Llama vs Mistral
The two leading open-weight LLM families compared — license, context, hosted pricing and where each one wins. Updated for 2026.
| Spec | Llama | Mistral |
|---|---|---|
| Maker | Meta AI | Mistral AI (France) |
| Flagship model | Llama 4 Maverick / 405B | Mistral Large 2, Codestral, Magistral |
| License | Llama Community License (custom) | Apache 2.0 (most), commercial for Large |
| Context window | Up to 1M (Llama 4) | 128k (Mistral Large) |
| Hosted price (input) | ~$0.20–$2.70 / Mtok | ~$0.25–$3 / Mtok |
| Self-hostable | Yes — weights on Hugging Face | Yes — most weights on Hugging Face |
| Best for | General reasoning, agents, broad ecosystem | Cost-efficient inference, code (Codestral), EU-hosted |
| Where to run | Together, Fireworks, Replicate, Bedrock, Groq | Mistral API, Together, Bedrock, Vertex |
When to pick Llama
- You want the broadest hosted ecosystem (Groq, Together, Fireworks, Bedrock).
- You need very long context (Llama 4 reaches 1M tokens).
- You're building general-purpose assistants and agents.
When to pick Mistral
- You want the most permissive open license (Apache 2.0).
- You're shipping a code product and want a dedicated code model (Codestral).
- You need EU data residency or sovereign hosting.
FAQ
Is Llama or Mistral better for coding?
Mistral's Codestral is a dedicated code model and is very competitive at low cost. Llama 4 is the stronger generalist, but for a code-only workload Codestral often wins on price/performance.
Which is more open?
Mistral is more permissively licensed — most of its open weights ship under Apache 2.0. Llama uses Meta's custom community license, which is liberal but adds restrictions for products with over 700M monthly users.
Which is cheaper to run?
Both are dramatically cheaper than closed frontier models. On hosted APIs Mistral and Llama trade blows; if you self-host, Llama's broader ecosystem (Groq, Fireworks, Together) often gives lower-latency options.
Which has the longer context window?
Llama 4 wins, going up to a 1M-token context. Mistral Large currently caps at 128k tokens — plenty for most chat and RAG workloads, but smaller for whole-codebase prompts.