LLaMA 4 Scout 17B 16E Instruct

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.

Muse Spark 1.1 is Meta's first paid agentic reasoning model, accepting text, image, video, and PDF input and returning text, with a 1,048,576-token context window and 131,072 max output tokens. It thinks before it answers with reasoning tokens billed as output, actively manages and compacts its own context on long-running workflows, and exposes native tool calling, structured output, parallel tool calling, and built-in web search grounding with inline citations via the Responses API.

Muse Spark 1.2 is Meta's reasoning model for complex agentic tasks, accepting text, images, video, audio, and PDF documents and returning text, with a 1,048,576-token context window. Called via the Responses API, it supports cross-turn reasoning replay, tool-calling loops, built-in web search grounding, and file inputs, with configurable reasoning effort and structured JSON output. Meta reports strong results on multi-file refactors, extended debugging sessions, and whole-repository generation.