Meta AI models

Llama-4-Scout-17B-16E-Instruct is a multimodal, instruction-tuned language model developed by Meta, designed to handle long-context tasks efficiently. It features 17 billion active parameters with a total of 109 billion parameters across 16 experts, utilizing a mixture-of-experts (MoE) architecture. The model supports a context window of up to 10 million tokens, making it suitable for applications requiring extensive context, such as multi-document summarization and large codebase exploration.

Llama 3.1 8B Instruct is Meta's compact, efficiency-focused language model from the Llama 3.1 family. At just 8 billion parameters, this instruction-tuned variant delivers impressive performance with minimal computational requirements, making it ideal for resource-constrained environments and latency-sensitive applications. Despite its smaller size, the model maintains competitive performance in human evaluations against much larger models, particularly excelling in straightforward dialogue, content generation, and common instruction-following tasks. The model features improved inference speed and reduced deployment costs compared to larger variants while maintaining the core capabilities that make Llama models effective. It's particularly well-suited for mobile applications, edge devices, and scaled deployments where efficiency and performance balance are critical considerations.