Llama 4 vs Mistral Large
Pricing, benchmarks, and use case comparison
Quick take
- •Llama 4 is meaningfully stronger at long context (95 vs 70 on our capability index).
- •Mistral Large is meaningfully stronger at reasoning (85 vs 74).
- •Llama 4 is open-weights (free to self-host); Mistral Large is paid API only.
- •Llama 4 has a Up to 10M tokens (Scout); ~1M tokens (Maverick) context window vs 128000 - better for whole-repo or long-document work.
Specs comparison
| Llama 4 | Mistral Large | |
|---|---|---|
| Provider | Meta | Mistral AI |
| Type | Open source | Closed source |
| Context window | ✓Up to 10M tokens (Scout); ~1M tokens (Maverick) | 128000 |
| Input / 1M tokens | ✓Free (self-host) | 2.00 |
| Output / 1M tokens | Free (self-host) | 6.00 |
| Release date | 2025-04 | 2024-02 |
Benchmarks
| Benchmark | Llama 4 | Mistral Large |
|---|---|---|
| Scout context window | 10M tokens | - |
| Scout size | 17B active / 109B total (16 experts) | - |
| Maverick size | 17B active / 400B total (128 experts) | - |
| MMLU | - | 84.0% |
| HumanEval | - | 92.0% |
Scores sourced from official provider release posts and independent benchmark aggregators.
Which should you choose?
Choose Llama 4 if...
- →You need extremely long context in an open model (Scout's 10M window)
- →Self-hosted or on-prem multimodal deployment
- →You want an efficient MoE that activates few parameters per token
- →Fine-tuning or full control over the model
Choose Mistral Large if...
- →You need a strong European-built flagship with open weights
- →Your work is multilingual or requires nuanced reasoning
- →You want structured/JSON output and solid coding ability
- →You need the option to self-host for data sovereignty