Mistral AI vs Groq
Both are free/open-source alternatives to OpenAI API (ChatGPT). Here's how they stack up — verified facts, no spin.
Mistral AI
Open-weight Apache-2.0 models with an EU-hosted API — the exit ramp stays open.
Mistral is the middle path between a closed API and full self-hosting: many of its models (Ministral 3B/8B/14B, Small, Devstral, Magistral Small and others) ship as open weights under Apache-2.0, so anything you build on La Plateforme's API can later move onto your own hardware — the exit ramp is built in. The API is EU-hosted (a real advantage for GDPR-sensitive workloads) and aggressively priced, from $0.04 per million tokens for Ministral 3B up to $2/$6 for Mistral Large.
Groq
The speed king — open models at 500+ tokens/second on custom LPU chips.
Groq runs open models (Llama and friends) on its custom LPU hardware and is, as of mid-2026, the fastest mainstream inference API available — 500+ tokens per second, at prices mostly under $1 per million tokens (Llama 3.3 70B at $0.59/$0.79). If your product's bottleneck is latency — voice agents, live UX, rapid tool loops — Groq is the honest answer. It's a proprietary hosted platform, but like Together, the models themselves are open, so you're renting speed, not locking in your stack.
Side by side
| Mistral AI | Groq | |
|---|---|---|
| Sovereignty Score | 70 | 42 |
| Open source | Yes | No |
| Self-hostable | Yes | No |
| Local-first | No | No |
| License | Apache-2.0 (open-weight models); proprietary API | Proprietary (platform); serves open-weight models |
| Pricing | Open weights free to self-host; API from $0.04/1M tokens (Ministral 3B), Large at $2/$6 per 1M | Most models under $1 per 1M tokens; Llama 3.3 70B $0.59/$0.79; batch −50% |
Mistral AI edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Mistral AI
Strengths
- +Open Apache-2.0 weights — you can take the model home
- +EU data residency (GDPR-friendly by default)
- +Very competitive pricing across the range
- +Codestral for code, Ministral for edge/cheap workloads
Trade-offs
- −Top-end quality trails the leading US frontier models
- −Which models are open vs API-only varies — check per model
Groq
Strengths
- +Fastest inference on the market (500+ tok/s)
- +Very low prices on open models
- +OpenAI-compatible API — near drop-in
Trade-offs
- −Hosted-only; custom hardware means no self-host path for the speed
- −Model catalog is narrower than Together's
More OpenAI API (ChatGPT) head-to-heads
Facts verified 2026-07-04. Licenses and pricing change — spotted something out of date? That's a correction we want.