NOUMENOpen app
NOUMEN

Mistral · Open weights

Mistral Large 4

Added Oct 6, 2026Open weights

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Use in the app

Mistral Large 4 is an open-weight multimodal mixture-of-experts model with 1.05T total and 49B active parameters, built for reasoning, coding, tool use, and structured output. It supports a 512K-token context window.

  • Reasoning
  • Tools
  • Vision
  • Structured output
Input
Text, Images
Output
Text
Reasoning effort
none · high
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.68
Output
$2.09
Cache read
$0.07

Reference prices as published by the model provider. API example

Specifications

Context window
524.3K
Max output
—

Side by side

Mistral Large 4 compared

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

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Mistral Large 4MistralThis modelMistral Code Agent LatestMistralRemove
ProviderMistralMistral
Discovery tierOpen weightsOpen weights
AddedOct 6, 2026Jun 2, 2026
Limits
Context window524.29KBest262.14K
Max output—32.77K
Average output—316 tokens
Pricing, USD per 1M tokens
Input$0.68$0.40Best
Output$2.09$2.00Best
Cache read$0.07—
Performance
Request success (24h)68.9%100.0%Best
Benchmark indexes
Intelligence Index—8.6
Coding Index—31.3
Math Index—36.7
Capabilities
ReasoningYesNo
ToolsYesYes
VisionYesNo
Structured outputYesYes
Reasoning effortnone, high—

Public evaluations

Benchmarks

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

  • Mistral Large 4
  • Mistral Code Agent Latest

Intelligence Index

  • Mistral Large 4—
  • Mistral Code Agent Latest8.6
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 31.3

  • Math Index

    Composite of the math evaluations

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 36.7

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 3.1%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 0.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 482 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 544 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 2.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 32.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 3.6%

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 59.4%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 76.2%

  • IFBench

    Following precise output instructions

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 38.1%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 0.0%

  • SciCode

    Scientific computing in Python

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 32.8%

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 44.8%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 36.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 18.9%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 24.9%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Large 4: —
    • Mistral Code Agent Latest: 32.3%

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.

Prompt cache hits
50.7%
About $0.37 per 1M input tokens with caching
Request success (24h)
69.2%
Failing

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 5.1%
  • 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": "mistralai/mistral-large-4",
    "reasoning_effort": "none",
    "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="mistralai/mistral-large-4",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

Other Mistral models, starting with the same family.