MiniMax/Mixture of Experts

MiniMax-M2.7

Name: MiniMax-M2.7
Author: MiniMax

MiniMax-M2.7 is our first model deeply participating in its own evolution. M2.7 is capable of building complex agent harnesses and completing highly elaborate productivity tasks, leveraging Agent Teams, complex Skills, and dynamic tool search.

chatcodingreasoningmultilingualmathagentictool_use

230B

Parameters (10B active)

200K

Context length

Benchmarks

Quantizations

Architecture

MoE

Released

2026-04-12

Layers

KV Heads

Head Dim

128

Family

minimax

Quantization Options

Quant	Bits	VRAM	Quality
IQ2_XXS	2.38	68.9 GB	low
IQ2_M	2.93	84.7 GB	low
Q2_K	3.16	91.3 GB	low
IQ3_XXS	3.25	93.9 GB	low
IQ3_XS	3.5	101.1 GB	low
Q3_K_S	3.64	105.1 GB	low
IQ3_M	3.76	108.6 GB	low
Q3_K_M	4	115.5 GB	low
Q3_K_L	4.3	124.1 GB	moderate
IQ4_XS	4.46	128.7 GB	moderate
Q4_K_S	4.67	134.8 GB	moderate
Q4_K_M	4.89	141.1 GB	good
Q5_K_S	5.57	160.6 GB	good
Q5_K_M	5.7	164.4 GB	good
Q6_K	6.56	189.1 GB	excellent
Q8_0	8.5	244.9 GB	lossless
FP16	16	460.5 GB	lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

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Community Ratings

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Benchmarks (9)

GPQA Diamond87.4

τ²-Bench84.8

IFBench75.7

AA Long Context68.7

AA Intelligence49.6

SciCode47.0

AA Coding41.9

Terminal-Bench39.4

HLE28.1

Run this model

▸Easiest way to get started·Beginners

DOCS ↗

curl -fsSL https://ollama.com/install.sh | sh

$ollama run minimax:230b-q4_K_M

Tag may need adjustment — check ollama.com/library/minimax for available tags.

▸ SETUP GUIDE

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more

Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

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