[whatmodel.sh](../../README.md) / MiniMax-M2.7
# MiniMax-M2.7
MiniMax · released 2026-03 · 230B params (10B active) · minimax-non-commercial
**score 66.7** ±0.7 · #117 of 732 · from 31 benchmark results across 8 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks MiniMax-M2.7 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅●▄▄▂▁ #83 of 313 21% 7
coding ▁▁▁▃▅▇█▇●▄▃▁ #98 of 469 29% 7
chat & writing ▁▁▁▃▆▄▆█▆●▇▃ #98 of 402* 4.6% 1
math ▁▁▃▅█▆●█▄▂▁▁ #172 of 431* 4.6% 2
web search ▁▁▁●▄█▃▄·▁·▁ #71 of 84* 1.9% 1
agent tasks · #83 of 313 · 21% of its score
Beats [GPT-5](gpt-5.md), [Grok 4.20 (0309 reasoning)](grok-4-20.md), [GPT-5.1](gpt-5-1.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 75.3% █····██·█●██ beats 4/6 hard · avg 21% 6.5% sharp · at its level
SWE-bench Multilingual 71.8% ▄··▄●···▄▄·█ beats 2/6 hard · avg 30% 4.5% at its level
Terminal-Bench 2.1 55.5% █··▄●·▄····▄ beats 3/6 very hard · avg 13% 4.0% sharp · at its level
τ³-bench 66.1% ●····▄·█·▄·█ beats 0/6 medium · avg 42% 2.0% at its level
BrowseComp 54.1% ▄·▄▄···●··█▄ beats 3/6 hard · avg 18% 1.9% at its level
τ³-bench telecom (tau2-bench ≥ v1… 89.6% ●·▄···▄·▄·█▄ beats 0/6 easy · avg 79% 1.3% maxed out
TerminalWorld-Verified (200 tasks… 49.0% ●··▄·▄·█···▄ beats 0/5 hard · avg 28% 0.9% blunt · at its level
coding · #98 of 469 · 29% of its score
Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [Grok 4.3](grok-4-3.md), [Muse Glimmer 30B](muse-glimmer-30b.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Qwen3 235B-A22B Thinking 2507](qwen3-235b-a22b-thinking-2507.md), [o4-mini](o4-mini.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1398.2 ▁▁▃▂▅●▃▆▅▅▁▂ beats 44/129 avg 981 8.2% sharp
SWE-bench Verified 75.3% █····██·█●██ beats 4/6 hard · avg 21% 6.5% sharp · at its level
SWE-bench Multilingual 71.8% ▄··▄●···▄▄·█ beats 2/6 hard · avg 30% 4.5% at its level
LiveCodeBench v6 (window unspecif… 77.2% ●█···█··████ beats 0/6 medium · avg 46% 3.0% typical
SciCode subproblems 38.3% ●····▃··█·▃▃ beats 0/6 hard · avg 19% 2.3% at its level
LiveCodeBench v6 (Feb–May 2025) 69.8% ●··█····▄▄·█ beats 0/6 medium · avg 47% 2.2% typical
SciCode (level unspecified) 47.0% ▄·····▄█▄●·▄ beats 5/6 hard · avg 16% 2.2% at its level
chat & writing · #98 of 402 · 4.6% of its score
Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [Inkling Small (266B-A12B)](inkling-small.md), [GPT-5.4 nano](gpt-5-4-nano.md) here, though they rank higher overall. Trails [Gemma 4 12B](gemma-4-12b.md), [Kimi K2 Instruct (0905)](kimi-k2-0905.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
LMArena (text) 1415.0 ▁▁▁▂▃▃▃▆▆●█▃ beats 266/385 avg 1260 4.6% blunt
math · #172 of 431 · 4.6% of its score
Beats [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md) here, though they rank higher overall. Trails [OpenThinker3 7B](openthinker3-7b.md), [Olmo 3 7B Think](olmo-3-7b-think.md), [OpenThinker2 32B](openthinker2-32b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
IMO-AnswerBench 68.3% ●····▄·▄▄·█▄ beats 0/6 medium · avg 41% 2.5% typical
AIME 2026 88.8% ▄·●·▄▄··█··▄ beats 1/6 medium · avg 57% 2.1% sharp · noisy
web search · #71 of 84 · 1.9% of its score
Beats [DeepSeek V4 Flash (Preview, Apr 2026)](deepseek-v4-flash-preview.md), [Nemotron 3 Ultra 550B-A55B](nemotron-3-ultra-550b-a55b.md), [GPT-5.4 nano](gpt-5-4-nano.md) here, though they rank higher overall. Trails [GPT-4.1 nano](gpt-4-1-nano-2025-04-14.md), [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BrowseComp 54.1% ▄·▄▄···●··█▄ beats 3/6 hard · avg 18% 1.9% at its level
other tests (15)
test result vs the others in its run difficulty counts why
GPQA Diamond 87.4% ▄·····▄█●▄·▄ beats 4/6 hard · avg 44% 10% sharp
GPQA (subset unspecified) 86.6% ▄●▄▄·▄·····▄ beats 1/6 hard · avg 47% 8.3% sharp
Humanity's Last Exam 23.1% ●··█▄··▄·▄·▄ beats 0/6 very hard · avg 5.7% 7.7% several runs
MMLU-Pro 81.9% ●·····▄▄▄·▄█ beats 0/6 medium · avg 53% 4.8% typical
Global-MMLU-Lite 87.3% ▃······●··█▃ beats 1/6 medium · avg 61% 4.2% typical
MMLU-ProX 78.4% ●·····▄·▄▄█▄ beats 0/6 medium · avg 58% 2.4% typical
