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MiniMax M1

Added Jan 8, 2025Open weights

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Use in the app

MiniMax-M1 is a hybrid MoE reasoning model with 40K thinking budget. World's first open-weight, large-scale hybrid-attention model with lightning attention for efficient test-time compute scaling. Excels at complex tasks requiring extensive reasoning.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.14
Output
$1.33

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
131.1K

Side by side

MiniMax M1 compared

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

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MiniMax M1MiniMaxThis modelQwen3 VL 235B A22B Instruct OriginalQwenRemove
ProviderMiniMaxQwen
Discovery tierOpen weightsBalanced
AddedJan 8, 2025Sep 25, 2025
Limits
Context window1M—
Max output131.07KBest32.77K
Average output—288 tokens
Pricing, USD per 1M tokens
Input$0.14Best$0.30
Output$1.33Best$1.90
Performance
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index10.0Best9.9
Math Index13.770.7Best
Capabilities
VisionNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • MiniMax M1
  • Qwen3 VL 235B A22B Instruct Original

Intelligence Index

  • MiniMax M110.0
  • Qwen3 VL 235B A22B Instruct Original9.9

MiniMax M1: Better than 21% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • MiniMax M1: 13.7
    • Qwen3 VL 235B A22B Instruct Original: 70.7Top

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • MiniMax M1: —
    • Qwen3 VL 235B A22B Instruct Original: 32.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • MiniMax M1: 7.8%Top
    • Qwen3 VL 235B A22B Instruct Original: 6.6%

  • GPQA Diamond

    Graduate-level science questions

    • MiniMax M1: 68.2%
    • Qwen3 VL 235B A22B Instruct Original: 71.2%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • MiniMax M1: 80.8%
    • Qwen3 VL 235B A22B Instruct Original: 82.3%Top

  • IFBench

    Following precise output instructions

    • MiniMax M1: 41.2%
    • Qwen3 VL 235B A22B Instruct Original: 42.7%Top

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • MiniMax M1: 65.7%Top
    • Qwen3 VL 235B A22B Instruct Original: 59.4%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • MiniMax M1: 13.7%
    • Qwen3 VL 235B A22B Instruct Original: 70.7%Top

  • AIME

    Competition mathematics

    • MiniMax M1: 81.3%
    • Qwen3 VL 235B A22B Instruct Original: —

  • MATH-500

    Competition math problems

    • MiniMax M1: 97.2%
    • Qwen3 VL 235B A22B Instruct Original: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • MiniMax M1: 2.3%
    • Qwen3 VL 235B A22B Instruct Original: 6.8%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • MiniMax M1: 31.6%
    • Qwen3 VL 235B A22B Instruct Original: 35.1%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • MiniMax M1: —
    • Qwen3 VL 235B A22B Instruct Original: 32.7%

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

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-M1",
    "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-M1",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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