Meta首款编程Agent来了！背后模型能力直追Opus 5
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Merged summary
TL;DR — Meta released Muse Code, its first terminal-based AI coding agent, powered by the new Muse Spark 1.2 model, which benchmarks just behind Anthropic's Opus 5 (max) on several coding evaluations. It marks Meta's entry into the coding-agent race against OpenAI and Anthropic.
- Architecture: A simple main agent loop augmented by persistent asynchronous background agents that run for the whole session (rather than being spawned per task), reducing redundant information gathering and latency. Available on macOS and Linux via a single terminal install command.
- Durable runtime: A local event log records model calls, tool runs, approvals, and code edits as the single source of truth, enabling exact replay and safe resumption after crashes or restarts — aimed at long-horizon tasks. Built-in skills include
/plan(approval-gated plans),/grill(stress-testing plans), and/goal(persistent goal pursuit). - Model results: Muse Spark 1.2 trails only Opus 5 (max) on Terminal-Bench 2.1 and on Meta's 440-task Internal Coding Bench; on DeepSWE 1.1 (113 tasks, 91 repos, 5 languages) it trails Opus 5 (max) and GPT-5.6 Terra (max).
- Training and pricing: Gains attributed to co-training with Muse Code (rejection-sampled agent trajectories, Muse Code's toolset, context compression, sub-agents), long-horizon task training, and self-improvement data generated by Muse Spark 1.1. Pricing is $1.25/M input and $4.25/M output, with a much cheaper Contributor tier ($0.10/$0.20); Zuckerberg hinted more open-source news is coming.
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Meta首款编程Agent来了！背后模型能力直追Opus 5
Public signals
N/A
TL;DR — Meta released Muse Code, its first terminal-based AI coding agent, powered by the new Muse Spark 1.2 model, which benchmarks just behind Anthropic's Opus 5 (max) on several coding evaluations. It marks Meta's entry into the coding-agent race against OpenAI and Anthropic.
- Architecture: A simple main agent loop augmented by persistent asynchronous background agents that run for the whole session (rather than being spawned per task), reducing redundant information gathering and latency. Available on macOS and Linux via a single terminal install command.
- Durable runtime: A local event log records model calls, tool runs, approvals, and code edits as the single source of truth, enabling exact replay and safe resumption after crashes or restarts — aimed at long-horizon tasks. Built-in skills include
/plan(approval-gated plans),/grill(stress-testing plans), and/goal(persistent goal pursuit). - Model results: Muse Spark 1.2 trails only Opus 5 (max) on Terminal-Bench 2.1 and on Meta's 440-task Internal Coding Bench; on DeepSWE 1.1 (113 tasks, 91 repos, 5 languages) it trails Opus 5 (max) and GPT-5.6 Terra (max).
- Training and pricing: Gains attributed to co-training with Muse Code (rejection-sampled agent trajectories, Muse Code's toolset, context compression, sub-agents), long-horizon task training, and self-improvement data generated by Muse Spark 1.1. Pricing is $1.25/M input and $4.25/M output, with a much cheaper Contributor tier ($0.10/$0.20); Zuckerberg hinted more open-source news is coming.