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Mistral · Open weights

Ministral 8B

Added Dec 4, 2025Open weights

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Use in the app

Ministral 8B is an efficient 8B parameter model from Mistral AI with vision capabilities, designed for edge deployment.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.15
Output
$0.15

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
32.8K
Average output (7 days)
40 tokens
Longer than 2% of 468 models

Side by side

Ministral 8B compared

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

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Ministral 8BMistralThis modelMistral Small 3 24B (2501)MistralRemove
ProviderMistralMistral
Discovery tierOpen weightsOpen weights
AddedDec 4, 2025Sep 10, 2026
Limits
Context window262.14KBest32.77K
Max output32.77KBest8.19K
Average output40 tokens403 tokens
Pricing, USD per 1M tokens
Input$0.15$0.12Best
Output$0.15Best$0.35
Performance
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index5.56.7Best
Coding Index9.7—
Math Index31.7Best4.3
Capabilities
ToolsNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Ministral 8B
  • Mistral Small 3 24B (2501)

Intelligence Index

  • Ministral 8B5.5
  • Mistral Small 3 24B (2501)6.7

Ministral 8B: Better than 1% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Ministral 8B: 9.7
    • Mistral Small 3 24B (2501): —

  • Math Index

    Composite of the math evaluations

    • Ministral 8B: 31.7Top
    • Mistral Small 3 24B (2501): 4.3

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Ministral 8B: 0.5%
    • Mistral Small 3 24B (2501): —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Ministral 8B: 0.0%
    • Mistral Small 3 24B (2501): —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Ministral 8B: 77 Elo
    • Mistral Small 3 24B (2501): —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Ministral 8B: 213 Elo
    • Mistral Small 3 24B (2501): —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Ministral 8B: 0.8%
    • Mistral Small 3 24B (2501): —

  • AA-LCRv1.1

    Reasoning across long documents

    • Ministral 8B: 25.7%Top
    • Mistral Small 3 24B (2501): 0.0%

  • MLCR-AA

    Long-context reasoning over medical records

    • Ministral 8B: 0.0%
    • Mistral Small 3 24B (2501): —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Ministral 8B: 4.3%Top
    • Mistral Small 3 24B (2501): 3.8%

  • GPQA Diamond

    Graduate-level science questions

    • Ministral 8B: 47.1%Top
    • Mistral Small 3 24B (2501): 46.2%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Ministral 8B: 64.2%
    • Mistral Small 3 24B (2501): 65.2%Top

  • IFBench

    Following precise output instructions

    • Ministral 8B: 29.1%Top
    • Mistral Small 3 24B (2501): 26.4%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Ministral 8B: 0.0%
    • Mistral Small 3 24B (2501): —

  • SciCode

    Scientific computing in Python

    • Ministral 8B: 20.7%
    • Mistral Small 3 24B (2501): —

  • LiveCodeBench

    Recent competitive programming problems

    • Ministral 8B: 30.3%Top
    • Mistral Small 3 24B (2501): 25.2%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Ministral 8B: 31.7%Top
    • Mistral Small 3 24B (2501): 4.3%

  • AIME

    Competition mathematics

    • Ministral 8B: —
    • Mistral Small 3 24B (2501): 8.0%

  • MATH-500

    Competition math problems

    • Ministral 8B: —
    • Mistral Small 3 24B (2501): 71.5%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Ministral 8B: 4.5%
    • Mistral Small 3 24B (2501): —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Ministral 8B: 26.6%Top
    • Mistral Small 3 24B (2501): 19.6%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Ministral 8B: 25.7%Top
    • Mistral Small 3 24B (2501): 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.

Prompt cache hits
0.0%
Request success (24h)
100.0%
Reliable

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.

Hourly request success · last 72 hoursLowest hour 28.5%
  • 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/ministral-8b-2512",
    "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/ministral-8b-2512",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

Other Mistral models, starting with the same family.