Meta’s Muse Spark 1.1 API pricing squeezes OpenAI and Anthropic as the AI price war heats up

Meta is entering the AI API business with Muse Spark 1.1 at prices that undercut even the dirt-cheap Grok 4.5, released just yesterday. At $4.25 per million output tokens, Meta charges a fraction of what Anthropic or OpenAI ask.

Meta calls it a “significant upgrade” over the original Muse Spark, which shipped in early April 2026. The model is available in “Thinking” mode in the Meta AI app and on meta.ai. Like its predecessor, Muse Spark 1.1 ships without open weights, suggesting Meta has moved on from the open-source Llama strategy that once made it a hero in the AI community. Alongside the model, Meta is launching a public preview of the new Meta Model API, giving developers direct access for the first time. The move puts Meta squarely in a market previously held by OpenAI, Anthropic, Google, and several Chinese providers. The new image model, Muse Image, isn’t available through the API yet.Ad Multi-agent orchestration aims to speed up complex projects Meta says Muse Spark 1.1 is trained to orchestrate multi-agent systems. As the main agent, the model gathers context, builds a plan, and delegates execution to parallel subagents. As a subagent, it stays on task and knows when to escalate back. The model generalizes to new native tools, MCP servers, and custom skills without specific training. It actively manages its one-million-token context window, remembering actions, retrieving and compressing information from earlier work without losing critical steps, according to Meta.AdDEC_D_Incontent-1 Meta says coding performance has also improved significantly on real-world tasks involving large codebases. The model can now diagnose complex bugs, add new features to enterprise systems, and handle large-scale code migrations. In the independent VALS-AI benchmark, Muse Spark 1.1 ranks fourth overall while being particularly fast and cost-effective. On the “Vibe Code Bench” coding benchmark alone, it jumped 36 places over its predecessor.Ad Meta also highlights multimodal strengths in perception, reasoning, and tool use. The model can interact with real-world environments and produce outputs based on actual observations, especially in computer-use workflows spanning multiple applications. Instead of clicking through each desktop step individually, the model decides when automation makes sense.