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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 modelGemma 3 4B ITGoogleRemove
ProviderMistralGoogle
Discovery tierOpen weightsOpen weights
AddedDec 4, 2025Jan 1, 2024
Limits
Context window131.07KBest128K
Max output32.77KBest8.19K
Average output54 tokens115 tokens
Pricing, USD per 1M tokens
Input$0.10Best$0.20
Output$0.10Best$0.20
Performance
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index4.84.8
Coding Index4.8Best2.7
Math Index22.0Best12.7
Capabilities
PDF inputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Ministral 3B
  • Gemma 3 4B IT

Intelligence Index

  • Ministral 3B4.8
  • Gemma 3 4B IT4.8

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: 4.8Top
    • Gemma 3 4B IT: 2.7

  • Math Index

    Composite of the math evaluations

    • Ministral 3B: 22.0Top
    • Gemma 3 4B IT: 12.7

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Ministral 3B: 0.7%
    • Gemma 3 4B IT: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Ministral 3B: 0.0%
    • Gemma 3 4B IT: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Ministral 3B: 29 Elo
    • Gemma 3 4B IT: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Ministral 3B: 0.0%
    • Gemma 3 4B IT: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Ministral 3B: 17.0%Top
    • Gemma 3 4B IT: 6.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Ministral 3B: 0.0%
    • Gemma 3 4B IT: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Ministral 3B: 5.4%Top
    • Gemma 3 4B IT: 5.3%

  • GPQA Diamond

    Graduate-level science questions

    • Ministral 3B: 35.8%Top
    • Gemma 3 4B IT: 29.1%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Ministral 3B: 52.4%Top
    • Gemma 3 4B IT: 41.7%

  • IFBench

    Following precise output instructions

    • Ministral 3B: 26.8%
    • Gemma 3 4B IT: 28.3%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Ministral 3B: 0.0%
    • Gemma 3 4B IT: —

  • SciCode

    Scientific computing in Python

    • Ministral 3B: 15.3%
    • Gemma 3 4B IT: —

  • LiveCodeBench

    Recent competitive programming problems

    • Ministral 3B: 24.7%Top
    • Gemma 3 4B IT: 11.2%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Ministral 3B: 22.0%Top
    • Gemma 3 4B IT: 12.7%

  • AIME

    Competition mathematics

    • Ministral 3B: —
    • Gemma 3 4B IT: 6.3%

  • MATH-500

    Competition math problems

    • Ministral 3B: —
    • Gemma 3 4B IT: 76.6%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Ministral 3B: 0.0%
    • Gemma 3 4B IT: 0.8%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Ministral 3B: 24.9%Top
    • Gemma 3 4B IT: 5.0%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Ministral 3B: 17.0%Top
    • Gemma 3 4B IT: 6.7%

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.