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Mistral · Open weights

Ministral 3B

Added Dec 4, 2025Open weights

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Use in the app

Ministral 3B is a tiny, efficient 3B 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
$0.10
Output
$0.10

Reference prices as published by the model provider. API example

Specifications

Context window
131.1K
Max output
32.8K
Average output (7 days)
54 tokens
Longer than 4% of 468 models

Side by side

Ministral 3B compared

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

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Ministral 3BMistralThis modelMinistral 14BMistralRemove
ProviderMistralMistral
Discovery tierOpen weightsOpen weights
AddedDec 4, 2025Dec 4, 2025
Limits
Context window131.07K262.14KBest
Max output32.77K32.77K
Average output54 tokens176 tokens
Pricing, USD per 1M tokens
Input$0.10Best$0.20
Output$0.10Best$0.20
Benchmark indexes
Intelligence Index4.86.0Best
Coding Index4.814.4Best
Math Index22.030.0Best
Capabilities
Reasoning effort——

Public evaluations

Benchmarks

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

  • Ministral 3B
  • Ministral 14B

Intelligence Index

  • Ministral 3B4.8
  • Ministral 14B6.0

Ministral 3B: Better than 0% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Ministral 3B: 4.8
    • Ministral 14B: 14.4Top

  • Math Index

    Composite of the math evaluations

    • Ministral 3B: 22.0
    • Ministral 14B: 30.0Top

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Ministral 3B: 0.7%Top
    • Ministral 14B: 0.6%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

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

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Ministral 3B: —
    • Ministral 14B: 131 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Ministral 3B: 29 Elo
    • Ministral 14B: 247 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Ministral 3B: 0.0%
    • Ministral 14B: 1.2%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • Ministral 3B: 17.0%
    • Ministral 14B: 26.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

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

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Ministral 3B: 5.4%Top
    • Ministral 14B: 4.6%

  • GPQA Diamond

    Graduate-level science questions

    • Ministral 3B: 35.8%
    • Ministral 14B: 57.2%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Ministral 3B: 52.4%
    • Ministral 14B: 69.3%Top

  • IFBench

    Following precise output instructions

    • Ministral 3B: 26.8%
    • Ministral 14B: 32.0%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

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

  • SciCode

    Scientific computing in Python

    • Ministral 3B: 15.3%
    • Ministral 14B: 23.8%Top

  • LiveCodeBench

    Recent competitive programming problems

    • Ministral 3B: 24.7%
    • Ministral 14B: 35.1%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Ministral 3B: 22.0%
    • Ministral 14B: 30.0%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Ministral 3B: 0.0%
    • Ministral 14B: 4.5%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Ministral 3B: 24.9%
    • Ministral 14B: 27.2%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Ministral 3B: 17.0%
    • Ministral 14B: 26.3%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

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-3b-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-3b-2512",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

Other Mistral models, starting with the same family.