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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 modelMistral Large 4MistralRemove
ProviderMistralMistral
Discovery tierOpen weightsOpen weights
AddedDec 2, 2025Oct 6, 2026
Limits
Context window262.14K524.29KBest
Max output32.77K—
Average output203 tokens—
Pricing, USD per 1M tokens
Input$0.10Best$0.68
Output$0.40Best$2.09
Cache read—$0.07
Performance
Request success (24h)100.0%Best68.8%
Benchmark indexes
Intelligence Index6.0—
Coding Index14.4—
Math Index30.0—
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesYes
Structured outputNoYes
Reasoning effort—none, high

Public evaluations

Benchmarks

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

  • Ministral 3 14B
  • Mistral Large 4

Intelligence Index

  • Ministral 3 14B6.0
  • Mistral Large 4—

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: 14.4
    • Mistral Large 4: —

  • Math Index

    Composite of the math evaluations

    • Ministral 3 14B: 30.0
    • Mistral Large 4: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Ministral 3 14B: 0.6%
    • Mistral Large 4: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Ministral 3 14B: 0.2%
    • Mistral Large 4: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Ministral 3 14B: 131 Elo
    • Mistral Large 4: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Ministral 3 14B: 247 Elo
    • Mistral Large 4: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Ministral 3 14B: 1.2%
    • Mistral Large 4: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Ministral 3 14B: 26.3%
    • Mistral Large 4: —

  • MLCR-AA

    Long-context reasoning over medical records

    • Ministral 3 14B: 2.2%
    • Mistral Large 4: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Ministral 3 14B: 4.6%
    • Mistral Large 4: —

  • GPQA Diamond

    Graduate-level science questions

    • Ministral 3 14B: 57.2%
    • Mistral Large 4: —

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Ministral 3 14B: 69.3%
    • Mistral Large 4: —

  • IFBench

    Following precise output instructions

    • Ministral 3 14B: 32.0%
    • Mistral Large 4: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Ministral 3 14B: 0.0%
    • Mistral Large 4: —

  • SciCode

    Scientific computing in Python

    • Ministral 3 14B: 23.8%
    • Mistral Large 4: —

  • LiveCodeBench

    Recent competitive programming problems

    • Ministral 3 14B: 35.1%
    • Mistral Large 4: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Ministral 3 14B: 30.0%
    • Mistral Large 4: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Ministral 3 14B: 4.5%
    • Mistral Large 4: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Ministral 3 14B: 27.2%
    • Mistral Large 4: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Ministral 3 14B: 26.3%
    • Mistral Large 4: —

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.