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Mistral · Balanced

Mistral Large 2411

Added Jan 1, 2024

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Use in the app

Upgrade to Mistral's flagship model. It is fluent in English, French, Spanish, German, and Italian, with high grammatical accuracy, with a long context window.

Input
Text
Output
Text
Discovery tier
Balanced

Pricing

USD per 1M tokens
Input
$2.01
Output
$6.00
Cache read
$0.20

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
102.4K
Average output (7 days)
955 tokens
Longer than 69% of 468 models

Side by side

Mistral Large 2411 compared

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

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Mistral Large 2411MistralThis modelLlama 3.2 3b InstructMetaRemove
ProviderMistralMeta
Discovery tierBalancedOpen weights
AddedJan 1, 2024Jan 1, 2024
Limits
Context window128K131.07KBest
Max output102.4KBest8.19K
Average output955 tokens119 tokens
Pricing, USD per 1M tokens
Input$2.01$0.0306Best
Output$6.00$0.0493Best
Cache read$0.20—
Performance
Throughput—165.2 tok/s
Time to first token—0.6 s
Request success (24h)100.0%100.0%
Benchmark indexes
Intelligence Index5.8Best5.7
Math Index—3.3
Capabilities
PDF inputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Mistral Large 2411
  • Llama 3.2 3b Instruct

Intelligence Index

  • Mistral Large 24115.8
  • Llama 3.2 3b Instruct5.7

Mistral Large 2411: Better than 3% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Mistral Large 2411: —
    • Llama 3.2 3b Instruct: 3.3

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Large 2411: —
    • Llama 3.2 3b Instruct: 4.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Large 2411: 3.5%
    • Llama 3.2 3b Instruct: 5.3%Top

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Large 2411: 35.1%Top
    • Llama 3.2 3b Instruct: 25.5%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Large 2411: 51.5%Top
    • Llama 3.2 3b Instruct: 34.7%

  • IFBench

    Following precise output instructions

    • Mistral Large 2411: —
    • Llama 3.2 3b Instruct: 26.2%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Large 2411: 17.8%Top
    • Llama 3.2 3b Instruct: 8.3%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Large 2411: —
    • Llama 3.2 3b Instruct: 3.3%

  • AIME

    Competition mathematics

    • Mistral Large 2411: 0.0%
    • Llama 3.2 3b Instruct: 6.7%Top

  • MATH-500

    Competition math problems

    • Mistral Large 2411: 52.7%Top
    • Llama 3.2 3b Instruct: 48.9%

Legacy evaluations

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Large 2411: —
    • Llama 3.2 3b Instruct: 21.1%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Large 2411: —
    • Llama 3.2 3b Instruct: 4.3%

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.

Request success (24h)
100.0%
Reliable
Hourly request success · last 72 hoursLowest hour 100.0%
  • 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",
    "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",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

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