Large codebase capabilities

• End-to-end engineering tasks: Plans changes, writes code, runs tests, and validates results across large repos in a single session .
• 1M-token context window — enough to hold large codebases in context simultaneously .
• Isolated git worktrees for each subagent, enabling safe parallel experimentation without conflicts .
• Restart-safe execution — sessions survive crashes, preserving task history and audit logs .
• Full audit logging: every action by every subagent is recorded for transparency .
• Bundled design skills (UX, architecture patterns) rather than requiring external prompts .
• Installs with a single terminal command on macOS and Linux .

Parallel sub-agents and Muse Spark

• Automatic subagent fan-out: When Muse Code receives a task, it spawns multiple persistent sub-agents that work in parallel on different parts of the problem .
• Persistent, async background agents — unlike tools that create agents per step, Muse Code's subagents remain active throughout the session and coordinate continuously .
• Muse Spark 1.2 was co-trained alongside the Muse Code agent harness itself, meaning the model was optimized for the multi-agent, long-horizon workflow from the ground up .

Competitive positioning

Meta is competing primarily on price, crash resilience, and architecture, not raw benchmark superiority:

• Aggressive pricing: Muse Code is pitched as one of the most affordable coding agents on the market . It offers a standard pay-as-you-go tier (~$1.25/M input tokens) and a cheaper "contributor" tier where Meta can use prompts for training .
• Terminal-Bench 2.1 score of 82.9% — placing it between Claude Code and Codex in Meta's reported evaluation .
• Key differentiators: persistent background agents (vs. per-step agents in competitors), isolated worktrees for safe parallel work, crash-safe sessions, and a model co-trained with the agent rather than adapted afterward .
• Meta is not claiming outright benchmark leadership — instead, it is positioning Muse Code as a lower-cost, more resilient alternative for large, long-running engineering tasks .

Large codebase capabilities

• End-to-end engineering tasks: Plans changes, writes code, runs tests, and validates results across large repos in a single session .
• 1M-token context window — enough to hold large codebases in context simultaneously .
• Isolated git worktrees for each subagent, enabling safe parallel experimentation without conflicts .
• Restart-safe execution — sessions survive crashes, preserving task history and audit logs .
• Full audit logging: every action by every subagent is recorded for transparency .
• Bundled design skills (UX, architecture patterns) rather than requiring external prompts .
• Installs with a single terminal command on macOS and Linux .

Parallel sub-agents and Muse Spark

• Automatic subagent fan-out: When Muse Code receives a task, it spawns multiple persistent sub-agents that work in parallel on different parts of the problem .
• Persistent, async background agents — unlike tools that create agents per step, Muse Code's subagents remain active throughout the session and coordinate continuously .
• Muse Spark 1.2 was co-trained alongside the Muse Code agent harness itself, meaning the model was optimized for the multi-agent, long-horizon workflow from the ground up .

Competitive positioning

Meta is competing primarily on price, crash resilience, and architecture, not raw benchmark superiority:

• Aggressive pricing: Muse Code is pitched as one of the most affordable coding agents on the market . It offers a standard pay-as-you-go tier (~$1.25/M input tokens) and a cheaper "contributor" tier where Meta can use prompts for training .
• Terminal-Bench 2.1 score of 82.9% — placing it between Claude Code and Codex in Meta's reported evaluation .
• Key differentiators: persistent background agents (vs. per-step agents in competitors), isolated worktrees for safe parallel work, crash-safe sessions, and a model co-trained with the agent rather than adapted afterward .
• Meta is not claiming outright benchmark leadership — instead, it is positioning Muse Code as a lower-cost, more resilient alternative for large, long-running engineering tasks .