Mistral vs DeepSeek
The two open-weight champions solve opposite problems. Scores from the RXed AI Periodic Table v1.0 — identical 20-element scale, deterministic overall. How we audit →
At a glance
Scope = elements of the AI stack in the tool’s stated scope (dashed = honestly out of scope, never a penalty). Quality = how well it scores on what it covers. No single number — both axes, always.
The verdict
Mistral (Scope 18/20 · Quality 7/10) is a platform: chat, coding agents, document AI, fine-tuning (Ft 8) and EU data residency in Paris by default — the strongest sovereignty story in AI, at prices well under the US frontier. DeepSeek (Scope 17/20 · Quality 5/10) is an engine: frontier-grade reasoning (Th 8.5, Lg 9) at roughly a tenth of US prices, MIT-licensed weights you can run anywhere — and almost nothing around it (Tr 2, Gr 2, Ev 2), with hosted-service China data residency that is a hard no for sensitive work. An EU SMB that wants one accountable vendor → Mistral. A builder with their own platform who wants the cheapest frontier-class brain to self-host → DeepSeek weights on EU/US infrastructure, never the hosted app for sensitive data.
Where the quality sits
Family mean over covered elements only — a dash means the family is honestly outside that tool’s scope.
| Mistral AI Scope 18/20 · Quality 7/10 audited 2026-07-08 | DeepSeek Scope 17/20 · Quality 5/10 audited 2026-07-10 | |
|---|---|---|
| Vendor | Mistral AI | DeepSeek (Hangzhou DeepSeek Artificial Intelligence, backed by High-Flyer) |
| Origin | FR | CN |
| Pricing | Vibe: Free · Pro $14.99 (~€14) · Team $24.99/user (~€23) · Enterprise custom · Students $5.99 (~€6) per month; API pay-per-token (Mistral Large ~$2 in / $6 out per M tokens, 50% batch discount); open-weight models self-hostable (commercial deployments need a Mistral license for some models) | Chat app free · API pay-per-token: V4-Flash ~€0.12/€0.24 per 1M in/out (~€0.0024 cache-hit), V4-Pro ~€0.37/€0.75 · Open weights (MIT) free to self-host |
| Pr Prompts | 7 | 6 |
| Em Embeddings | 7 | 2 |
| Cx Context | 7 | 9 |
| Tr Tracing | 7 | 2 |
| Lg LLM | 8 | 9 |
| Fc Function calling | 8 | 7 |
| Vx Vector store | 6 | — |
| Rg RAG | 7 | 4 |
| Gr Guardrails | 7 | 2 |
| Mm Multimodal | 8 | 3 |
| Ag Agents | 8 | 6 |
| Ft Fine-tuning | 8 | 6 |
| Fw Frameworks & harnesses | 8 | 4 |
| Ev Evaluations | 6 | 2 |
| Sm Small models | 9 | 8 |
| Ma Multi-agent | 7 | — |
| Sy Synthetic data | — | — |
| Pc Protocols | 7 | 2 |
| In Interpretability | — | 5 |
| Th Thinking models | 7 | 9 |
Hover any score for the written reason. Full detail: Mistral AI passport → · DeepSeek passport →