[whatmodel.sh](../../README.md) / MiniMax-M2.1
# MiniMax-M2.1
MiniMax · released 2025-12 · 228.7B params (10B active) · other
**score 64.7** ±1.2 · #146 of 732 · from 12 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-M2.1 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅●▄▄▂▁ #107 of 313 40% 4
coding ▁▁▁▃▅▇█▇●▄▃▁ #104 of 469* 26% 1
chat & writing ▁▁▁▃▆▄▆█▆●▇▃ #131 of 402* 15% 1
math ▁▁▃▅█●▄█▄▂▁▁ #231 of 431 9.3% 3
web search ●█···█····██ #5 of 5* 8.6% 1
agent tasks · #107 of 313 · 40% 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
ContractBench success rate (33 ob… 60.6% ▆··▆▄▂▄▂▆●█▄ beats 16/23 very hard · avg 13% 12% sharp · at its level
τ²-bench Telecom (maker) 87.0% ▁·▅█▆▅▂●▃▂▃▁ beats 65/89 medium · avg 38% 12% sharp · small run
BrowseComp-ZH 47.8% ▁▁▁▁▁▃·▁▄●▄█ beats 11/23 hard · avg 20% 8.6% at its level
τ²-bench aggregate 86.6% ▄······▄█·●▄ beats 4/5 medium · avg 51% 7.7% typical
coding · #104 of 469 · 26% 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) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1384.0 ▁▁▃▂▅●▃▆▅▅▁▂ beats 38/129 avg 981 26% sharp
chat & writing · #131 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) 1384.5 ▁▁▁▂▃▃▃▆▆●█▃ beats 225/385 avg 1260 15% blunt
math · #231 of 431 · 9.3% of its score
Trails [Gemma 7B (base)](gemma-7b-base.md), [Phi-2 (2.7B)](phi-2.md), [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
IMO-AnswerBench 60.4% ▁▁·▁▁●▄▆█▄▂▃ beats 6/44 medium · avg 36% 5.2% at its level · small run
HMMT November 2025 74.3% ▁▁····▁●▃█▇▁ beats 3/23 easy · avg 66% 2.3% noisy
HMMT February 2025 71.0% ▁▁▁▃▁●▅▃██▁▄ beats 9/31 medium · avg 61% 1.7% noisy · small run
web search · #5 of 5 · 8.6% of its score
test result vs the others in its run difficulty counts why
BrowseComp-ZH 47.8% ▁▁▁▁▁▃·▁▄●▄█ beats 11/23 hard · avg 20% 8.6% at its level
other tests (3)
test result vs the others in its run difficulty counts why
ResearchRubrics 60.2% ███··█●·█··█ beats 4/6 hard · avg 33% 5.9% at its level
GAIA (no-file subset) 64.3% ▃●····█▃···▃ beats 1/6 medium · avg 49% 3.1% at its level
Vectara hallucination leaderboard… 88.2% ▁▂▁▂●█▂▃▄▃▁▁ beats 27/98 very easy · avg 90% 0.0% blunt
source results share of the score
LMArena leaderboard dataset 2 41% ▇▇▇▇▇
stepfun-ai/Step-3.5-Flash model card 7 35% ▇▇▇▇
ContractBench: Can LLM Agents Preserve … 1 12% ▇
MiniMax vendor-reported benchmark score… 1 12% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 1% ▇
weighted average = 64.7
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · stepfun-ai/Step-3.5-Flash model card ([Apache-2.0](../../licenses/stepfun-ai--Step-3.5-Flash.txt)) · ContractBench: Can LLM Agents Preserve Observation Contracts? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## 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 MiniMax-M2.1 (Q4_K_M, needs ~138.3 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/MiniMax-M2.1-GGUF/tree/8ffdd68efe5bc49af9e6c5c939d2e0331d17804b))
```sh
llama cli -hf unsloth/MiniMax-M2.1-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf unsloth/MiniMax-M2.1-GGUF:Q4_K_M
```
Other versions: Q8_0 (~243.1 GB) `llama cli -hf unsloth/MiniMax-M2.1-GGUF:Q8_0` · UD-IQ2_XXS (~74.1 GB) `llama cli -hf unsloth/MiniMax-M2.1-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 MiniMax-M2.1 (Q4_K_M, needs ~138.3 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/MiniMax-M2.1-GGUF/tree/8ffdd68efe5bc49af9e6c5c939d2e0331d17804b))
```sh
lms get https://huggingface.co/unsloth/MiniMax-M2.1-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 (~243.1 GB) `lms get https://huggingface.co/unsloth/MiniMax-M2.1-GGUF@Q8_0 -y` · UD-IQ2_XXS (~74.1 GB) `lms get https://huggingface.co/unsloth/MiniMax-M2.1-GGUF@UD-IQ2_XXS -y` · 4bit (~128.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/MiniMax-M2.1-4bit -y` · 8bit (~243 GB, Mac only) `lms get https://huggingface.co/mlx-community/MiniMax-M2.1-8bit -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-M2.1 (4bit, needs ~128.7 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/MiniMax-M2.1-4bit/tree/d1c9115092167b919e0296d4087fc210f2e17ae0))
```sh
mlx_lm.chat --model mlx-community/MiniMax-M2.1-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/MiniMax-M2.1-4bit
```
Other versions: 8bit (~243 GB, Mac only) `mlx_lm.chat --model mlx-community/MiniMax-M2.1-8bit`
Model repository and documentation: [huggingface.co/MiniMaxAI](https://huggingface.co/MiniMaxAI/MiniMax-M2.1)
[interactive version](https://whatmodel.sh/model/minimax-m2-1) · [JSON](https://whatmodel.sh/api?format=json&all=1)