MiMo V2.6 Distill Qwen 9B: Agentic Coding and Tool Use

MiMo-V2.6-Distill-Qwen-9B is an agentic model from Xiaomi MiMo, built by supervised fine-tuning of Qwen3.5-9B on data generated by the larger MiMo-V2.6 family. It targets four areas in particular: software engineering, general-purpose agent work, visual coding, and cybersecurity. Xiaomi released it as an SFT checkpoint intended as a starting point for open research into agentic reinforcement learning, so it is as useful as an RL base as it is as a served assistant.

Key Features

• Agentic by design - trained on multi-step tool-use and terminal trajectories rather than single-turn chat, so it plans, calls tools, and recovers from failed steps
• Explicit thinking mode - the chat template exposes an enable_thinking switch, and reasoning content is returned separately from the final answer
• Multimodal input - accepts images alongside text, which is what powers its visual coding ability (reading a screenshot or mockup and writing the code for it)
• Long context - a 262,144-token context window, enough to hold large repositories, long agent traces, and extended tool transcripts in a single session
• Strong code and security focus - the training mixture is weighted heavily toward code and cybersecurity tasks rather than general web text

Architecture

The model inherits the Qwen3.5 architecture: a hybrid attention stack that interleaves three linear-attention (gated delta) layers with one full-attention layer, repeating through all 32 layers. The linear layers carry a fixed-size recurrent state instead of a growing key-value cache, so memory at long context grows far more slowly than in a conventional transformer of similar depth. A vision encoder feeds image tokens into the same text backbone, giving one unified model for text and image input rather than a bolted-on adapter. Weights are published in BF16.

Benchmarks

Xiaomi reports results for this SFT checkpoint against its Qwen3.5-9B starting point in the MiMo-V2.6 technical report. The gains are largest where agentic training matters most. On SWE Pro it scores 44.6 against 32.0 for the base model, and on the internal MiMo Code evaluation it reaches 51.6 against 19.5. Cybersecurity shows the widest gap, 31.3 against 5.7 on the internal MiMo Cyber set. General agent benchmarks move similarly: AutomationBench v1.0.6 rises to 30.3 from 5.0, Terminal Bench 2.1 to 37.1 from 27.0, Toolathlon-Verified to 35.2 from 25.9, and JobBench to 18.3 from 2.6. On SWE Verified the two are close, 61.1 against 60.0, and on visual coding the model reaches 64.0 against 61.7.

Training Data

The supervised fine-tuning mixture totals 77.4 billion tokens, of which 27.2 billion are loss-bearing. It is split roughly evenly across four domains: code contributes the largest share at about 30 percent of tokens, followed by general agent data at about 28 percent, visual data at about 27 percent, and cybersecurity at about 14 percent. The visual and code slices carry the most loss-bearing tokens, which matches where the reported gains concentrate.

Use Cases

• Autonomous software engineering agents that read a repository, edit files, run tests, and iterate
• Terminal and shell agents that chain commands and handle their own error recovery
• Visual coding, turning a screenshot, design mockup, or diagram into working code
• Security research assistants for capture-the-flag style tasks and vulnerability triage
• Tool-calling backends for workflow automation across office documents and internal APIs
• A starting checkpoint for teams running their own agentic reinforcement learning experiments

Full model details are available on the MiMo-V2.6-Distill-Qwen-9B model card.