MiMo-V2.6-Distill-Qwen-9B

MiMo-V2.6-Distill-Qwen-9B is a 9B agentic model developed by Xiaomi MiMo through supervised fine-tuning of Qwen3.5-9B on MiMo-generated data. It covers coding, general-purpose agent tasks, visual coding, and cybersecurity. We release this SFT checkpoint as a starting point for open research in agentic reinforcement learning.

Evaluation

Results for the released SFT checkpoint, as reported in the MiMo-V2.6 technical report.

Domain	Benchmark	Metric	Qwen3.5-9B	MiMo-V2.6-Distill-Qwen-9B (SFT)
Code	SWE Verified	avg@3	60.0	61.1
Code	SWE Pro	avg@3	32.0	44.6
Code	MiMo Code (mini)†	avg@3	19.5	51.6
Cyber	MiMo Cyber (mini)†	avg@3	5.7	31.3
General	AutomationBench v1.0.6	avg@1	5.0	30.3
General	Terminal Bench 2.1	avg@1	27.0	37.1
General	Toolathlon-Verified	avg@1	25.9	35.2
General	OfficeQA	avg@1	9.0	19.5
General	JobBench	avg@1	2.6	18.3
General	MiMo General (mini)†	avg@1	28.5	62.2
Visual	MiMo Visual Coding (mini)†	avg@1	61.7	64.0

† Internal evaluation sets.

Training Data

The weighted SFT data mixture contains 77.4B total tokens, including 27.2B loss-bearing tokens.

Domain	Total tokens (B)	Token share (%)	Loss-bearing tokens (B)
Code	23.2	29.9	7.3
Cyber	11.0	14.2	4.8
General	22.0	28.5	5.7
Visual	21.2	27.4	9.4
Total	77.4	100.0	27.2

Quickstart

For text generation, use a recent SGLang build with Qwen3.5 support. The checkpoint includes its tokenizer and MiMo v2.6 chat template.

sglang serve \
  --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B \
  --reasoning-parser mimo \
  --host 0.0.0.0 \
  --port 30000

Query the endpoint with thinking explicitly enabled:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY",
)

response = client.chat.completions.create(
    model="XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
    messages=[
        {"role": "user", "content": "What is 15% of 240?"}
    ],
    max_tokens=2048,
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)

message = response.choices[0].message
print("Thinking:", getattr(message, "reasoning_content", "") or "")
print("Answer:", message.content or "")

Citation

@misc{mimo2026v26,
  title={MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement},
  author={{Xiaomi MiMo Team}},
  year={2026},
  howpublished={\url{https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL}},
}