Muse Wonder Full vs. Muse Code

Muse Wonder Full is a large-scale artificial intelligence model developed by Meta for advanced agentic reasoning, professional knowledge work, and complex software engineering tasks. Introduced in October 2026, it represents the larger variant of the Muse Wonder family and builds on Meta's research into scaling pretrained models and reinforcement learning. The model is designed to manage complex objectives involving multiple steps, changing requirements, external information, and sustained problem-solving. Its Active Memory capability maintains important contextual information across conversations and retrieves relevant details when they become useful for subsequent tasks. Active Focus enables the model to pursue long-running assignments, explore alternative strategies, recover from obstacles, and adapt to new information. The system can request clarification when instructions are incomplete and seek user confirmation before performing important actions. Muse Wonder Full supports professional applications involving legal documents, financial analysis, health-related information, and business process automation. Its coding capabilities include generating software, analyzing existing repositories, tracking implementation requirements, modifying source code, and performing iterative testing and debugging. Meta trained the model on diverse long-running software engineering tasks to support development projects requiring multiple rounds of implementation and revision. Evaluations include MemoryBench for contextual memory, FocusBench for sustained task execution, SWE-Marathon and DeepSWE for software engineering, and additional assessments covering professional agents and unfamiliar problem-solving tasks. Muse Wonder Full is available to Muse Code subscribers, with Meta also reporting safety evaluations addressing personal information handling, adversarial instructions, unauthorized code execution, and misuse prevention.

Muse Code is Meta’s terminal-based AI coding agent built to take on complex software engineering tasks across large repositories. The agent is powered by Muse Spark 1.2 and is designed to plan code changes, write implementation code, validate results, and support end-to-end developer workflows. Muse Code can coordinate multiple persistent subagents for each task, helping solve difficult problems faster and with less manual intervention. Its architecture uses a simple agent loop enhanced by async background agents that remain active for the full session instead of being spawned only for individual steps. These background agents reduce repeated information gathering, carry out next actions, and communicate back to the main agent when useful. Muse Code’s runtime uses a local event log where model calls, tool runs, approvals, and edits are continuously appended. This event log serves as a single source of truth, making the runtime replay-exact and restart-safe if a crash or interruption occurs. The design allows Muse Code to handle long-running development work without losing progress or context. Muse Code includes bundled skills such as /plan for approval-gated task planning, /grill for stress-testing plans, and /goal for working toward successful completion of a defined objective. Example workflows include interpreting a video input, understanding the requested output, and producing a rich software experience such as a vacation home marketing and booking page. By combining terminal execution, autonomous planning, persistent background agents, replay-safe runtime design, bundled skills, and Muse Spark 1.2 model support, Muse Code helps developers complete ambitious coding tasks with greater reliability.