[whatmodel.sh](../../README.md) / MiMo-V2-Flash (309B-A15B)
# MiMo-V2-Flash (309B-A15B)
Xiaomi · released 2025-12 · 309.8B params (15B active) · mit
**score 65.0** ±1.2 · #144 of 732 · from 12 benchmark results across 3 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-Flash (309B-A15B) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅●▄▄▂▁ #111 of 313* 17% 2
coding ▁▁▁▃▅▇█●▅▄▃▁ #145 of 469* 33% 2
chat & writing ▁▁▁▃▆▄▆█▆●▇▃ #130 of 402* 15% 1
math ▁▁▃▅█▆▄●▄▂▁▁ #108 of 431 19% 4
web search █●···█····██ #4 of 5* 8.8% 1
agent tasks · #111 of 313 · 17% of its score
Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [o3-pro](o3-pro.md), [Grok 4.3](grok-4-3.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Nova 2 Pro (Preview)](nova-2-pro.md), [Mistral Medium 3.5 (128B)](mistral-medium-3-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BrowseComp-ZH 51.2% ▁▁▁▁▁▃·▁▄●▄█ beats 14/23 hard · avg 20% 8.8% at its level
τ²-bench aggregate 84.1% ▄······●█·▄▄ beats 1/5 medium · avg 51% 7.7% typical
coding · #145 of 469 · 33% 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), [Devstral Small 2 24B](devstral-small-2.md), [Gemma 4 12B](gemma-4-12b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1291.7 ▁▁▃●▅█▃▆▅▅▁▂ beats 19/129 avg 981 27% sharp
LiveCodeBench v6 (window unspecif… 81.6% ▂▃▄▄▅██▆●▇▄▆ beats 47/67 medium · avg 48% 6.5% small run
chat & writing · #130 of 402 · 15% of its score
Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [o3-mini](o3-mini.md) here, though they rank higher overall. Trails [LFM2.5 2.6B](lfm2-5-2-6b.md), [Nemotron-Cascade-8B](nemotron-cascade-8b.md), [DeepSeek V3 0324](deepseek-v3-0324.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1386.1 ▁▁▁▂▃▃▃▆▆●█▃ beats 226/385 avg 1260 15% blunt
math · #108 of 431 · 19% of its score
Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [MiniMax-M3](minimax-m3.md), [Nemotron 3 Ultra 550B-A55B](nemotron-3-ultra-550b-a55b.md) here, though they rank higher overall. Trails [Gemini 2.5 Flash](gemini-2-5-flash.md), [GLM-4.5-Air](glm-4-5-air.md), [Granite 4.2 30B](granite-4-2-30b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AIME 2025 95.1% ▁·▁▁▁▂▅▆█▇▅● beats 129/133 easy · avg 69% 10% sharp · maxed out · small run
IMO-AnswerBench 80.9% ▁▁·▁▁▁▄▆●▄▂▃ beats 26/44 medium · avg 36% 5.2% at its level · small run
HMMT November 2025 91.0% ▁▁····▁▅▃●▇▁ beats 16/23 easy · avg 66% 2.4% noisy
HMMT February 2025 95.4% ▁▁▁▃▁▅▅▃█●▁▄ beats 27/31 medium · avg 61% 1.7% noisy · small run
web search · #4 of 5 · 8.8% of its score
test result vs the others in its run difficulty counts why
BrowseComp-ZH 51.2% ▁▁▁▁▁▃·▁▄●▄█ beats 14/23 hard · avg 20% 8.8% at its level
other tests (3)
test result vs the others in its run difficulty counts why
WildClawBench 30.8% ▃·▅●█·▅█▅▃▃▃ beats 3/17 very hard · avg 11% 6.7% at its level
ResearchRubrics 54.3% ●██··██·█··█ beats 0/6 hard · avg 33% 6.0% at its level
GAIA (no-file subset) 78.2% ▃▃····█●···▃ beats 5/6 medium · avg 49% 3.1% at its level
source results share of the score
stepfun-ai/Step-3.5-Flash model card 9 51% ▇▇▇▇▇▇
LMArena leaderboard dataset 2 42% ▇▇▇▇▇
WildClawBench: A Benchmark for Real-Wor… 1 7% ▇
prior: pull toward the middle 1% ▇
weighted average = 65.0
Licences: stepfun-ai/Step-3.5-Flash model card ([Apache-2.0](../../licenses/stepfun-ai--Step-3.5-Flash.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · WildClawBench: A Benchmark for Real-World ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## 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-Flash (309B-A15B) (Q4_K_M, needs ~186.8 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/MiMo-V2-Flash-GGUF/tree/04c761fbebcf4f3239b1cfdafec85533dcc0dea3))
```sh
llama cli -hf unsloth/MiMo-V2-Flash-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf unsloth/MiMo-V2-Flash-GGUF:Q4_K_M
```
Other versions: Q8_0 (~328.2 GB) `llama cli -hf unsloth/MiMo-V2-Flash-GGUF:Q8_0` · UD-IQ2_XXS (~101.6 GB) `llama cli -hf unsloth/MiMo-V2-Flash-GGUF:UD-IQ2_XXS`
### 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-Flash (309B-A15B) (Q4_K_M, needs ~186.8 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/MiMo-V2-Flash-GGUF/tree/04c761fbebcf4f3239b1cfdafec85533dcc0dea3))
```sh
lms get https://huggingface.co/unsloth/MiMo-V2-Flash-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 (~328.2 GB) `lms get https://huggingface.co/unsloth/MiMo-V2-Flash-GGUF@Q8_0 -y` · UD-IQ2_XXS (~101.6 GB) `lms get https://huggingface.co/unsloth/MiMo-V2-Flash-GGUF@UD-IQ2_XXS -y` · 4bit (~173.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/MiMo-V2-Flash-4bit -y`
### 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-Flash (309B-A15B) (4bit, needs ~173.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/MiMo-V2-Flash-4bit/tree/65d2673628480cc377aaeefc1b409f1d750bc9df))
```sh
mlx_lm.chat --model mlx-community/MiMo-V2-Flash-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/MiMo-V2-Flash-4bit
```
Model repository and documentation: [huggingface.co/XiaomiMiMo](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash)
[interactive version](https://whatmodel.sh/model/mimo-v2-flash) · [JSON](https://whatmodel.sh/api?format=json&all=1)