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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 modelReka Flash 3rekaRemove
ProviderMistralreka
Discovery tierBalancedOpen weights
AddedJan 1, 2024Oct 15, 2026
Limits
Context window128KBest65.54K
Max output102.4KBest65.54K
Average output955 tokens2.7K tokens
Pricing, USD per 1M tokens
Input$2.01$0.11Best
Output$6.00$0.21Best
Cache read$0.20—
Performance
Request success (24h)100.0%100.0%
Benchmark indexes
Intelligence Index5.8Best5.6
Math Index—33.7
Capabilities
ReasoningNoYes
Structured outputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Mistral Large 2411
  • Reka Flash 3

Intelligence Index

  • Mistral Large 24115.8
  • Reka Flash 35.6

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: —
    • Reka Flash 3: 33.7

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Large 2411: —
    • Reka Flash 3: 0.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Large 2411: 3.5%
    • Reka Flash 3: 4.4%Top

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Large 2411: 35.1%
    • Reka Flash 3: 52.9%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Large 2411: 51.5%
    • Reka Flash 3: 66.9%Top

  • IFBench

    Following precise output instructions

    • Mistral Large 2411: —
    • Reka Flash 3: 30.4%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Large 2411: 17.8%
    • Reka Flash 3: 43.5%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Large 2411: —
    • Reka Flash 3: 33.7%

  • AIME

    Competition mathematics

    • Mistral Large 2411: 0.0%
    • Reka Flash 3: 51.0%Top

  • MATH-500

    Competition math problems

    • Mistral Large 2411: 52.7%
    • Reka Flash 3: 89.3%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Large 2411: —
    • Reka Flash 3: 0.0%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Large 2411: —
    • Reka Flash 3: 0.0%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Large 2411: —
    • Reka Flash 3: 0.0%

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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