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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

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TryHermes 3 70BPhi 4 MultimodalCeleris 1All 3

Public evaluations

Benchmarks

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

Intelligence Index

6.0

Better than 3% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    14.4

    Composite of the coding evaluations

    Better than 8% of 168 scored models

  • Math Index

    30.0

    Composite of the math evaluations

    Better than 27% of 118 scored models

Agentic work

  • AutomationBench-AA

    0.6%

    Workflow automation, penalized for guardrail violations

    Better than 6% of 173 scored models

  • AutomationBench-AA tasks completed

    0.2%

    Workflows finished end to end without a guardrail violation

    Better than 13% of 173 scored models

  • AA-Briefcasev1.1

    131 Elo

    Agentic knowledge work, rated head to head

    Better than 1% of 166 scored models

  • GDPval-AAv2.1

    247 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 2% of 187 scored models

Document reasoning

  • GDP.pdf

    1.2%

    Professional reasoning over PDF documents, all-pass rate

    Better than 4% of 171 scored models

  • AA-LCRv1.1

    26.3%

    Reasoning across long documents

    Better than 11% of 309 scored models

  • MLCR-AA

    2.2%

    Long-context reasoning over medical records

    Better than 5% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    4.6%

    Expert-level questions across academic fields

    Better than 13% of 323 scored models

  • GPQA Diamond

    57.2%

    Graduate-level science questions

    Better than 15% of 291 scored models

  • MMLU-Pro

    69.3%

    Multi-subject knowledge and reasoning

    Better than 19% of 132 scored models

  • IFBench

    32.0%

    Following precise output instructions

    Better than 9% of 215 scored models

Coding

  • Terminal-Benchv4.0

    0.0%

    Practical software engineering in a terminal

    Better than 0% of 171 scored models

  • SciCode

    23.8%

    Scientific computing in Python

    Better than 4% of 174 scored models

  • LiveCodeBench

    35.1%

    Recent competitive programming problems

    Better than 27% of 129 scored models

Math

  • AIME 2025

    30.0%

    Competition mathematics, 2025 problems

    Better than 27% of 118 scored models

Legacy evaluations

  • Terminal-Bench Hard

    4.5%

    Earlier terminal task suite

    Better than 10% of 212 scored models

  • τ²-Bench Telecom

    27.2%

    Tool use in a customer-support simulation

    Better than 18% of 214 scored models

  • AA-LCR (unversioned)

    26.3%

    Long-context reasoning, earlier grading

    Better than 11% of 309 scored models

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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