NOUMENOpen app
NOUMEN

MiniMax · Open weights

MiniMax M2.7

Added Mar 18, 2026Open weights

Compare models

Choose up to 3 models to compare.0 of 3 chosen

Loading models…

Use in the app

MiniMax M2.7 is the first model deeply involved in iterating on its own training. It excels in real-world software engineering (SWE-Pro 56.22%), end-to-end project delivery (VIBE-Pro 55.6%), and complex office workflows with strong tool-use compliance and agentic capabilities.

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

Pricing

USD per 1M tokens
Input
$0.21
Output
$0.84
Cache read
$0.042

Reference prices as published by the model provider. API example

Specifications

Context window
204.8K
Max output
131.1K
Average output (7 days)
556 tokens
Longer than 52% of 465 models

Side by side

Compare models

Put MiniMax M2.7 next to up to three other models. The comparison stays in the link, so you can share it.

Compare models

Choose up to 3 models to compare.0 of 3 chosen

Loading models…

TryNvidia Nemotron 3 Ultra 550BQwen3.5 27B TEEGPT 5All 3

Public evaluations

Benchmarks

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

Intelligence Index

22.8

Better than 55% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    52.6

    Composite of the coding evaluations

    Better than 51% of 168 scored models

Agentic work

  • AutomationBench-AA

    4.4%

    Workflow automation, penalized for guardrail violations

    Better than 23% of 173 scored models

  • AutomationBench-AA tasks completed

    0.3%

    Workflows finished end to end without a guardrail violation

    Better than 19% of 173 scored models

  • AA-Briefcasev1.1

    718 Elo

    Agentic knowledge work, rated head to head

    Better than 24% of 166 scored models

  • GDPval-AAv2.1

    1,008 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 35% of 187 scored models

Document reasoning

  • GDP.pdf

    6.0%

    Professional reasoning over PDF documents, all-pass rate

    Better than 25% of 171 scored models

  • AA-LCRv1.1

    78.3%

    Reasoning across long documents

    Better than 69% of 309 scored models

  • MLCR-AA

    5.6%

    Long-context reasoning over medical records

    Better than 18% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    29.6%

    Expert-level questions across academic fields

    Better than 61% of 323 scored models

  • GPQA Diamond

    87.4%

    Graduate-level science questions

    Better than 70% of 291 scored models

  • IFBench

    75.7%

    Following precise output instructions

    Better than 87% of 215 scored models

Coding

  • Terminal-Benchv4.0

    0.0%

    Practical software engineering in a terminal

    Better than 0% of 171 scored models

  • SciCode

    50.1%

    Scientific computing in Python

    Better than 47% of 174 scored models

Legacy evaluations

  • Terminal-Bench Hard

    39.4%

    Earlier terminal task suite

    Better than 76% of 212 scored models

  • τ²-Bench Telecom

    84.8%

    Tool use in a customer-support simulation

    Better than 65% of 214 scored models

  • AA-LCR (unversioned)

    78.3%

    Long-context reasoning, earlier grading

    Better than 69% of 309 scored models

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
52.2 tok/s
Faster than 36% of 247 models
Time to first token
1.6 s
Quicker than 87% of 247 models
Prompt cache hits
77.2%
About $0.0803 per 1M input tokens with caching
Request success (24h)
100.0%
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 70.5%
  • 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-m2.7",
    "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-m2.7",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

MiniMax

Other MiniMax models, starting with the same family.