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Meta Ships Muse Code Coding Agent and Muse Spark 1.2 Model

Meta released Muse Code, a terminal-based coding agent, alongside Muse Spark 1.2, the model that powers it. The pair was co-trained to perform best together, with Muse Spark 1.2 showing significant gains on coding benchmarks compared to its predecessor. Muse Code runs as a command-line agent that handles multi-file software engineering tasks. It uses persistent background subagents that stay active across a session rather than spawning fresh for each task, which Meta says reduces latency on com

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Meta Ships Muse Code Coding Agent and Muse Spark 1.2 Model

Meta released Muse Code, a terminal-based coding agent, alongside Muse Spark 1.2, the model that powers it. The pair was co-trained to perform best together, with Muse Spark 1.2 showing significant gains on coding benchmarks compared to its predecessor.

Muse Code runs as a command-line agent that handles multi-file software engineering tasks. It uses persistent background subagents that stay active across a session rather than spawning fresh for each task, which Meta says reduces latency on complex multi-step work. Every model call, tool run, approval, and edit is recorded in a local event log, making the runtime replay-exact and restart-safe after crashes.

The agent ships with several bundled commands: /plan produces an approval-gated plan for a task, /grill stress-tests that plan, and /goal drives toward completion of a specified objective.

Muse Spark 1.2 is a coding-focused update to Muse Spark 1.1. Meta scaled up training compute on coding tasks and expanded training environment diversity. The model was trained on long-horizon coding work including whole-repository generation, large end-to-end projects, and auto-research. It also used a self-improvement loop: Muse Spark 1.1 generated challenging coding environments and graded candidate solutions, producing training data that helped Spark 1.2 follow complex instructions more precisely.

On Terminal-Bench 2.1 and DeepSWE 1.1, Muse Spark 1.2 scores competitively with frontier coding models. Meta also published a case study showing the agent iteratively optimizing GPU kernels (KDA and MLA, on NVIDIA Hopper GPUs) over more than 1,000 tool calls spanning up to 24 hours, with substantial speedup over baseline implementations.

Pricing: Standard and Contributor Tiers

Pricing follows a two-tier model. The standard tier costs $1.25 per million input tokens and $4.25 per million output tokens, roughly in line with Gemini 3.6 Flash. Users who opt into letting Meta use their data for product improvement get the contributor tier at $0.10 per million input and $0.20 per million output, a roughly 12x discount that undercuts even GPT-5.6 Luna.

Muse Spark 1.2 standard vs contributor pricing comparison - 12x difference for data sharing

Muse Code is available now for macOS and Linux. Muse Spark 1.2 is accessible through the Muse Code CLI and the Meta Model API.

Sources

Meta AI Research: Introducing Muse Code and Muse Spark 1.2 — https://research.meta.ai/blog/introducing-muse-code-and-muse-spark-1-2

Simon Willison: Introducing Muse Code and Muse Spark 1.2 — https://simonwillison.net/2026/Aug/5/muse-code-and-muse-spark-12/

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