Muse Ember
Muse Ember is an efficient multimodal AI model developed by Meta for autonomous agents, software engineering, complex reasoning, and high-volume task automation. The model specializes in repetitive agentic operations such as tool calling, code analysis, information extraction, workflow routing, and document processing. It supports text, images, audio, video, and PDF inputs with a one-million-token context window and a maximum output length of 512,000 tokens. Muse Ember can execute multistep business workflows, analyze large software repositories, perform coding tasks, and interpret visual information from documents and screenshots. Its cost-efficient architecture makes it suitable for multi-agent systems in which larger models handle planning while Muse Ember executes frequent subtasks. The model is available through Meta Model API and Muse Code, with API pricing starting at $0.10 per million input tokens and $0.50 per million output tokens.
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Muse Spark 1.2
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Muse Spark 1.2 is Meta’s coding-focused model update designed to power Muse Code and improve software engineering workflows. The model is built for code generation, complex debugging, codebase understanding, long-horizon development tasks, and end-to-end developer workflows. Muse Spark 1.2 was co-trained with Muse Code to improve performance inside the terminal coding agent environment. It supports planning, goal conditioning, context compaction, subagent coordination, and iterative coding workflows across large repositories. The model was trained with expanded coding compute, diverse development environments, self-improvement loops, and long-running engineering tasks. Built for AI developers and software teams, Muse Spark 1.2 helps agents plan, write, validate, debug, and optimize code with greater autonomy.
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Muse Code
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Muse Code is Meta’s terminal coding agent, powered by Muse Spark 1.2, for handling complex software engineering tasks across large repositories. The agent can plan changes, write code, validate results, and coordinate multiple persistent subagents during development sessions. Muse Code uses async background agents that stay active throughout a session to reduce repeated information gathering and help complete multi-step tasks with less steering. Its runtime uses a local event log that records model calls, tool runs, approvals, and edits so sessions can be replayed and resumed after failures. Muse Code includes bundled skills such as /plan for approval-gated planning, /grill for stress-testing plans, and /goal for working toward completion. Built for AI developers and software teams, Muse Code helps automate coding workflows, long-running engineering tasks, debugging, and repository-level development.
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Muse
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Meta Muse is a personal AI agent designed to complete tasks across different parts of a user’s daily life through conversation and connected apps. It runs on a persistent secure virtual machine with its own browser, allowing it to navigate websites, book appointments, fill out forms, handle customer service tasks, and complete other web-based workflows. Users can interact with Muse through its mobile app or WhatsApp and can connect services such as email, calendars, and Instagram. The agent can create plans around user goals, track progress, make suggestions, and continue working on assigned tasks over time. Sensitive actions such as sending emails or making purchases can require user approval, and Muse provides an audit trail showing completed and planned actions. Meta positions Muse as a privacy-focused personal agent with secure credential storage, one-time payment card support, and controls designed to keep users in charge of what the agent does.
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