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MiniMax · Open weights

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 modelGemini 2.5 FlashGoogleRemove
ProviderMiniMaxGoogle
Discovery tierOpen weightsEfficient
AddedJan 8, 2025Jun 5, 2025
Limits
Context window1M1.05MBest
Max output131.07KBest65.54K
Average output—368 tokens
Pricing, USD per 1M tokens
Input$0.14Best$0.30
Output$1.33Best$2.50
Cache read—$0.03
Performance
Throughput—249.0 tok/s
Time to first token—4.9 s
Request success (24h)—99.4%
Benchmark indexes
Intelligence Index10.0Best9.9
Math Index13.760.3Best
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Audio inputNoYes
Video inputNoYes
Reasoning effort—low, medium, high

Public evaluations

Benchmarks

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

  • MiniMax M1
  • Gemini 2.5 Flash

Intelligence Index

  • MiniMax M110.0
  • Gemini 2.5 Flash9.9

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
    • Gemini 2.5 Flash: 60.3Top

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • MiniMax M1: —
    • Gemini 2.5 Flash: 49.9%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • MiniMax M1: 7.8%Top
    • Gemini 2.5 Flash: 4.7%
  • GPQA Diamond

    Graduate-level science questions

    • MiniMax M1: 68.2%
    • Gemini 2.5 Flash: 68.3%Top
  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • MiniMax M1: 80.8%
    • Gemini 2.5 Flash: 80.9%Top
  • IFBench

    Following precise output instructions

    • MiniMax M1: 41.2%Top
    • Gemini 2.5 Flash: 39.0%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • MiniMax M1: 65.7%Top
    • Gemini 2.5 Flash: 49.5%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • MiniMax M1: 13.7%
    • Gemini 2.5 Flash: 60.3%Top
  • AIME

    Competition mathematics

    • MiniMax M1: 81.3%Top
    • Gemini 2.5 Flash: 50.0%
  • MATH-500

    Competition math problems

    • MiniMax M1: 97.2%Top
    • Gemini 2.5 Flash: 93.2%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • MiniMax M1: 2.3%
    • Gemini 2.5 Flash: 12.1%Top
  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • MiniMax M1: 31.6%Top
    • Gemini 2.5 Flash: 14.9%
  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • MiniMax M1: —
    • Gemini 2.5 Flash: 49.9%

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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