Head to head
Mistral Large 3 vs Llama 4 Maverick
Which is better, Mistral Large 3 or Llama 4 Maverick?
Mistral Large 3 finishes ahead of Llama 4 Maverick on the index, 82.9 to 80.4. It is the stronger of the two on Craft, while Llama 4 Maverick still leads on Speed. Both are scored on the same rubric, in the same week, and neither placing is sponsored.
Updated
Mistral Large 3
14 of 53
82.9
Europe's answer, published under Apache 2.0, with a quarter of a million tokens of context.
Llama 4 Maverick
23 of 53
80.4
The open-weights default: good enough, cheap to serve, supported everywhere.
The four scores, side by side
| Score | Mistral Large 3 | Llama 4 Maverick | Difference |
|---|---|---|---|
| Craft | 78 | 73 | +5 |
| Speed | 80 | 84 | +4 |
| Control | 93 | 92 | +1 |
| Value | 90 | 90 | — |
| Index score | 82.9 | 80.4 | +2.5 |
Which to pick
Pick Mistral Large 3 unless Speed is what decides it for you, which is exactly where Llama 4 Maverick is the better answer.
Mistral Large 3
Large 3 is a sparse model that activates a fraction of its parameters per token, which is how Mistral gets frontier-adjacent quality out of something you can actually host. The licence is Apache 2.0 with no conditions attached, the context runs to a quarter of a million tokens, and for European teams it answers the data-residency question without a legal review. It is a step behind the very top on hard reasoning.
Where it shines
- Apache 2.0 with no user, revenue or field-of-use clauses
- Sparse design keeps serving costs sane for its quality
- EU-hosted API option, which several sectors require
Llama 4 Maverick
Maverick is the mixture-of-experts middle of Llama 4 and the model most self-hosting teams land on. It is multimodal, it activates a small fraction of its parameters per token so it serves affordably, and every inference server, quantisation format and fine-tuning toolkit supports it on day one. That ecosystem, rather than the benchmarks, is the reason to pick it.
Where it shines
- Supported by every serving stack and quantisation tool that matters
- Mixture-of-experts design keeps serving costs down
- Largest fine-tuning community of any open family