Muse Ember 1.0

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.

Meta Llama 3.1 8B Instruct, served in an FP8 Turbo configuration.

Llama 3.3 70B Turbo is an FP8-quantized version of Llama 3.3 70B for significantly faster inference with a minor accuracy trade-off.

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 Guard 4 is a natively multimodal 12B safety classifier for classifying prompts and responses as safe or unsafe.

Muse Glimmer is a 30B multimodal agentic model distilled from Muse Spark, combining reasoning, tool use and failure recovery in a model that runs locally on consumer hardware.

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.

Muse Spark Open is Meta's open-weight 816B-parameter Mixture-of-Experts model (42B active) tuned for agentic coding. It accepts text, image, video and audio input with a 1M-token context window and supports reasoning and tool calling.