis over long inputs — extended document sets, large codebases, or multi-turn investigations — where the 1M-token context and reasoning trace both matter. It suits tasks that benefit from a written chain of reasoning before an answer, and the thinking toggle lets you turn that off when you want a faster, direct response. The combination of open weights and a permissive licence also makes it a reasonable choice when the workload must run inside your own inference environment. Its omnimodal input handling fits workflows that mix text with images, audio or video, though the response is always text.

Limitations. Output is text only, so it cannot generate images, audio or speech. Tool calling and structured output are supported — the vendor's serving recipes ship a MiMo tool-call parser with auto tool choice, and the API exposes tools, tool_choice and response_format — though no native web search is documented. While thinking mode is enabled — which is the default — custom temperature and top_p values are overridden rather than honoured. Xiaomi does not publish an official training-data cutoff, and the standard serving mode is throughput-oriented: Xiaomi sells a separate low-latency serving option for time-sensitive workloads.

Sovereignty note. MiMo-V2.6-Pro comes from Xiaomi, a China-based creator, so it falls outside US CLOUD Act reach. The weights are released under MIT and can be self-hosted on your own infrastructure, though endpoint-level data residency depends on the host serving the model.