Llama 4
Leading Intelligence. Unrivaled speed and efficiency. The most accessible and scalable generation of Llama is here.
Llama 4 models
Our models are optimized for easy deployment, cost efficiency, and performance that scales to billions of users.
Llama 4 Scout
Class-leading natively multimodal model that offers superior text and visual intelligence, single H100 GPU efficiency, and a 10M context window for seamless long document analysis. Download
Llama 4 Maverick
Industry-leading natively multimodal model for image and text understanding with groundbreaking intelligence and fast responses at a low cost. Download
Llama 4 capabilities
- Natively multimodal
- Unparalleled long context
- Expert image grounding
All Llama 4 models are designed with native multimodality, leveraging early fusion that allows us to pre-train the model with large amounts of unlabeled text and vision tokens - a step change in intelligence from separate, frozen multimodal weights.
How to guides:
Video tutorials:
Build a Multimodal WhatsApp Chatbot
Start building with Llama 4
Protections in the era of generative AI.
Comprehensive system-level protections proactively identify and mitigate potential risks, empowering developers to more easily deploy generative AI responsibly.
Protection tools accessible to everyone
Llama 4 benchmark
| Task | Metric | Llama 4 Maverick | Llama 4 Scout |
|---|---|---|---|
| Image Reasoning | MMMU | 73.4 | 69.4 |
| MathVista | 73.7 | 70.7 | |
| Image Understanding | ChartQA | 90 | 88.8 |
| DocVQA (test) | 94.4 | 94.4 | |
| Coding | LiveCodeBench (10.01.2024 - 02.01.2025) | 43.4 | 32.8 |
| Reasoning & Knowledge | MMLU Pro | 80.5 | 74.3 |
| DocVQA (test) | 69.8 | 57.2 | |
| Multilingual | Multilingual MMLU | 84.6 | - |
| Long Context | MTOB (half book) | eng->kgv/kgv->eng | 54.0 / 46.4 |
| eng->kgv/kgv->eng | 42.2 / 36.6 | ||
| MTOB (full book) | eng->kgv/kgv->eng | 50.8 / 46.7 | |
| eng->kgv/kgv->eng | 39.7 / 36.3 |
Methodology & Notes
- For Llama model results, we report 0 shot evaluation with temperature = 0 and no majority voting or parallel test time compute. For high-variance benchmarks (GPQA Diamond, LiveCodeBench), we average over multiple generations to reduce uncertainty.
- Specialized long context evals are not traditionally reported for generalist models, so we share internal runs to showcase llama's frontier performance.
- $0.19/Mtok (3:1 blended) is our cost estimate for Llama 4 Maverick assuming distributed inference. On a single host, we project the model can be served at $0.30 - $0.49/Mtok (3:1 blended).