Key Features
• Multimodal Intelligence
-1. Natively processes text and images, with video support planned -2. Uses early fusion for seamless text-image understanding, outperforming bolted-on approaches -3. Supports 12 languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, Vietnamese
• Mixture of Experts (MoE) Architecture
-1. Activates only 17B of 109B (Scout) or 400B (Maverick) parameters per task for efficiency -2. Llama 4 Scout fits on a single H100 GPU with Int4 quantization; Maverick fits on a single H100 host -3. Behemoth (2T total parameters, 288B active) outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM benchmarks
• Massive Context Window
-1. Scout: 10M token context, enabling multi-document summarization and code analysis -2. Maverick: 1M token context, ideal for long-form tasks -3. Processes up to 7,500 pages (Scout) or 750 pages (Maverick) of text
• Performance Excellence
-1. Maverick matches GPT-4o and Gemini 2.0 on coding, reasoning, and image benchmarks -2. Scout outperforms Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across benchmarks -3. Behemoth scores higher than GPT-4o on LMArena AI benchmark (experimental version)
• Cost and Accessibility
-1. Open-source under Llama 4 Community License, free for research and commercial use (up to 700M monthly users) -2. Available on Hugging Face, AWS Bedrock, Azure AI Foundry, and Google Cloud Vertex AI -3. No public pricing for API; costs depend on platform (e.g., AWS, Azure)
• Developer-Friendly Features
-1. Supports function calling, instruction-tuned models for chat, and vision tasks like image reasoning -2. Quantized checkpoints (BF16, FP8, Int4) for flexible deployment -3. Enables synthetic data generation and model distillation
• Practical Applications
-1. Powers chatbots, content summarization, code generation, and research platforms -2. Ideal for enterprises, developers, and academics building multimodal AI solutions
