Llama 4 (Meta) vs Codestral 2 (Mistral)
Which one should you pick? Here's the full breakdown.
Llama 4 (Meta)
Meta's open-weights flagship family -- Scout (10M context), Maverick (multimodal 400B MoE), Behemoth in preview
Codestral 2 (Mistral)
Mistral's dedicated code model -- Codestral 2 (launched 2026-04-08) relicensed under Apache 2.0, removing the commercial-use restrictions of the original. 22B dense, strong FIM (fill-in-middle), available via Mistral API + Hugging Face
| Category | Llama 4 (Meta) | Codestral 2 (Mistral) |
|---|---|---|
| Ease of Use | 5.0 | 6.0 |
| Output Quality | 8.5 | 8.0 |
| Value | 9.0 | 9.0 |
| Features | 9.0 | 7.0 |
| Overall | 7.9 | 7.5 |
Pricing Comparison
| Feature | Llama 4 (Meta) | Codestral 2 (Mistral) |
|---|---|---|
| Free Tier | Yes | Yes |
| Starting Price | $0 | $0 |
Benchmark Head-to-Head
Llama 4 Maverick (17B/400B MoE) benchmarks — Codestral 2 (Mistral) has no published benchmarks
| Benchmark | Description | Score |
|---|---|---|
| MMLU-Pro | Harder multi-subject reasoning | 80.5% |
| GPQA Diamond | Graduate-level science questions | 69.8% |
| HumanEval | Python code generation | 88% |
| MMMU (multimodal) | 73.4% |
Which Should You Pick?
Pick Llama 4 (Meta) if...
- ✓More features (9 vs 7)
Developers and teams who need a permissively-licensed open-weights model with strong tooling, long context (Scout), or multimodal (Maverick). Safe default choice given the ecosystem.
Visit Llama 4 (Meta)Pick Codestral 2 (Mistral) if...
- ✓Easier to use (6 vs 5)
Developers and teams who want a legally-clean open-weights code model they can self-host OR hit via API, particularly those with EU data-residency requirements. Ideal for building in-house IDE extensions, code-review bots, or CI/CD AI integrations where the Apache 2.0 license removes procurement friction.
Visit Codestral 2 (Mistral)Our Verdict
Llama 4 (Meta) edges out Codestral 2 (Mistral) with a 7.9 vs 7.5 overall score. Both are solid picks, but Llama 4 (Meta) has the advantage in output quality.