[whatmodel.sh](../../README.md) / MiMo-V2.6-Flash (309B-A15B)
# MiMo-V2.6-Flash (309B-A15B)
Xiaomi · released 2026-09 · 309B params (15B active) · mit
**score 75.6** ±1.0 · #27 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-Flash (309B-A15B) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅▄●▂▁ #30 of 313 53% 7
coding ▁▁▁▃▅▇█▇▅▄●▁ #21 of 469* 21% 2
chat & writing ▁▁▁▃▆▄▆█▆▇●▃ #58 of 402* 11% 1
vision & documents ▁▁▂▂▁▃▄▂▆●█▄ #54 of 153* 7.4% 1
agent tasks · #30 of 313 · 53% of its score
Beats [Hy4 preview (770B-A49B)](hy4-preview.md), [GPT-5.6 Terra](gpt-5-6-terra.md), [GPT-5.4](gpt-5-4.md) here, though they rank higher overall. Trails [Grok 4.7](grok-4-7.md), [GLM-5.3](glm-5-3.md), [GPT-6 Luna](gpt-6-luna.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena Agent (October 2026 aggrega… 0.0100 IPS ▁▁▁▃▃▂●▁▂▂▁▁ beats 28/59 avg -0.379 IPS 28% sharp
Terminal-Bench 4.0 (maker) 28.8% ▁·▁··▁▆▆●██▃ beats 15/33 very hard · avg 0.5% 10% sharp · at its level · small run
Terminal-Bench 2.1 (maker) 87.6% ▁·▁·▁▁▄▃▆█▅● beats 57/63 hard · avg 23% 5.5% sharp · small run
OSWorld-Verified (maker) 80.8% █▃·▂▅▂▃▂▅▃●▆ beats 21/27 hard · avg 24% 4.2% small run
Agents' Last Exam (metric/harness… 27.6% ▂·▂·●▄▂·▂▂▄▄ beats 5/14 very hard · avg 6.5% 2.7% at its level · small run
Toolathlon-Verified (maker) 73.6% ▂·▂▂▅▃···▂●█ beats 9/18 hard · avg 32% 1.6% blunt · at its level · small run
DeepSWE v1.1 (resolved) (maker) 67.9% ▂····▂▁▁▆●██ beats 17/31 hard · avg 19% 1.2% at its level · noisy · small run
coding · #21 of 469 · 21% of its score
Beats [GPT-5.6 Sol](gpt-5-6-sol.md), [GPT-5.5](gpt-5-5.md), [MiMo-V2.6-Pro](mimo-v2-6-pro.md) here, though they rank higher overall. Trails [Grok 4.7](grok-4-7.md), [Qwen3.8 Max (0902)](qwen3-8-max-0902.md), [Kimi K3](kimi-k3.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1637.2 ▁▁▃▂▅█▃▆▅●▁▂ beats 110/129 avg 981 20% sharp
DeepSWE v1.1 (resolved) (maker) 67.9% ▂····▂▁▁▆●██ beats 17/31 hard · avg 19% 1.2% at its level · noisy · small run
chat & writing · #58 of 402 · 11% of its score
Trails [Gemma 4 31B](gemma-4-31b.md), [Claude Sonnet 4.5](claude-sonnet-4-5.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
LMArena (text) 1447.6 ▁▁▁▂▃▃▃▆▆▇●▃ beats 315/385 avg 1260 11% blunt
vision & documents · #54 of 153 · 7.4% 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) 1244.5 ▁▁▂▂▂▂▃▄▅▅●▇ beats 100/152 avg 1061 7.4% blunt
other tests (3)
test result vs the others in its run difficulty counts why
AutomationBench v1.0.6 (maker) 52.3% ▃···▃·▃▃··●▅ beats 5/8 very hard · avg 7.2% 3.2% at its level · small run
SEC-Bench Pro (pass@1) (maker) 47.5% ▄·▄·●·█▄···▄ beats 2/6 very hard · avg 7.5% 2.9% at its level · small run
ExploitGym (pass@1) (maker) 6.0% ▄·▄··●·▄▄··▄ beats 3/6 very hard · avg 0.7% 2.3% 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 = 75.6
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) · $0.18/M blended · $0.14 in / $0.28 out per 1M tokens · [mimo.mi.com](https://mimo.mi.com/docs/en-US/quick-start/summary/model)
[price source](https://mimo.mi.com/docs/en-US/price/pay-as-you-go): Xiaomi MiMo API Pricing (pay-as-you-go), 'Overseas Pricing of the Model', Language Models, Real-time API row 'mimo-v2.6-flash': Input (Cache Hit) $0.0028, Input (Cache Miss) $0.14, Output $0.28 per M tokens (USD); cache-miss input and output used; batch API ($0.07/$0.14) and China RMB prices excluded. Observed 2026-10-09. · [context source](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL/blob/5711b268169967567844e1e560e8a3966da959b1/README.md): Official model card 'Model Summary': 'Context Length: 1M tokens'; Xiaomi API model page lists mimo-v2.6-flash 'Context Window: 1M', 'Maximum Output: 128K'. Observed 2026-10-09.
### 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-Flash (309B-A15B) (Q2_K, needs ~127.77 GB, from [huggingface.co/ggml-org](https://huggingface.co/ggml-org/MiMo-V2.6-Flash-RL-GGUF/tree/a5d1269fdcf9c3346934411f700038e9cd9d5102))
```sh
llama cli -hf ggml-org/MiMo-V2.6-Flash-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-Flash-RL-GGUF:Q2_K
```
Other versions: mxfp4 (~170.12 GB) `llama cli -hf ggml-org/MiMo-V2.6-Flash-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-Flash (309B-A15B) (Q2_K, needs ~127.77 GB, from [huggingface.co/ggml-org](https://huggingface.co/ggml-org/MiMo-V2.6-Flash-RL-GGUF/tree/a5d1269fdcf9c3346934411f700038e9cd9d5102))
```sh
lms get https://huggingface.co/ggml-org/MiMo-V2.6-Flash-RL-GGUF@Q2_K -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: mxfp4 (~170.12 GB) `lms get https://huggingface.co/ggml-org/MiMo-V2.6-Flash-RL-GGUF@MXFP4 -y`
Model repository and documentation: [huggingface.co/XiaomiMiMo](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL)
[interactive version](https://whatmodel.sh/model/mimo-v2-6-flash) · [JSON](https://whatmodel.sh/api?format=json&all=1)