Mistral 7B
A solid pick for self-hosting and full data control.
Overview
Mistral 7B Instruct v0.3 is the instruct-tuned version of Mistral AI's open-weights model, released under an Apache 2.0 licence that allows unrestricted commercial use. With just 7.25 billion parameters and a 32k-token context window, it runs comfortably on a laptop at Q4 quantisation, making it a solid pick for sovereign, on-premise deployment in Europe where data never leaves the building. It supports native function calling for agentic workflows, but ships without any built-in moderation layer, so guardrails are entirely on you.
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
- Lagging on Arena
- 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
- Local assistant on laptop
- Prototyping agents via function calling
- Sovereign on-prem deployment
- Low-cost internal chatbot
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/mistral-7b)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.
6/8 documented · 2/8 primary sources
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 4.1 GB | 7 GB | 16GB PC / M1+ Mac / 8GB GPU |
| Q8 · balanced | 7.8 GB | 11 GB | 32GB PC / 24GB Mac / RTX 4070 Ti+ |
| FP16 · max quality | 14.5 GB | 18 GB | 32GB PC / 24GB Mac / RTX 4070 Ti+ |
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.
Easiest way to try it on a workstation. Install Ollama, then:
ollama run mistral:7bAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | 1110 |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |