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MiniMax · Open weights

MiniMax M3

Added Jun 1, 2026Open weights

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Use in the app

MiniMax M3 is the non-thinking route for MiniMax's open-weights frontier model, built for coding, agent workflows, tool use, and multimodal understanding from step zero. It keeps native thinking disabled for faster direct answers. MiniMax reports 59.0% on SWE-Bench Pro and 66.0% on Terminal Bench 2.1, with Sparse Attention designed to scale context to 1M.

  • Tools
  • Vision
  • Structured output
Input
Text, Images
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.23
Output
$0.96
Cache read
$0.05

Reference prices as published by the model provider. API example

Specifications

Context window
512K
Max output
80K
Average output (7 days)
491 tokens
Longer than 48% of 465 models

Side by side

MiniMax M3 compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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MiniMax M3MiniMaxThis modelMiniMax M2.1MiniMaxRemove
ProviderMiniMaxMiniMax
Discovery tierOpen weightsOpen weights
AddedJun 1, 2026Jan 20, 2025
Limits
Context window512KBest200K
Max output80K131.07KBest
Average output491 tokens359 tokens
Pricing, USD per 1M tokens
Input$0.23Best$0.30
Output$0.96Best$1.20
Cache read$0.05$0.03Best
Performance
Throughput87.7 tok/s—
Time to first token1.8 s—
Request success (24h)99.9%—
Benchmark indexes
Intelligence Index29.2Best20.9
Coding Index58.6—
Math Index—82.7
Capabilities
ReasoningNoYes
ToolsYesYes
VisionYesNo
Structured outputYesYes
Reasoning effort——

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • MiniMax M3
  • MiniMax M2.1

Intelligence Index

  • MiniMax M329.2
  • MiniMax M2.120.9

MiniMax M3: Better than 70% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • MiniMax M3: 58.6
    • MiniMax M2.1: —

  • Math Index

    Composite of the math evaluations

    • MiniMax M3: —
    • MiniMax M2.1: 82.7

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • MiniMax M3: 21.3%
    • MiniMax M2.1: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • MiniMax M3: 4.4%
    • MiniMax M2.1: —

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • MiniMax M3: 0.0%
    • MiniMax M2.1: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • MiniMax M3: 1,089 Elo
    • MiniMax M2.1: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • MiniMax M3: 1,243 Elo
    • MiniMax M2.1: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • MiniMax M3: 9.8%
    • MiniMax M2.1: —

  • AA-LCRv1.1

    Reasoning across long documents

    • MiniMax M3: 83.0%Top
    • MiniMax M2.1: 67.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • MiniMax M3: 4.4%
    • MiniMax M2.1: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • MiniMax M3: 39.0%Top
    • MiniMax M2.1: 23.2%

  • GPQA Diamond

    Graduate-level science questions

    • MiniMax M3: 92.9%Top
    • MiniMax M2.1: 83.0%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • MiniMax M3: —
    • MiniMax M2.1: 87.5%

  • IFBench

    Following precise output instructions

    • MiniMax M3: 82.9%Top
    • MiniMax M2.1: 69.9%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • MiniMax M3: 2.0%
    • MiniMax M2.1: —

  • SciCode

    Scientific computing in Python

    • MiniMax M3: 47.1%
    • MiniMax M2.1: —

  • LiveCodeBench

    Recent competitive programming problems

    • MiniMax M3: —
    • MiniMax M2.1: 81.0%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • MiniMax M3: —
    • MiniMax M2.1: 82.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • MiniMax M3: 42.4%Top
    • MiniMax M2.1: 28.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • MiniMax M3: 88.9%Top
    • MiniMax M2.1: 85.4%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • MiniMax M3: 83.0%Top
    • MiniMax M2.1: 67.7%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
87.7 tok/s
Faster than 56% of 250 models
Time to first token
1.8 s
Quicker than 80% of 250 models
Prompt cache hits
87.8%
About $0.0719 per 1M input tokens with caching
Request success (24h)
99.9%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 98.3%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax/minimax-m3",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="minimax/minimax-m3",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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