Ministral 3 14B
A solid pick for self-hosting and full data control.
Overview
Ministral 3 14B is the largest open model in Mistral AI's Ministral 3 family: a 13.5B-parameter language model paired with a 0.4B vision encoder, released under Apache 2.0. It matches heavier models like Mistral Small 3.2 24B on reasoning and instruction benchmarks while fitting in 24GB of VRAM in FP8, so a single desktop GPU is enough to run it. Its 256k-token context and native agentic tool-calling make it a solid pick for local assistants and agent pipelines. Limit: explicit step-by-step reasoning is reserved for the separate 'Reasoning' variant, not this instruct model. Open weights and a permissive licence mean it can run fully on-premise, keeping data inside Europe.
Skill profile
Not disclosed
Three independent measures
What people prefer, what the model can do, what the market uses. Each source has its biases and none is enough on its own: we show them side by side, never blended.
Strengths
- Open-source and self-hostable
Limitations
- API pricing not disclosed
Who is it for
- you want to control cost or self-host
- your data must stay in the EU
Ideal use cases
- Private AI assistants
- Local agentic tool use
- Multilingual document analysis
- Vision-enabled chatbots
- Custom fine-tuning
Access & availability
Key specifications
What it costs per month
Estimate from sourced API prices and a hardware tier for self-hosting. Assumptions are editable.
Assumptions: 36-month amortisation, 0.25 €/kWh, 22 days/month, 1 $ = 0.92 €. Excludes labour, software licences, redundancy.
nAIvigate Trust Index
What the vendor offers and documents for professional use in Europe. An undocumented criterion counts as zero and stays visible: the score rises when the vendor publishes, not when we assume.
19/19 criteria documented · 100 % · 12 of them set by rule
See the criteria▾
Trust Index badge
Show this score in your docs, README or security page. It updates automatically.
[](https://naivigate.com/en/model/ministral-3-14b)Data & compliance
What we know, line by line, with source and date. These lines feed the Data and Enterprise families of the Trust Index above. GDPR compliance depends on your processing, not on the model alone.
| EU data residency | Yes |
| Data processing agreement (DPA) | N/A· self-hosted: data never leaves |
| Transfer outside the EU | No |
| Training on your data | No |
| Configurable retention | N/A· self-hosted: data never leaves |
| Zero Data Retention | N/A· self-hosted: data never leaves |
| Self-hostable | Yes |
| Declared hosting | self |
Quality of this card’s data
This score measures the quality of the data on this card: price, context, Elo, licence, documented, sourced and fresh. It measures neither the model nor its compliance: for that, see the Trust Index above.
5/8 documented · 1/8 primary sources
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 8 GB | 11 GB | 32GB PC / 24GB Mac / RTX 4070 Ti+ |
| Q8 · balanced | 15 GB | 18 GB | 32GB PC / 24GB Mac / RTX 4070 Ti+ |
| FP16 · max quality | 28 GB | 33 GB | 64GB Mac / dual 24GB GPUs |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve mistralai/Ministral-3-14B-Instruct-2512 \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |