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MiniMax · Efficient

MiniMax M3.1 Flash

Added Oct 19, 2026

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Use in the app

MiniMax M3.1 Flash is a multimodal model for coding, tool use, and long-context work. It accepts text, images, and video and produces text, with a 1,000,000-token context window and up to 524,288 output tokens within the remaining context. Thinking is always enabled, with low, medium, high, xhigh, and max effort levels; max is the default. Thinking tokens count toward the output limit. Temperature is fixed at 1 and top-p at 0.95; custom values have no effect.

  • Reasoning
  • Tools
  • Vision
  • Video input
Input
Text, Images, Video
Output
Text
Reasoning effort
low · medium · high · xhigh · max
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$0.15
Output
$0.60
Cache read
$0.015

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
524.3K
Average output (7 days)
1.4K tokens
Longer than 82% of 468 models

Side by side

MiniMax M3.1 Flash compared

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

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MiniMax M3.1 FlashMiniMaxThis modelMiniMax M3MiniMaxRemove
ProviderMiniMaxMiniMax
Discovery tierEfficientOpen weights
AddedOct 19, 2026Jun 1, 2026
Limits
Context window1.05MBest512K
Max output524.29KBest80K
Average output1.4K tokens491 tokens
Pricing, USD per 1M tokens
Input$0.15Best$0.23
Output$0.60Best$0.96
Cache read$0.015Best$0.05
Performance
Throughput119.8 tok/s202.4 tok/sBest
Time to first token7.0 s2.8 sBest
Request success (24h)99.7%99.9%Best
Benchmark indexes
Intelligence Index42.7Best29.2
Coding Index—58.6
Capabilities
ReasoningYesNo
ToolsYesYes
VisionYesYes
Structured outputNoYes
Video inputYesNo
Reasoning effortlow, medium, high, xhigh, max—

Public evaluations

Benchmarks

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

  • MiniMax M3.1 Flash
  • MiniMax M3

Intelligence Index

  • MiniMax M3.1 Flash42.7
  • MiniMax M329.2

MiniMax M3.1 Flash: Better than 88% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • MiniMax M3.1 Flash: —
    • MiniMax M3: 58.6

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • MiniMax M3.1 Flash: 72.5%Top
    • MiniMax M3: 21.3%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • MiniMax M3.1 Flash: 38.1%Top
    • MiniMax M3: 4.4%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • MiniMax M3.1 Flash: —
    • MiniMax M3: 0.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • MiniMax M3.1 Flash: 1,473 EloTop
    • MiniMax M3: 1,089 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • MiniMax M3.1 Flash: 1,458 EloTop
    • MiniMax M3: 1,243 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • MiniMax M3.1 Flash: 20.8%Top
    • MiniMax M3: 9.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • MiniMax M3.1 Flash: 79.7%
    • MiniMax M3: 83.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • MiniMax M3.1 Flash: 16.7%Top
    • MiniMax M3: 4.4%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • MiniMax M3.1 Flash: 48.0%Top
    • MiniMax M3: 39.0%

  • GPQA Diamond

    Graduate-level science questions

    • MiniMax M3.1 Flash: —
    • MiniMax M3: 92.9%

  • IFBench

    Following precise output instructions

    • MiniMax M3.1 Flash: —
    • MiniMax M3: 82.9%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • MiniMax M3.1 Flash: 25.8%Top
    • MiniMax M3: 2.0%

  • SciCode

    Scientific computing in Python

    • MiniMax M3.1 Flash: 53.4%Top
    • MiniMax M3: 47.1%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • MiniMax M3.1 Flash: —
    • MiniMax M3: 42.4%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • MiniMax M3.1 Flash: —
    • MiniMax M3: 88.9%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • MiniMax M3.1 Flash: 79.7%
    • MiniMax M3: 83.0%Top

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
119.8 tok/s
Faster than 68% of 254 models
Time to first token
7.0 s
Quicker than 22% of 254 models
Prompt cache hits
78.4%
About $0.0441 per 1M input tokens with caching
Request success (24h)
99.7%
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 92.6%
  • 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-m3.1-flash",
    "reasoning_effort": "medium",
    "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-m3.1-flash",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

MiniMax

Other MiniMax models, starting with the same family.