Mistral Large 3 vs Llama 5
Side-by-side comparison of pricing, context window, modalities, licensing, and strengths — with practical guidance on which model fits which workload.
| Spec | Mistral Large 3 | Llama 5 |
|---|---|---|
| Provider | Mistral | Meta |
| Context window | 256K tokens | 5M tokens |
| Input price (per 1M tokens) | Free / Self-host | Free / Self-host |
| Output price (per 1M tokens) | Free / Self-host | Free / Self-host |
| Example workload (10M in + 2M out) | Free (self-hosted — infra costs only) | Free (self-hosted — infra costs only) |
| Modalities | text, image, code | text, image, code |
| Knowledge cutoff | Not disclosed | Not disclosed |
| Release date | Dec 2025 | Apr 2026 |
| License | Apache 2.0 | Llama Community License |
| Strengths | Multilingual (200+ languages), 675B MoE (41B active), Apache 2.0, European | 5M context, Open weights, 600B params, Fine-tune ecosystem |
| Open weights | Yes | Yes |
Verdict
Europe’s champion against Meta’s juggernaut. Mistral Large 3 (675B MoE, 41B active) made waves by shipping under Apache 2.0 — a genuinely unrestricted license — with standout multilingual coverage across 200+ languages. Llama 5 is the larger ecosystem play: a 5M-token context window, more community fine-tunes and serving tooling than any other open model, and Meta’s sustained backing. License nuance matters here: Apache 2.0 (Mistral) has no usage conditions at all, while Meta’s community license carries acceptable-use terms.
When to choose Mistral Large 3
Pick Mistral Large 3 for multilingual products, EU data-sovereignty requirements, and truly unrestricted Apache 2.0 licensing.
When to choose Llama 5
Pick Llama 5 for massive-context work, the richest fine-tuning ecosystem, and broadest deployment tooling.
Prices and specs reflect published provider information and change frequently — always confirm on the provider's pricing page before committing to a workload.
Frequently asked questions
Which is cheaper: Mistral Large 3 or Llama 5?
One of these models has open weights, so API pricing isn't directly comparable — self-hosting shifts the cost to infrastructure. For the managed model, see the per-token prices in the table above.
Which has the larger context window?
Llama 5 supports 5M tokens versus 256K for Mistral Large 3 — roughly 19.5× more room for documents, code, and conversation history.
Can I self-host either model?
Mistral Large 3 has downloadable weights (Apache 2.0), so you can run it on your own hardware. Both models are open-weights.
How should I test which model is better for my use case?
Benchmarks are a starting point, not an answer. Run both models on 20–50 examples of your real task and compare outputs blind. Use our token counter to estimate prompt sizes and the cost calculator to project monthly spend before committing.