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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 modelMinistral 3 14BMistralRemove
ProviderMistralMistral
Discovery tierOpen weightsOpen weights
AddedDec 4, 2025Dec 2, 2025
Limits
Context window262.14K262.14K
Max output32.77K32.77K
Average output40 tokens203 tokens
Pricing, USD per 1M tokens
Input$0.15$0.10Best
Output$0.15Best$0.40
Performance
Request success (24h)100.0%100.0%
Benchmark indexes
Intelligence Index5.56.0Best
Coding Index9.714.4Best
Math Index31.7Best30.0
Capabilities
VisionNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Ministral 8B
  • Ministral 3 14B

Intelligence Index

  • Ministral 8B5.5
  • Ministral 3 14B6.0

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
    • Ministral 3 14B: 14.4Top

  • Math Index

    Composite of the math evaluations

    • Ministral 8B: 31.7Top
    • Ministral 3 14B: 30.0

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Ministral 8B: 0.5%
    • Ministral 3 14B: 0.6%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Ministral 8B: 0.0%
    • Ministral 3 14B: 0.2%Top

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Ministral 8B: 77 Elo
    • Ministral 3 14B: 131 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Ministral 8B: 213 Elo
    • Ministral 3 14B: 247 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Ministral 8B: 0.8%
    • Ministral 3 14B: 1.2%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • Ministral 8B: 25.7%
    • Ministral 3 14B: 26.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Ministral 8B: 0.0%
    • Ministral 3 14B: 2.2%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Ministral 8B: 4.3%
    • Ministral 3 14B: 4.6%Top

  • GPQA Diamond

    Graduate-level science questions

    • Ministral 8B: 47.1%
    • Ministral 3 14B: 57.2%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Ministral 8B: 64.2%
    • Ministral 3 14B: 69.3%Top

  • IFBench

    Following precise output instructions

    • Ministral 8B: 29.1%
    • Ministral 3 14B: 32.0%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Ministral 8B: 0.0%Top
    • Ministral 3 14B: 0.0%Top

  • SciCode

    Scientific computing in Python

    • Ministral 8B: 20.7%
    • Ministral 3 14B: 23.8%Top

  • LiveCodeBench

    Recent competitive programming problems

    • Ministral 8B: 30.3%
    • Ministral 3 14B: 35.1%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Ministral 8B: 31.7%Top
    • Ministral 3 14B: 30.0%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Ministral 8B: 4.5%Top
    • Ministral 3 14B: 4.5%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Ministral 8B: 26.6%
    • Ministral 3 14B: 27.2%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Ministral 8B: 25.7%
    • Ministral 3 14B: 26.3%Top

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

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