Mistral · Open weights
Ministral 3 14B
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.
| Ministral 3 14BMistralThis model | Mistral Small 3 24B (2501)MistralRemove | |
|---|---|---|
| Provider | Mistral | Mistral |
| Discovery tier | Open weights | Open weights |
| Added | Dec 2, 2025 | Sep 10, 2026 |
| Limits | ||
| Context window | 262.14KBest | 32.77K |
| Max output | 32.77KBest | 8.19K |
| Average output | 203 tokens | 403 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.10Best | $0.12 |
| Output | $0.40 | $0.35Best |
| Performance | ||
| Request success (24h) | 100.0% | — |
| Benchmark indexes | ||
| Intelligence Index | 6.0 | 6.7Best |
| Coding Index | 14.4 | — |
| Math Index | 30.0Best | 4.3 |
| Capabilities | ||
| Tools | No | Yes |
| Vision | Yes | No |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Ministral 3 14B
- Mistral Small 3 24B (2501)
Intelligence Index
- Ministral 3 14B6.0
- Mistral Small 3 24B (2501)6.7
Ministral 3 14B: Better than 3% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Ministral 3 14B: 14.4
- Mistral Small 3 24B (2501): —
Math Index
Composite of the math evaluations
- Ministral 3 14B: 30.0Top
- Mistral Small 3 24B (2501): 4.3
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Ministral 3 14B: 0.6%
- Mistral Small 3 24B (2501): —
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Ministral 3 14B: 0.2%
- Mistral Small 3 24B (2501): —
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Ministral 3 14B: 131 Elo
- Mistral Small 3 24B (2501): —
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Ministral 3 14B: 247 Elo
- Mistral Small 3 24B (2501): —
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Ministral 3 14B: 1.2%
- Mistral Small 3 24B (2501): —
AA-LCRv1.1
Reasoning across long documents
- Ministral 3 14B: 26.3%Top
- Mistral Small 3 24B (2501): 0.0%
MLCR-AA
Long-context reasoning over medical records
- Ministral 3 14B: 2.2%
- Mistral Small 3 24B (2501): —
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Ministral 3 14B: 4.6%Top
- Mistral Small 3 24B (2501): 3.8%
GPQA Diamond
Graduate-level science questions
- Ministral 3 14B: 57.2%Top
- Mistral Small 3 24B (2501): 46.2%
MMLU-Pro
Multi-subject knowledge and reasoning
- Ministral 3 14B: 69.3%Top
- Mistral Small 3 24B (2501): 65.2%
IFBench
Following precise output instructions
- Ministral 3 14B: 32.0%Top
- Mistral Small 3 24B (2501): 26.4%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Ministral 3 14B: 0.0%
- Mistral Small 3 24B (2501): —
SciCode
Scientific computing in Python
- Ministral 3 14B: 23.8%
- Mistral Small 3 24B (2501): —
LiveCodeBench
Recent competitive programming problems
- Ministral 3 14B: 35.1%Top
- Mistral Small 3 24B (2501): 25.2%
Math
AIME 2025
Competition mathematics, 2025 problems
- Ministral 3 14B: 30.0%Top
- Mistral Small 3 24B (2501): 4.3%
AIME
Competition mathematics
- Ministral 3 14B: —
- Mistral Small 3 24B (2501): 8.0%
MATH-500
Competition math problems
- Ministral 3 14B: —
- Mistral Small 3 24B (2501): 71.5%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Ministral 3 14B: 4.5%
- Mistral Small 3 24B (2501): —
τ²-Bench Telecom
Tool use in a customer-support simulation
- Ministral 3 14B: 27.2%Top
- Mistral Small 3 24B (2501): 19.6%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Ministral 3 14B: 26.3%Top
- Mistral Small 3 24B (2501): 0.0%
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.
- 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.
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
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