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Mistral · Open weights

Ministral 3 14B

Added Dec 2, 2025Open weights

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Use in the app

Ministral 3 14B is a balanced model in the Ministral 3 family, designed for edge deployment. A powerful, efficient language model with vision capabilities, fine-tuned for instruction tasks. Features multilingual support, strong system prompt adherence, and native function calling. Apache 2.0 licensed.

  • Vision
Input
Text, Images
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.10
Output
$0.40

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
32.8K
Average output (7 days)
203 tokens
Longer than 22% of 468 models

Side by side

Ministral 3 14B compared

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

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Ministral 3 14BMistralThis modelMinistral 14BMistralRemove
ProviderMistralMistral
Discovery tierOpen weightsOpen weights
AddedDec 2, 2025Dec 4, 2025
Limits
Context window262.14K262.14K
Max output32.77K32.77K
Average output203 tokens176 tokens
Pricing, USD per 1M tokens
Input$0.10Best$0.20
Output$0.40$0.20Best
Performance
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index6.06.0
Coding Index14.414.4
Math Index30.030.0
Capabilities
VisionYesNo
Reasoning effort——

Public evaluations

Benchmarks

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

  • Ministral 3 14B
  • Ministral 14B

Intelligence Index

  • Ministral 3 14B6.0
  • Ministral 14B6.0

Ministral 3 14B: Better than 3% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

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

  • Math Index

    Composite of the math evaluations

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

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

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

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

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

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Ministral 3 14B: 131 EloTop
    • Ministral 14B: 131 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

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

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

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

  • AA-LCRv1.1

    Reasoning across long documents

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

  • MLCR-AA

    Long-context reasoning over medical records

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

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

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

  • GPQA Diamond

    Graduate-level science questions

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

  • MMLU-Pro

    Multi-subject knowledge and reasoning

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

  • IFBench

    Following precise output instructions

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

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

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

  • SciCode

    Scientific computing in Python

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

  • LiveCodeBench

    Recent competitive programming problems

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

Math

  • AIME 2025

    Competition mathematics, 2025 problems

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

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

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

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

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

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Ministral 3 14B: 26.3%Top
    • Ministral 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
24.9%
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 100.0%
  • 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-14b-instruct-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-14b-instruct-2512",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

Other Mistral models, starting with the same family.