MiMo V2.6 Flash RL: Agentic Mixture-of-Experts with a 1M-Token Context

MiMo-V2.6-Flash-RL is the efficiency-balanced checkpoint of Xiaomi's MiMo-V2.6 series, a sparse Mixture-of-Experts model with 309B total parameters and 15B activated per token. It is built for long-horizon agent work: coding across large repositories, terminal and computer-use agents, and cybersecurity tasks, with a 1-million-token context window. The series is trained around one idea, scaling reinforcement learning toward self-improvement, by growing RL compute, environment diversity, and grader compute together.

Key Features

• Long horizon - a 1M-token context window for long repositories, extended tool traces, and multi-session agent runs
• Native omnimodal design - the model carries its own vision and audio encoders alongside the text backbone, covering text, image, video, and audio in one model
• You Only RL Once - one mixed RL run across coding, general agents, visual work, and cybersecurity, rather than separate per-domain runs, so strategies transfer to harnesses never seen in training
• Groupwise agentic grading - an agentic grader compares rollouts within each group to rank passing solutions, steering the model toward shorter paths and fewer tokens per task
• Thinking mode - the model reasons before it answers, with the reasoning returned separately from the final answer; setting the chat template's enable_thinking option to false turns it off per request
• Tool calling - structured tool calls for agent frameworks

Architecture

The language backbone has 48 layers. The first uses global attention with a dense feed-forward network; the rest interleave sliding-window and global attention, 39 sliding-window layers with a 128-token window and 9 global layers, and all of them use sparse Mixture-of-Experts feed-forward networks that route each token to 8 of 256 experts. Because only the 9 global layers keep a key-value cache that grows with context length, memory at long context grows far more slowly than in a conventional transformer of similar depth. The expert weights are stored in MXFP4 with a BF16 router. A 681M-parameter vision encoder and a two-stage audio encoder feed the same backbone, and the checkpoint also ships a 5-layer speculative drafter that predicts seven tokens per forward pass.

Benchmarks

Xiaomi reports the Flash checkpoint close behind the larger MiMo-V2.6 Pro on most evaluations while activating far fewer parameters per token. On code agent tasks it scores 67.9 on DeepSWE v1.1, 61.2 on MiMo Code Bench, and 26.0 on ProgramBench. On general agent tasks it reaches 87.6 on Terminal Bench 2.1, 80.8 on OSWorld-Verified, 73.6 on Toolathlon-Verified, 61.2 on JobBench, and 52.3 on AutomationBench v1.0.6. On cybersecurity it posts 95.1 on CyberGym and 77.2 on MiMo Cyber Bench, and on visual agent work it scores 71.5 on MiMo VisualCoding. The previous MiMo-V2.5 Pro scored 19.0 on DeepSWE v1.1 and 65.2 on Terminal Bench 2.1.

Use Cases

• Long-horizon coding agents that navigate large repositories, run terminal tools, and sustain multi-step engineering sessions
• Computer-use and tool-calling agents for desktop, office, and job workflows
• Security research and vulnerability analysis
• Long-context analysis where full repositories, tool traces, or multi-session histories must stay in the prompt

Full model details are available on the MiMo-V2.6-Flash-RL model card. The larger sibling is MiMo-V2.6-Pro-RL.