[whatmodel.sh](../../README.md) / MiMo-V2.6-Distill-Qwen-9B
# MiMo-V2.6-Distill-Qwen-9B
Xiaomi · released 2026-09 · 9.41B params · mit
**score 64.4** ±2.2 · #149 of 732 · from 5 benchmark results across 1 source · † no independently run results verified
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks MiMo-V2.6-Distill-Qwen-9B among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄●▅▄▄▂▁ #130 of 313 89% 4
coding ▁▁▁▃▅▇█▇●▄▃▁ #130 of 469* 58% 2
agent tasks · #130 of 313 · 89% of its score
Beats [o3](o3.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Large](jamba-1-5-large.md), [Devstral 2 (2512)](devstral-2.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Pro (split unspecified)… 44.6% ▂·▁▁▁▁▂▃●▇▂▁ beats 24/58 very hard · avg 9.6% 31% sharp · at its level · small run
SWE-bench Verified (maker) 61.1% ▁▁▁▁▁▁▂▄●▆▁▁ beats 61/138 hard · avg 23% 27% sharp · at its level · small run
Terminal-Bench 2.1 (maker) 37.1% ▁·▁·▁▁▄●▆█▅█ beats 14/63 very hard · avg 11% 24% sharp · at its level · small run
Toolathlon-Verified (maker) 35.2% ▂·●▂▅▃···▂██ beats 1/18 hard · avg 24% 7.8% blunt · at its level · small run
coding · #130 of 469 · 58% of its score
Beats [Qwen3.5 Plus](qwen3-5-plus.md), [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Solar Pro 4](solar-pro-4.md), [Gemma 4 26B-A4B](gemma-4-26b-a4b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Pro (split unspecified)… 44.6% ▂·▁▁▁▁▂▃●▇▂▁ beats 24/58 very hard · avg 9.6% 31% sharp · at its level · small run
SWE-bench Verified (maker) 61.1% ▁▁▁▁▁▁▂▄●▆▁▁ beats 61/138 hard · avg 23% 27% sharp · at its level · small run
other tests (1)
test result vs the others in its run difficulty counts why
AutomationBench v1.0.6 (maker) 30.3% ▃···▃·▃●··█▅ beats 3/8 very hard · avg 6.9% 8.4% noisy · small run
source results share of the score
XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B mo… 5 98% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 2% ▇
weighted average = 64.4
Licences: XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B model card ([MIT](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B/blob/2367e865d009c13ac81713a2878291d33ab28177/README.md))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run MiMo-V2.6-Distill-Qwen-9B (Q4_K_M, needs ~6.76 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF/tree/4371da10c84fb26da3592d4cf312d24aa82b7b65))
```sh
llama cli -hf bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~10.15 GB) `llama cli -hf ggml-org/MiMo-V2.6-Distill-Qwen-9B-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run MiMo-V2.6-Distill-Qwen-9B (Q4_K_M, needs ~6.76 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF/tree/4371da10c84fb26da3592d4cf312d24aa82b7b65))
```sh
lms get https://huggingface.co/bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~10.15 GB) `lms get https://huggingface.co/ggml-org/MiMo-V2.6-Distill-Qwen-9B-GGUF@Q8_0 -y` · 4bit (~6 GB, Mac only) `lms get https://huggingface.co/agnosticeng/MiMo-V2.6-Distill-Qwen-9B-4bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run MiMo-V2.6-Distill-Qwen-9B (Q4_K_M, needs ~6.76 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF/tree/4371da10c84fb26da3592d4cf312d24aa82b7b65))
```sh
ollama run hf.co/bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~10.15 GB) `ollama run hf.co/ggml-org/MiMo-V2.6-Distill-Qwen-9B-GGUF:Q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run MiMo-V2.6-Distill-Qwen-9B (4bit, needs ~6 GB, Mac only, from [huggingface.co/agnosticeng](https://huggingface.co/agnosticeng/MiMo-V2.6-Distill-Qwen-9B-4bit/tree/86998933799bdba2eb380c31d12810e9c289e724))
```sh
mlx_lm.chat --model agnosticeng/MiMo-V2.6-Distill-Qwen-9B-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model agnosticeng/MiMo-V2.6-Distill-Qwen-9B-4bit
```
Model repository and documentation: [huggingface.co/XiaomiMiMo](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B)
[interactive version](https://whatmodel.sh/model/mimo-v2-6-distill-qwen-9b) · [JSON](https://whatmodel.sh/api?format=json&all=1)