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}},
}