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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 modelMistral Small 4 119B ThinkingMistralRemove
ProviderMistralMistral
Discovery tierBalancedOpen weights
AddedJun 2, 2026Mar 17, 2026
Limits
Context window256K262.14KBest
Max output32.77KBest16.38K
Average output—997 tokens
Pricing, USD per 1M tokens
Input$0.30Best$0.40
Output$0.90Best$1.40
Performance
Throughput—126.8 tok/s
Time to first token—3.0 s
Request success (24h)—95.9%
Benchmark indexes
Intelligence Index—11.3
Coding Index—26.6
Capabilities
ReasoningNoYes
ToolsYesYes
VisionNoYes
Structured outputYesYes
Reasoning effort—none, high

Public evaluations

Benchmarks

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

  • Mistral Code Latest
  • Mistral Small 4 119B Thinking

Intelligence Index

  • Mistral Code Latest—
  • Mistral Small 4 119B Thinking11.3
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 26.6

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 1.0%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 0.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 241 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 362 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 0.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 49.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 1.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 9.9%

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 76.9%

  • IFBench

    Following precise output instructions

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 48.2%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 0.0%

  • SciCode

    Scientific computing in Python

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 38.8%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 17.4%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 41.2%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Code Latest: —
    • Mistral Small 4 119B Thinking: 49.7%

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

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