Llama 4 vs Mistral Large
2026 - Pricing, benchmarks, and use case comparison
Quick take
- •Llama 4 is open-weights - free to self-host with no API costs. Mistral Large requires paid API access.
- •Llama 4 has a 10M context window - 78x larger than Mistral Large's 128K. Better for long documents and large codebases.
- •Llama 4 is open-source: fine-tune it, self-host it, or use any inference provider. Mistral Large is closed-source.
Specs comparison
| Llama 4 | Mistral Large | |
|---|---|---|
| Provider | Meta | Mistral AI |
| Type | Open source | Closed source |
| Context window | ✓10M | 128K |
| Input / 1M tokens | ✓Free (self-host) | $3.00 |
| Output / 1M tokens | Free (self-host) | $9.00 |
| Release date | 2025-04 | 2024-07 |
Benchmarks
| Benchmark | Llama 4 | Mistral Large |
|---|---|---|
| MMLU | ~85% | 84.0% |
| HumanEval | - | 92.0% |
Scores sourced from official provider release posts.
Strengths
Llama 4
- ✓Fully open weights - no usage restrictions
- ✓10M context in Llama 4 Scout variant
- ✓Native multimodal support
- ✓Strong performance relative to size
- ✓Enormous ecosystem of community tools and fine-tunes
Mistral Large
- ✓Native multilingual support across 12+ languages
- ✓Reliable function calling and tool use
- ✓Strong coding across 80+ programming languages
- ✓EU-based inference for data compliance
- ✓128K context at competitive pricing
Which should you choose?
Choose Llama 4 if you need...
- →Self-hosted and on-premise deployments
- →Privacy-sensitive workloads
- →Custom fine-tuning
- →Researchers and open-source builders
Choose Mistral Large if you need...
- →Multilingual European deployments
- →Tool-calling and agentic pipelines
- →Code generation and review
- →GDPR-compliant AI applications