[whatmodel.sh](../../README.md) / MiMo-V2.6-Pro
# MiMo-V2.6-Pro
Xiaomi · released 2026-09 · 1020B params (42B active) · mit
**score 77.4** ±1.0 · #19 of 732 · from 13 benchmark results across 2 sources
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-Pro among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅▄●▂▁ #19 of 313 53% 7
coding ▁▁▁▃▅▇█▇▅▄●▁ #24 of 469* 21% 2
chat & writing ▁▁▁▃▆▄▆█▆▇▇● #24 of 402* 11% 1
vision & documents ▁▁▂▂▁▃▄▂▆●█▄ #53 of 153* 7.3% 1
agent tasks · #19 of 313 · 53% of its score
Beats [Muse Wonder](muse-wonder.md), [GPT-5.5](gpt-5-5.md) here, though they rank higher overall. Trails [Claude Opus 4.8](claude-opus-4-8.md), [DeepSeek V4.1 Flash 1017](deepseek-v4-1-flash-1017.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena Agent (October 2026 aggrega… 0.0277 IPS ▁▁▁▃▃▂●▁▂▂▁▁ beats 41/59 avg -0.379 IPS 28% sharp
Terminal-Bench 4.0 (maker) 34.9% ▁·▁··▁▆▆▅●█▃ beats 17/33 very hard · avg 0.5% 10% sharp · at its level · small run
Terminal-Bench 2.1 (maker) 89.9% ▁·▁·▁▁▄▃▆█▅● beats 61/63 hard · avg 23% 5.0% sharp · small run
OSWorld-Verified (maker) 82.0% █▃·▂▅▂▃▂▅▃●▆ beats 23/27 hard · avg 24% 4.0% small run
Agents' Last Exam (metric/harness… 31.6% ▂·▂·█●▂·▂▂▄▄ beats 6/14 very hard · avg 6.5% 2.8% at its level · small run
Toolathlon-Verified (maker) 76.9% ▂·▂▂▅▃···▂●█ beats 13/18 hard · avg 32% 1.6% blunt · at its level · small run
DeepSWE v1.1 (resolved) (maker) 71.9% ▂····▂▁▁▆█●█ beats 23/31 hard · avg 19% 1.2% at its level · noisy · small run
coding · #24 of 469 · 21% of its score
Beats [GPT-5.6 Sol](gpt-5-6-sol.md), [GPT-5.5](gpt-5-5.md) here, though they rank higher overall. Trails [Grok 4.7](grok-4-7.md), [Qwen3.8-Flash-Next](qwen3-8-flash-next.md), [Qwen3.8 Max (0902)](qwen3-8-max-0902.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1625.4 ▁▁▃▂▅█▃▆▅●▁▂ beats 107/129 avg 981 20% sharp
DeepSWE v1.1 (resolved) (maker) 71.9% ▂····▂▁▁▆█●█ beats 23/31 hard · avg 19% 1.2% at its level · noisy · small run
chat & writing · #24 of 402 · 11% of its score
Beats [GPT-6 Astra](gpt-6-astra.md), [Claude Sonnet 5.5](claude-sonnet-5-5.md), [GPT-6 Sol](gpt-6-sol.md) here, though they rank higher overall. Trails [Gemini 3.6 Flash](gemini-3-6-flash.md), [Muse Spark 1.2](muse-spark-1-2.md), [Muse Spark 1.1](muse-spark-1-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1479.7 ▁▁▁▂▃▃▃▆▆▇█● beats 360/385 avg 1260 11% blunt
vision & documents · #53 of 153 · 7.3% of its score
Trails [Qwen3.6 Flash](qwen3-6-flash.md), [Gemma 4 31B](gemma-4-31b.md), [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1244.6 ▁▁▂▂▂▂▃▄▅▅●▇ beats 101/152 avg 1061 7.3% blunt
other tests (3)
test result vs the others in its run difficulty counts why
AutomationBench v1.0.6 (maker) 53.1% ▃···▃·▃▃··●▅ beats 6/8 very hard · avg 7.2% 3.2% at its level · small run
SEC-Bench Pro (pass@1) (maker) 66.3% ▄·▄·▄·█●···▄ beats 5/6 very hard · avg 7.5% 2.9% at its level · small run
ExploitGym (pass@1) (maker) 17.8% ▄·▄··█·▄●··▄ beats 5/6 very hard · avg 0.7% 2.5% noisy · small run
source results share of the score
LMArena leaderboard dataset 4 66% ▇▇▇▇▇▇▇▇
Xiaomi MiMo official model cards and do… 9 33% ▇▇▇▇
prior: pull toward the middle 0% ▇
weighted average = 77.4
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### Through an API
api (Xiaomi MiMo) · $0.54/M blended · $0.435 in / $0.87 out per 1M tokens · [mimo.mi.com](https://mimo.mi.com/docs/en-US/quick-start/summary/model)
[price source](https://github.com/anomalyco/models.dev/blob/6b32416340759c52202fef2b0e69cd89adb74bbe/providers/xiaomi/models/mimo-v2.6-pro.toml): Standard base token rate from models.dev (revision 6b32416, observed 2026-10-07). Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL/blob/73875d00b30a89ef8cc353a0b60b0e9f9561952d/README.md): Context length stated on the official model card.
### 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-Pro (Q2_K, needs ~420.38 GB, from [huggingface.co/ggml-org](https://huggingface.co/ggml-org/MiMo-V2.6-Pro-RL-GGUF/tree/5f387f5260fc110678396a3076a140c469b41eaa))
```sh
llama cli -hf ggml-org/MiMo-V2.6-Pro-RL-GGUF:Q2_K
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf ggml-org/MiMo-V2.6-Pro-RL-GGUF:Q2_K
```
Other versions: mxfp4 (~557.05 GB) `llama cli -hf ggml-org/MiMo-V2.6-Pro-RL-GGUF:MXFP4`
### 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-Pro (Q2_K, needs ~420.38 GB, from [huggingface.co/ggml-org](https://huggingface.co/ggml-org/MiMo-V2.6-Pro-RL-GGUF/tree/5f387f5260fc110678396a3076a140c469b41eaa))
```sh
lms get https://huggingface.co/ggml-org/MiMo-V2.6-Pro-RL-GGUF@Q2_K -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: mxfp4 (~557.05 GB) `lms get https://huggingface.co/ggml-org/MiMo-V2.6-Pro-RL-GGUF@MXFP4 -y`
Model repository and documentation: [huggingface.co/XiaomiMiMo](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL)
[interactive version](https://whatmodel.sh/model/mimo-v2-6-pro) · [JSON](https://whatmodel.sh/api?format=json&all=1)