[whatmodel.sh](../../README.md) / MiniMax-M2
# MiniMax-M2
MiniMax · released 2025-10 · 230B params (10B active) · modified-mit
**score 63.7** ±1.1 · #157 of 732 · from 7 benchmark results across 5 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 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄●▅▄▄▂▁ #115 of 313 30% 3
coding ▁▁▁▃▅▇█●▅▄▃▁ #141 of 469* 23% 1
chat & writing ▁▁▁▃▆▄▆█●▇▇▃ #173 of 402* 13% 1
agent tasks · #115 of 313 · 30% 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), [Devstral 2 (2512)](devstral-2.md), [Nova 2 Pro (Preview)](nova-2-pro.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
ContractBench success rate (33 ob… 53.5% ▆··▆▄▂▄▂●▆█▄ beats 14/23 very hard · avg 13% 11% sharp · at its level
τ²-bench Telecom (maker) 87.0% ▁·▅█▆▅▂●▃▂▃▁ beats 64/89 medium · avg 38% 10% sharp · small run
Terminal-Bench 2.0 with Terminus … 30.0% ▃··▃·▃▃█●▅▃▅ beats 8/14 very hard · avg 6.1% 8.8% sharp
coding · #141 of 469 · 23% 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), [Gemma 4 12B](gemma-4-12b.md), [Seed-OSS 36B Instruct](seed-oss-36b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1297.1 ▁▁▃●▅█▃▆▅▅▁▂ beats 20/129 avg 981 23% sharp
chat & writing · #173 of 402 · 13% of its score
Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1343.5 ▁▁▁▂▃▃▃▆●▇█▃ beats 185/385 avg 1260 13% blunt
other tests (2)
test result vs the others in its run difficulty counts why
Medmarks-V mean win rate (19 veri… 0.5708 ▁▄▃▃▇▄█▃●▁▁▁ beats 26/30 avg 0.418 24% sharp
Medmarks-V weighted mean win rate… 57.1% ▂▃▅▂▆▃█▅█●▃▆ beats 27/34 medium · avg 41% 10% blunt · at its level
source results share of the score
LMArena leaderboard dataset 2 36% ▇▇▇▇
Medmarks: A Comprehensive Open-Source L… 2 34% ▇▇▇▇
ContractBench: Can LLM Agents Preserve … 1 11% ▇
MiniMax vendor-reported benchmark score… 1 10% ▇
Terminal-Bench: Benchmarking Agents on … 1 9% ▇
prior: pull toward the middle 1% ▇
weighted average = 63.7
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks ([CC BY 4.0](../../licenses/paper-2605.01417v1-CC-BY-4.0.txt)) · ContractBench: Can LLM Agents Preserve Observation Contracts? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Terminal-Bench: Benchmarking Agents on Hard ([CC BY 4.0](../../licenses/paper-2601.11868v1-CC-BY-4.0.txt))
## How to run it
### Through an API
api (Amazon Bedrock) · $0.52/M blended · $0.3 in / $1.2 out per 1M tokens · [docs.aws.amazon.com](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html)
[price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/amazon-bedrock/models/minimax.minimax-m2.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/minimax/MiniMax-M2.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 (ud-q4_k_xl, needs ~138.5 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/MiniMax-M2-GGUF/tree/main))
```sh
llama cli -hf unsloth/MiniMax-M2-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-GGUF:UD-Q4_K_XL
```
Other versions: ud-q2_k_xl (~69.5 GB) `llama cli -hf unsloth/MiniMax-M2-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 (ud-q4_k_xl, needs ~138.5 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/MiniMax-M2-GGUF/tree/main))
```sh
lms get https://huggingface.co/unsloth/MiniMax-M2-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-GGUF@UD-Q2_K_XL -y`
Model repository and documentation: [huggingface.co/MiniMaxAI](https://huggingface.co/MiniMaxAI/MiniMax-M2)
[interactive version](https://whatmodel.sh/model/minimax-m2) · [JSON](https://whatmodel.sh/api?format=json&all=1)