Meta Ships Muse Code Coding Agent With Co-Trained Muse Spark 1.2 Model

Meta has shipped Muse Code, its first coding agent, in a beta release alongside Muse Spark 1.2, a new version of its flagship model that the company says was co-trained with the agent for tighter integration.

Muse Code is a terminal agent that installs with a single command and takes on complete software engineering tasks, including planning changes, writing code, and validating results. On its Muse Code product page, Meta describes it as “an agent for your most complex coding workstreams,” with multiple agents coordinating on each task: parallel workers doing the implementation while reviewers run in the background. The company says every action the agent takes is transparent and traceable, and that because Muse Code and Muse Spark 1.2 were co-trained, the pairing produces better tool use, fewer retries, and higher-quality output than a generic wrapper around an outside model.

The underlying model, Muse Spark 1.2, is optimized for real coding workflows with what Meta describes as higher first-attempt accuracy and more reliable tool calling. It carries a 1 million-token context window, which Meta says lets long-running tasks run start to finish in a single session. Meta publishes vendor-reported benchmark charts for the model on the page, including results on Terminal-Bench, DeepSWE, its own Meta Internal Coding Bench, and GDPval, though the page presents them as images without a methodology write-up.

Pricing splits into a contributor tier and a standard tier

Access runs through the Meta Model API, which Meta says is now in public preview with expanded global access. The standard Muse Spark 1.2 tier is priced at $1.25 per million input tokens, $0.15 per million cached input tokens, and $4.25 per million output tokens, and prompts on that tier are not used to improve Meta’s products. A contributor tier drops the price to $0.10 per million input tokens, $0.002 per million cached input tokens, and $0.20 per million output tokens, in exchange for letting Meta use the data to improve its models. The model is also available through OpenRouter for developers who want to swap it into existing tools.