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Mistral · Balanced

Mistral Code Latest

Added Jun 2, 2026

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Use in the app

Mistral Code Latest is Mistral's direct API alias for codestral-2508, a low-latency coding model for code generation, completion, fill-in-the-middle workflows, function calling, and structured output.

  • Tools
  • Structured output
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

Side by side

Mistral Code Latest compared

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

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Mistral Code LatestMistralThis modelQwen3 VL 235B A22B Instruct OriginalQwenRemove
ProviderMistralQwen
Discovery tierBalancedBalanced
AddedJun 2, 2026Sep 25, 2025
Limits
Context window256K—
Max output32.77K32.77K
Average output—288 tokens
Pricing, USD per 1M tokens
Input$0.30$0.30
Output$0.90Best$1.90
Performance
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index—9.9
Math Index—70.7
Capabilities
ToolsYesNo
VisionNoYes
Structured outputYesNo
Reasoning effort——

Public evaluations

Benchmarks

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

  • Mistral Code Latest
  • Qwen3 VL 235B A22B Instruct Original

Intelligence Index

  • Mistral Code Latest—
  • Qwen3 VL 235B A22B Instruct Original9.9
3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Mistral Code Latest: —
    • Qwen3 VL 235B A22B Instruct Original: 70.7

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Code Latest: —
    • Qwen3 VL 235B A22B Instruct Original: 32.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Code Latest: —
    • Qwen3 VL 235B A22B Instruct Original: 6.6%

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Code Latest: —
    • Qwen3 VL 235B A22B Instruct Original: 71.2%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Code Latest: —
    • Qwen3 VL 235B A22B Instruct Original: 82.3%

  • IFBench

    Following precise output instructions

    • Mistral Code Latest: —
    • Qwen3 VL 235B A22B Instruct Original: 42.7%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Code Latest: —
    • Qwen3 VL 235B A22B Instruct Original: 59.4%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Code Latest: —
    • Qwen3 VL 235B A22B Instruct Original: 70.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Code Latest: —
    • Qwen3 VL 235B A22B Instruct Original: 6.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Code Latest: —
    • Qwen3 VL 235B A22B Instruct Original: 35.1%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Code Latest: —
    • Qwen3 VL 235B A22B Instruct Original: 32.7%

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
0.0%

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.

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

Mistral

Other Mistral models, starting with the same family.