codestral-2508

completions

bymistralai

codestral-2508

completions

Published by: mistralaiProvider:

mistralai

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. [Blog Post](https://mistral.ai/news/codestral-25-08)

Released

Jan 13, 2025

Knowledge

Jul 17, 2024

Context

256K

Input

$0.3 / 1M tokens

Output

$0.9 / 1M tokens

Capabilities: tools

Accepts: text

Returns: text

Released Jan 13, 2025Knowledge Cutoff: Jul 17, 2024

Context: 256K Input: $0.3 / 1M tokensOutput: $0.9 / 1M tokensCapabilities: toolsAccepts: textReturns: text

Access codestral-2508 through LangDB AI Gateway

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Benchmark Results for codestral-2508

Category Performance Scores:

Academia: Score 22.20 (Top 89% - Rank #299)
Marketing: Score 35.08 (Top 77% - Rank #258)
Science: Score 33.44 (Top 91% - Rank #305)
Writing: Score 32.56 (Top 87% - Rank #292)

Overall Performance: 30.819861111111116 average score across all categories

Detailed Benchmark Scores:

Benchmark	Score	Percentile	Domain
AIME	4.30	Top 86%	Mathematics
AAII	13.20	Top 84%	General
GPQA	31.20	Top 93%	STEM (Physics, Chemistry, Biology)
HLE	4.50	Top 57%	General Knowledge
LiveCodeBench	24.30	Top 73%	Programming
MATH-500	60.70	Top 79%	Mathematics
MMLU-Pro	44.60	Top 93%	General Knowledge
SciCode	24.70	Top 63%	Scientific

GPQA Score: 31.20 - Graduate-level reasoning benchmark

Model Comparison:

Provider: mistralai

Model Type: completions

Context Size: 256000 tokens

Comparing against 335 models in the database

Category Scores

Benchmark Tests

View Other Benchmarks

AIME

4.3

Mathematics

AAII

13.2

General

GPQA

31.2

STEM (Physics, Chemistry, Biology)

HLE

4.5

General Knowledge

LiveCodeBench

24.3

Programming

MATH-500

60.7

Mathematics

MMLU-Pro

44.6

General Knowledge

SciCode

24.7

Scientific

Metric	AIME	AAII	GPQA	HLE	LiveCodeBench	MATH-500	MMLU-Pro	SciCode
Score	4.3	13.2	31.2	4.5	24.3	60.7	44.6	24.7

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Code Examples

Integration samples and API usage

Code Samples for codestral-2508

Python SDK Example:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.us-east-1.langdb.ai/projects/<your_project_id>",
    api_key="<your_api_key>"
)

response = client.chat.completions.create(
    model="codestral-2508",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ]
)

print(response.choices[0].message.content)

TypeScript SDK Example:

import OpenAI from 'openai';

const client = new OpenAI({
    baseURL: "https://api.us-east-1.langdb.ai/projects/<your_project_id>",
    apiKey: "<your_api_key>"
});

const response = await client.chat.completions.create({
    model: "codestral-2508",
    messages: [
        { role: "user", content: "Hello, how are you?" }
    ]
});

console.log(response.choices[0].message.content);

cURL Example:

curl -X POST "https://api.us-east-1.langdb.ai/projects/<your_project_id>/v1/chat/completions" \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "codestral-2508",
    "messages": [
        {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

Model: codestral-2508

Provider: mistralai

API Endpoint: $https://api.us-east-1.langdb.ai

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codestral-2508

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Access codestral-2508 through LangDB AI Gateway

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Benchmark Tests

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