Mistral · Open weights
Ministral 14B
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.
| Ministral 14BMistralThis model | Ministral 8BMistralRemove | |
|---|---|---|
| Provider | Mistral | Mistral |
| Discovery tier | Open weights | Open weights |
| Added | Dec 4, 2025 | Dec 4, 2025 |
| Limits | ||
| Context window | 262.14K | 262.14K |
| Max output | 32.77K | 32.77K |
| Average output | 176 tokens | 40 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.20 | $0.15Best |
| Output | $0.20 | $0.15Best |
| Performance | ||
| Request success (24h) | — | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | 6.0Best | 5.5 |
| Coding Index | 14.4Best | 9.7 |
| Math Index | 30.0 | 31.7Best |
| Capabilities | ||
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Ministral 14B
- Ministral 8B
Intelligence Index
- Ministral 14B6.0
- Ministral 8B5.5
Ministral 14B: Better than 3% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Ministral 14B: 14.4Top
- Ministral 8B: 9.7
Math Index
Composite of the math evaluations
- Ministral 14B: 30.0
- Ministral 8B: 31.7Top
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Ministral 14B: 0.6%Top
- Ministral 8B: 0.5%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Ministral 14B: 0.2%Top
- Ministral 8B: 0.0%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Ministral 14B: 131 EloTop
- Ministral 8B: 77 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Ministral 14B: 247 EloTop
- Ministral 8B: 213 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Ministral 14B: 1.2%Top
- Ministral 8B: 0.8%
AA-LCRv1.1
Reasoning across long documents
- Ministral 14B: 26.3%Top
- Ministral 8B: 25.7%
MLCR-AA
Long-context reasoning over medical records
- Ministral 14B: 2.2%Top
- Ministral 8B: 0.0%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Ministral 14B: 4.6%Top
- Ministral 8B: 4.3%
GPQA Diamond
Graduate-level science questions
- Ministral 14B: 57.2%Top
- Ministral 8B: 47.1%
MMLU-Pro
Multi-subject knowledge and reasoning
- Ministral 14B: 69.3%Top
- Ministral 8B: 64.2%
IFBench
Following precise output instructions
- Ministral 14B: 32.0%Top
- Ministral 8B: 29.1%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Ministral 14B: 0.0%Top
- Ministral 8B: 0.0%Top
SciCode
Scientific computing in Python
- Ministral 14B: 23.8%Top
- Ministral 8B: 20.7%
LiveCodeBench
Recent competitive programming problems
- Ministral 14B: 35.1%Top
- Ministral 8B: 30.3%
Math
AIME 2025
Competition mathematics, 2025 problems
- Ministral 14B: 30.0%
- Ministral 8B: 31.7%Top
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Ministral 14B: 4.5%Top
- Ministral 8B: 4.5%Top
τ²-Bench Telecom
Tool use in a customer-support simulation
- Ministral 14B: 27.2%Top
- Ministral 8B: 26.6%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Ministral 14B: 26.3%Top
- Ministral 8B: 25.7%
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.
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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