Meta Llama
Open-access large language model family designed for developers, researchers, and businesses to build, experiment, and responsibly scale their generative AI ideas
Overview
Meta Llama is an open-access large language model family that has become the foundation for generative AI innovation globally. Unlike proprietary models, Llama's weights are freely available for researchers and commercial use, fostering collaboration across developers, researchers, and organizations. The latest Llama 4 introduces mixture-of-experts (MoE) architecture with models like Scout (17B-16E) and Maverick (17B-128E), supporting up to 10 million token context windows and native multimodal capabilities. With hundreds of millions of downloads and thousands of community projects, Llama has built a broad open foundation model ecosystem.
The Verdict
Who Should Use Meta Llama?
Best For
- Researchers needing full model access
- Companies building proprietary AI products
- Teams requiring on-premise deployment
- Developers fine-tuning for specific domains
- Projects needing multimodal capabilities
Not Ideal For
- Users wanting simple API access (use hosted versions)
- Projects without GPU infrastructure
- Teams unfamiliar with model deployment
- Quick prototypes (hosted APIs faster to start)
What's Great
- Completely free and open-weight models
- Industry-leading 10M token context (Llama 4 Scout)
- MoE architecture for efficient inference
- Large ecosystem with broad platform support
- Native multimodal capabilities (vision + text)
- Commercial use permitted with license
Watch Out For
- Large models require significant GPU resources
- License requires acceptance via Meta website
- Self-hosting complexity vs managed APIs
- Llama 4 requires 4+ GPUs for full precision
- EU availability restrictions on some versions
Pricing
View all models & details
Llama 4 (April 2025)
- Scout-17B-16E — 10M context, MoE
- Maverick-17B-128E — 1M context, multimodal
- Native vision capabilities
- Mixture-of-experts architecture
Llama 3.x Series
- Llama 3.3 — 70B, 128K context
- Llama 3.2 — 1B, 3B, 11B, 90B (vision)
- Llama 3.1 — 8B, 70B, 405B, 128K context
- Llama 3 — 8B, 70B, 8K context
Specialized Models
- Llama Guard 4 — Safety classifier
- Code Llama — Code generation
- Purple Llama — Security tools
- Llama Stack — Full toolchain
Languages Supported
- English, Spanish, French, German
- Italian, Portuguese, Hindi
- Thai, Vietnamese, Indonesian
- Arabic, Filipino (Tagalog)
Benchmarks
Real-World Usage
Community Stats
- Reference implementation in the llama repo
- 34M+ HuggingFace downloads
- Model weights and cards in the llama-models repo
- Thousands of community projects
Ecosystem Support
- AWS Bedrock, Azure, Google Cloud
- Hugging Face Transformers
- vLLM, TGI, Ollama
- LangChain, LlamaIndex
How It Compares
| Feature | Meta Llama | Mistral | Qwen | DeepSeek |
|---|---|---|---|---|
| Max Context | 10M tokens | 128K tokens | 128K tokens | 128K tokens |
| Largest Model | 405B (3.1), MoE (4) | 8x22B (Mixtral) | 72B | 671B (V3) |
| Open Weights | Yes, free | Partial | Yes | Yes |
| Multimodal | Vision + Text | Text only | Vision + Text | Vision + Text |
| HuggingFace Downloads | 34M+ | 9M+ | 77M+ | 24M+ |
| MoE Architecture | Yes (Llama 4) | Yes (Mixtral) | No | Yes |
| Best For | General purpose, enterprise | Efficiency | Multilingual | Reasoning |