MiniMax-M3
MiniMaxAIothertextvisionvideo

MiniMax-M3

Updated Nov 7, 2026
Parameters
440.3B
Context
1,048,576
License
other
Updated
Nov 7, 2026

Intelligence benchmarks

Artificial Analysis indexes - compared with the best open and proprietary models

Intelligence

18.9

AA Index

Coding

13.9

AA Index

Agentic

15.5

AA Index

Intelligence Index - MiniMax-M3 vs. the field

Best open-weight models (you can run locally) and leading proprietary models for context.

Gemini 4 Carbon
60
closed
Claude Opus 5.5
59.1
closed
GPT-6 Astra
56.9
closed
GPT-6.1 Sol
54.6
closed
Claude Sonnet 5.5
54.5
closed
Kimi K3.1 Pro
46.5
open
DeepSeek V4.1 Flash 1017
32.6
open
MiniMax-M3
18.9
open

Coding Index comparison

Claude Opus 5.5
51.7
closed
Claude Sonnet 5.5
50.5
closed
Gemini 4 Carbon
50.4
closed
GPT-6 Astra
49.1
closed
GPT-6.1 Terra
47.6
closed
Kimi K3.1 Pro
42.1
open
DeepSeek V4.1 Flash 1017
30.6
open
MiniMax-M3
13.9
open

Agentic Index comparison

Gemini 4 Carbon
53.1
closed
Kimi K3.1 Pro
52.3
open
Muse Wonder Full
52
closed
Gemini 4 Argon
49.7
closed
GPT-6 Astra
49.7
closed
Muse Wonder
49.3
closed
MiniMax-M3.1-Flash
46.7
open
MiniMax-M3
15.5
open

Benchmark data from Artificial Analysis · updated 2026-11-09.

Hosted API cost & speed

What it costs to rent this model instead of running it locally

MetricValue
Input (per 1M tokens)$0.30
Output (per 1M tokens)$1.20
Median speed (hosted API)138 tok/s

Median across hosted providers, measured by Artificial Analysis - updated 2026-11-09. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.

Arena rating (LMArena)

Human preference wins from head-to-head chat battles

MetricValue
Overall arena rating1431.3
Rank (overall)#93
Battle votes68,611

Overall rating published by LMArena on 2026-10-28 - a pairwise human-preference Elo across live chat battles; higher is better. Fetched 2026-11-07. Open-weights models compete head-to-head with proprietary ones here — no benchmark prompts, just what people pick.

Standard benchmarks

Performance across standard evaluations

BenchmarkScore
GPQA Diamond92.9
HLE39
SciCode47.1
IFBench82.9
Terminal-Bench42.4

Source: Artificial Analysis component evals - updated 2026-11-09.

Will it run on your hardware?

Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest

Too big for 24 GB at any quant
0 of 4 quantizations fit MiniMax-M3 with real runtime overhead.
Q4_K_M
255 GB
Q5_K_M
313 GB
Q8_0
471 GB
FP16
881 GB
fits tight too big

Need an exact figure for your context length? Use the VRAM calculator.

Run it locally

Copy-paste - running in under a minute

vLLMOpenAI-compatible API
vllm serve MiniMaxAI/MiniMax-M3-MXFP8

New to this? Start with Ollama · serve to many users with vLLM.

Deep dive

Notes, sources, and the full write-up

MiniMax-M3 is a 440.3B-parameter other model from MiniMax. It scores 18.9 on the Artificial Analysis Intelligence Index (coding 13.9). At Q4_K_M it needs roughly 255 GB of VRAM, placing it in the 48 GB+ / multi-GPU hardware tier.

BenchmarksLink to this section

Artificial Analysis Intelligence Index - MiniMax-M3 vs. leading closed models:

ModelIntelligenceCodingGPQA
MiniMax-M318.913.992.9
Gemini 4 Carbon6050.4-
Claude Opus 5.559.151.7-
GPT-6 Astra56.949.196.1
GPT-6.1 Sol54.646.8-
Claude Sonnet 5.554.550.5-

Source: Artificial Analysis (2026-11-09).

PopularityLink to this section

MiniMax-M3 has 228,413 downloads in the last month on HuggingFace and 37 likes.

Frequently asked

Quick answers to common questions

How much VRAM does MiniMax-M3 need?

MiniMax-M3 with 440.3B parameters needs approximately 255 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is MiniMax-M3 better than other MiniMaxAI models?

MiniMax-M3 has 440.3B parameters with 1,048,576 context - a strong choice for general use.

What license is MiniMax-M3 under?

MiniMax-M3 is released under the other license, making it suitable for most commercial and personal projects.

What hardware runs MiniMax-M3 well?

With 440.3B parameters, MiniMax-M3 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.

What is the best quantization for MiniMax-M3?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~313 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

How long can MiniMax-M3's context window handle?

MiniMax-M3 supports a 1,048,576-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.

What models compete with MiniMax-M3?

MiniMax-M3 competes with other 220B–660B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.

Nearby options

Similar models and compatible hardware by spec