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Mistral · Balanced

Codestral 2508

Added Aug 1, 2025

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Use in the app

Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation.

Input
Text
Output
Text
Discovery tier
Balanced

Pricing

USD per 1M tokens
Input
$0.30
Output
$0.90

Reference prices as published by the model provider. API example

Specifications

Context window
256K
Max output
32.8K
Average output (7 days)
124 tokens
Longer than 12% of 468 models

Side by side

Codestral 2508 compared

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

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Codestral 2508MistralThis modelMistral Small 3 24B (2501)MistralRemove
ProviderMistralMistral
Discovery tierBalancedOpen weights
AddedAug 1, 2025Sep 10, 2026
Limits
Context window256KBest32.77K
Max output32.77KBest8.19K
Average output124 tokens403 tokens
Pricing, USD per 1M tokens
Input$0.30$0.12Best
Output$0.90$0.35Best
Benchmark indexes
Intelligence Index—6.7
Math Index—4.3
Capabilities
ToolsNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Codestral 2508
  • Mistral Small 3 24B (2501)

Intelligence Index

  • Codestral 2508—
  • Mistral Small 3 24B (2501)6.7
3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Codestral 2508: —
    • Mistral Small 3 24B (2501): 4.3

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Codestral 2508: —
    • Mistral Small 3 24B (2501): 0.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Codestral 2508: —
    • Mistral Small 3 24B (2501): 3.8%

  • GPQA Diamond

    Graduate-level science questions

    • Codestral 2508: —
    • Mistral Small 3 24B (2501): 46.2%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Codestral 2508: —
    • Mistral Small 3 24B (2501): 65.2%

  • IFBench

    Following precise output instructions

    • Codestral 2508: —
    • Mistral Small 3 24B (2501): 26.4%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Codestral 2508: —
    • Mistral Small 3 24B (2501): 25.2%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Codestral 2508: —
    • Mistral Small 3 24B (2501): 4.3%

  • AIME

    Competition mathematics

    • Codestral 2508: —
    • Mistral Small 3 24B (2501): 8.0%

  • MATH-500

    Competition math problems

    • Codestral 2508: —
    • Mistral Small 3 24B (2501): 71.5%

Legacy evaluations

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Codestral 2508: —
    • Mistral Small 3 24B (2501): 19.6%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Codestral 2508: —
    • Mistral Small 3 24B (2501): 0.0%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

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/codestral-2508",
    "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/codestral-2508",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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