llama-4-scout

Llama-4-Scout 17B is a specialized Mixture-of-Experts (MoE) model designed to balance high-density knowledge with efficient inference. While the total parameter count sits at 109B, the architecture only activates 17B parameters per token, making it significantly faster and more cost-effective for real-time applications than dense models of a similar scale. For developers, the standout feature is its native multimodality; you can pass image and text inputs through a single unified interface without needing separate vision encoders. With a massive 1.3M token context window, it is purpose-built for long-form document analysis, complex codebase reasoning, and large-scale data extraction. Unlike previous iterations that required heavy fine-tuning for specific tasks, Scout's instruction-tuned weights are optimized for direct integration into RAG pipelines and agentic workflows where low latency and high reasoning accuracy are critical.