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MiniMax · Open weights

MiniMax M3 Thinking

Added Jun 1, 2026Open weights

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Use in the app

MiniMax M3 Thinking is the adaptive-thinking version of MiniMax's open-weights frontier model for coding, agent workflows, tool use, long-context tasks, and native multimodal understanding. 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.

  • Reasoning
  • 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)
1.9K tokens
Longer than 90% of 465 models

Side by side

MiniMax M3 Thinking compared

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

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MiniMax M3 ThinkingMiniMaxThis modelGPT 6 LunaOpenAIRemove
ProviderMiniMaxOpenAI
Discovery tierOpen weightsEfficient
AddedJun 1, 2026Sep 22, 2026
Limits
Context window512K1.05MBest
Max output80K128KBest
Average output1.9K tokens120 tokens
Pricing, USD per 1M tokens
Input$0.23$0.10Best
Output$0.96$0.50Best
Cache read$0.05$0.01Best
Cache write—$0.13
Performance
Throughput218.4 tok/sBest68.2 tok/s
Time to first token7.2 s4.9 sBest
Request success (24h)99.9%Best99.8%
Benchmark indexes
Intelligence Index29.229.9Best
Coding Index58.6—
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputNoYes
Reasoning effort—none, low, medium, high, xhigh, max

Public evaluations

Benchmarks

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

  • MiniMax M3 Thinking
  • GPT 6 Luna

Intelligence Index

  • MiniMax M3 Thinking29.2
  • GPT 6 Luna29.9

MiniMax M3 Thinking: Better than 70% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • MiniMax M3 Thinking: 58.6
    • GPT 6 Luna: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • MiniMax M3 Thinking: 21.3%
    • GPT 6 Luna: 40.5%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • MiniMax M3 Thinking: 4.4%
    • GPT 6 Luna: 10.2%Top

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • MiniMax M3 Thinking: 0.0%
    • GPT 6 Luna: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • MiniMax M3 Thinking: 1,089 Elo
    • GPT 6 Luna: 1,097 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • MiniMax M3 Thinking: 1,243 Elo
    • GPT 6 Luna: 1,263 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • MiniMax M3 Thinking: 9.8%
    • GPT 6 Luna: 13.8%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • MiniMax M3 Thinking: 83.0%Top
    • GPT 6 Luna: 78.3%

  • MLCR-AA

    Long-context reasoning over medical records

    • MiniMax M3 Thinking: 4.4%
    • GPT 6 Luna: 18.3%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • MiniMax M3 Thinking: 39.0%Top
    • GPT 6 Luna: 28.3%

  • GPQA Diamond

    Graduate-level science questions

    • MiniMax M3 Thinking: 92.9%
    • GPT 6 Luna: —

  • IFBench

    Following precise output instructions

    • MiniMax M3 Thinking: 82.9%
    • GPT 6 Luna: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • MiniMax M3 Thinking: 2.0%
    • GPT 6 Luna: 2.5%Top

  • SciCode

    Scientific computing in Python

    • MiniMax M3 Thinking: 47.1%
    • GPT 6 Luna: 50.9%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • MiniMax M3 Thinking: 42.4%
    • GPT 6 Luna: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • MiniMax M3 Thinking: 88.9%
    • GPT 6 Luna: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • MiniMax M3 Thinking: 83.0%Top
    • GPT 6 Luna: 78.3%

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
218.4 tok/s
Faster than 86% of 251 models
Time to first token
7.2 s
Quicker than 18% of 251 models
Prompt cache hits
81.0%
About $0.0841 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 87.5%
  • 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:thinking",
    "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:thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

MiniMax

Other MiniMax models, starting with the same family.