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MiniMax · Open weights

MiniMax M2.1

Added Jan 20, 2025Open weights

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Use in the app

MiniMax M2.1 builds on M2 with enhanced context understanding and improved complex tool use. 230B parameter MoE model (10B active) optimized for agentic workflows and long-horizon tasks.

  • Reasoning
  • Tools
  • Structured output
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.30
Output
$1.20
Cache read
$0.03

Reference prices as published by the model provider. API example

Specifications

Context window
200K
Max output
131.1K
Average output (7 days)
359 tokens
Longer than 37% of 465 models

Side by side

MiniMax M2.1 compared

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

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MiniMax M2.1MiniMaxThis modelMiniMax M2.7MiniMaxRemove
ProviderMiniMaxMiniMax
Discovery tierOpen weightsOpen weights
AddedJan 20, 2025Mar 18, 2026
Limits
Context window200K204.8KBest
Max output131.07K131.07K
Average output359 tokens556 tokens
Pricing, USD per 1M tokens
Input$0.30$0.21Best
Output$1.20$0.84Best
Cache read$0.03Best$0.042
Performance
Throughput—24.3 tok/s
Time to first token—0.9 s
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index20.922.8Best
Coding Index—52.6
Math Index82.7—
Capabilities
ReasoningYesYes
ToolsYesYes
Structured outputYesYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • MiniMax M2.1
  • MiniMax M2.7

Intelligence Index

  • MiniMax M2.120.9
  • MiniMax M2.722.8

MiniMax M2.1: Better than 51% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • MiniMax M2.1: —
    • MiniMax M2.7: 52.6

  • Math Index

    Composite of the math evaluations

    • MiniMax M2.1: 82.7
    • MiniMax M2.7: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • MiniMax M2.1: —
    • MiniMax M2.7: 4.4%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • MiniMax M2.1: —
    • MiniMax M2.7: 0.3%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • MiniMax M2.1: —
    • MiniMax M2.7: 718 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • MiniMax M2.1: —
    • MiniMax M2.7: 1,008 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • MiniMax M2.1: —
    • MiniMax M2.7: 6.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • MiniMax M2.1: 67.7%
    • MiniMax M2.7: 78.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • MiniMax M2.1: —
    • MiniMax M2.7: 5.6%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • MiniMax M2.1: 23.2%
    • MiniMax M2.7: 29.6%Top

  • GPQA Diamond

    Graduate-level science questions

    • MiniMax M2.1: 83.0%
    • MiniMax M2.7: 87.4%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • MiniMax M2.1: 87.5%
    • MiniMax M2.7: —

  • IFBench

    Following precise output instructions

    • MiniMax M2.1: 69.9%
    • MiniMax M2.7: 75.7%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • MiniMax M2.1: —
    • MiniMax M2.7: 0.0%

  • SciCode

    Scientific computing in Python

    • MiniMax M2.1: —
    • MiniMax M2.7: 50.1%

  • LiveCodeBench

    Recent competitive programming problems

    • MiniMax M2.1: 81.0%
    • MiniMax M2.7: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • MiniMax M2.1: 82.7%
    • MiniMax M2.7: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • MiniMax M2.1: 28.8%
    • MiniMax M2.7: 39.4%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • MiniMax M2.1: 85.4%Top
    • MiniMax M2.7: 84.8%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • MiniMax M2.1: 67.7%
    • MiniMax M2.7: 78.3%Top

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/minimax-m2.1",
    "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-m2.1",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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