IFBench (aggregation unspecified) 75.7% ▃······●▃▃·▃ beats 1/6 medium · avg 45% 2.4% at its level
AgentWorldBench (open-ended, over… 46.1% ●▄··▄▄·▄█▄██ beats 0/11 hard · avg 26% 2.4% blunt · at its level
WildClawBench 33.8% ▃·▅▅●·▅█▅▃▃▃ beats 5/17 very hard · avg 11% 2.2% at its level
IFBench prompt-level (loose) 74.6% ▄●·▄··▄▄···█ beats 1/6 medium · avg 47% 2.0% at its level
MultiChallenge 42.5% ●··········█ beats 0/6 hard · avg 28% 1.8% at its level
WideSearch corrected dataset 75.2% ▃······▃▃▃●▃ beats 6/7 hard · avg 22% 1.4% at its level · noisy
SkillsBench 87-task subset 28.4% ▄··▄▄●···█·█ beats 3/7 very hard · avg 9.1% 1.3% at its level
CritPt 0.6% ●····▄█···█▄ beats 0/6 very hard · avg 1.1% 0.8% too hard for it · noisy
Vectara hallucination leaderboard… 87.1% ▁▂▁▂●█▂▃▄▃▁▁ beats 18/98 very easy · avg 90% 0.0% blunt
source results share of the score
NVIDIA 16 52% ▇▇▇▇▇▇
A.X K2 Technical Report 7 26% ▇▇▇
LMArena leaderboard dataset 2 13% ▇▇
inclusionAI/Ling-3.0-flash model card 2 3% ▇
Qwen/Qwen-AgentWorld-35B-A3B model card 1 2% ▇
WildClawBench: A Benchmark for Real-Wor… 1 2% ▇
TerminalWorld: Benchmarking Agents on R… 1 1% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 66.7
Licences: NVIDIA ([CC BY 4.0](../../licenses/report-2606.15007v1.txt)) · A.X K2 Technical Report ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · inclusionAI/Ling-3.0-flash model card ([MIT](../../licenses/cards-final-NOTICE.txt)) · Qwen/Qwen-AgentWorld-35B-A3B model card ([Apache-2.0](https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/blob/60d2b0434a53d2e62a7c00a489586815d94ebffb/README.md)) · WildClawBench: A Benchmark for Real-World ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · TerminalWorld: Benchmarking Agents on Real-World Terminal Tasks ([CC BY 4.0](../../licenses/paper-2605.22535v1-CC-BY-4.0.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### Through an API
api (MiniMax) · $0.52/M blended · $0.3 in / $1.2 out per 1M tokens · [platform.minimax.io](https://platform.minimax.io/docs/guides/text-generation)
[price source](https://platform.minimax.io/docs/guides/pricing-paygo.md): USD per 1M tokens, MiniMax Open Platform pay-as-you-go, MiniMax-M2.7: input $0.3, output $1.2. Prompt-cache read $0.06 and write $0.375, and MiniMax-M2.7-highspeed ($0.6/$2.4) excluded. Currency: USD. MiniMax pay-as-you-go pricing page, observed 2026-09-25. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/minimax/MiniMax-M2.7.toml)
### 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 MiniMax-M2.7 (ud-q4_k_xl, needs ~138.5 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/MiniMax-M2.7-GGUF/tree/main))
```sh
llama cli -hf unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_XL
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_XL
```
Other versions: ud-q2_k_xl (~69.5 GB) `llama cli -hf unsloth/MiniMax-M2.7-GGUF:UD-Q2_K_XL`
### 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 MiniMax-M2.7 (ud-q4_k_xl, needs ~138.5 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/MiniMax-M2.7-GGUF/tree/main))
```sh
lms get https://huggingface.co/unsloth/MiniMax-M2.7-GGUF@UD-Q4_K_XL -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: ud-q2_k_xl (~69.5 GB) `lms get https://huggingface.co/unsloth/MiniMax-M2.7-GGUF@UD-Q2_K_XL -y` · 4bit (~128.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/MiniMax-M2.7-4bit -y` · 8bit (~243 GB, Mac only) `lms get https://huggingface.co/mlx-community/MiniMax-M2.7-8bit -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 MiniMax-M2.7 (4bit, needs ~128.7 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/MiniMax-M2.7-4bit/tree/66d2e5cb7c5cda05251b4625c504af4b034df7ff))
```sh
mlx_lm.chat --model mlx-community/MiniMax-M2.7-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/MiniMax-M2.7-4bit
```
Other versions: 8bit (~243 GB, Mac only) `mlx_lm.chat --model mlx-community/MiniMax-M2.7-8bit`
Model repository and documentation: [huggingface.co/MiniMaxAI](https://huggingface.co/MiniMaxAI/MiniMax-M2.7)
[interactive version](https://whatmodel.sh/model/minimax-m2-7) · [JSON](https://whatmodel.sh/api?format=json&all=1)