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Mistral · Open weights

Ministral 14B

Added Dec 4, 2025Open weights

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Use in the app

Ministral 14B is a powerful 14B parameter model from Mistral AI with vision capabilities, offering frontier performance in a compact size.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.20
Output
$0.20

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
32.8K
Average output (7 days)
176 tokens
Longer than 18% of 468 models

Side by side

Ministral 14B compared

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

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Ministral 14BMistralThis modelMistral Large 4MistralRemove
ProviderMistralMistral
Discovery tierOpen weightsOpen weights
AddedDec 4, 2025Oct 6, 2026
Limits
Context window262.14K524.29KBest
Max output32.77K—
Average output176 tokens—
Pricing, USD per 1M tokens
Input$0.20Best$0.68
Output$0.20Best$2.09
Cache read—$0.07
Performance
Request success (24h)—69.3%
Benchmark indexes
Intelligence Index6.0—
Coding Index14.4—
Math Index30.0—
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
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 14B
  • Mistral Large 4

Intelligence Index

  • Ministral 14B6.0
  • Mistral Large 4—

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

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Ministral 14B: 14.4
    • Mistral Large 4: —

  • Math Index

    Composite of the math evaluations

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

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

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

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

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

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

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

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

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

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

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

  • AA-LCRv1.1

    Reasoning across long documents

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

  • MLCR-AA

    Long-context reasoning over medical records

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

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

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

  • GPQA Diamond

    Graduate-level science questions

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

  • MMLU-Pro

    Multi-subject knowledge and reasoning

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

  • IFBench

    Following precise output instructions

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

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

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

  • SciCode

    Scientific computing in Python

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

  • LiveCodeBench

    Recent competitive programming problems

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

Math

  • AIME 2025

    Competition mathematics, 2025 problems

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

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

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

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

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

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

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

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-14b-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-2512",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

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