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XiaomiMiMo
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Model Details
Model Provider
XiaomiMiMo
Model Tree
Qwen/Qwen3.5-9B
Input Modalities
Output Modalities
Supported Functionality
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.
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 |
For text generation, use a recent SGLang build with Qwen3.5 support. The checkpoint includes its tokenizer and MiMo v2.6 chat template.
bash
sglang serve \ --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B \ --reasoning-parser mimo \ --host 0.0.0.0 \ --port 30000Query the endpoint with thinking explicitly enabled:
python
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].messageprint("Thinking:", getattr(message, "reasoning_content", "") or "")print("Answer:", message.content or "")bibtex
@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}},}| Total |
| 77.4 |
| 100.0 |
| 27.2 |