In an effort to catch up with OpenAI and Anthropic in one of AI’s fastest-growing markets, Meta has launched its first AI coding agent, Muse Code, alongside an updated coding-focused AI model.
CEO Mark Zuckerberg announced the preview release of Muse Code, describing it as a tool that can handle software engineering tasks from planning code changes to writing software and validating the results. The release also includes Muse Spark 1.2, an updated version of Meta’s foundation model that has been optimized for coding workloads.
The products come from Meta Superintelligence Labs, the AI division led by Chief AI Officer Alexandr Wang. Wang joined Meta as part of Zuckerberg’s effort to jumpstart the company’s AI development after its earlier models struggled to match rivals in several key benchmarks, particularly software development.
Coding assistants are a highly competitive segment of generative AI. Products from OpenAI and Anthropic have demonstrated that AI can automate much of the software development process, allowing developers to generate code and test applications with far less manual work. Meta is now attempting to establish its own position in that market.
According to Wang, Muse Code can be installed with a single command and supports complete software engineering workflows rather than simply generating snippets of code.
The system is built around a coding harness that manages AI models designed specifically for software development projects. Developers can use the service through Meta’s existing developer platform, where the company’s Muse Spark APIs are already available.
Competing on Price
Wang said pricing, rather than outright model capability, is one way Meta hopes to differentiate itself from established competitors. Muse Code will be available through a pay-as-you-go model that follows the pricing introduced with Muse Spark 1.1, which charges $1.25 per million input tokens and $4.25 per million output tokens.
A lower-priced contributor tier allows developers to use the service at far less cost if they agree to share data that helps improve the underlying models.
The company is also beginning to offer zero-data-retention options for enterprise customers that do not want development data stored to train future models.
Muse Spark 1.2 was developed alongside Muse Code to improve coding performance. Although Meta does not position the model as the industry’s most advanced frontier AI system, the company compares its performance with leading models from Anthropic, OpenAI, Google and xAI.
The launch is part of an effort to revamp Meta’s AI business. After its Llama models failed to gain the momentum the company expected among developers, Zuckerberg embarked on an aggressive hiring campaign and reorganized the company’s AI operations under Meta Superintelligence Labs. Since then, the company has introduced multiple members of the Muse model family, including Muse Image, while also previewing its Muse Video model.
Beyond the Muse Code launch, Meta’s AI strategy comes with a substantial price tag. During its latest earnings report, Meta increased the lower end of its projected capital expenditures to between $134 billion and $145 billion, reflecting the cost of building AI models and constructing the data centers needed to support them. The spending has weighed on investor sentiment even as the company continues to prioritize AI as a long-term growth strategy.