Meta Superintelligence Labs Releases Muse Spark 1.1: A Multimodal Reasoning Model for Agentic Tasks on Meta Model API

Muse Spark 1.1: A Multimodal Reasoning Model for Agentic Tasks on Meta Model API">

Today, Meta Superintelligence Labs released Muse Spark 1.1 . Alongside it, Meta opened a public preview of the Meta Model API. That second part is the structural change. Meta’s models previously reached developers mainly as open weights. Muse Spark 1.1 is closed, hosted, and metered per token. So the question is narrow. Where does it belong in a stack you already run?

Meta describes it as a multimodal reasoning model built for agentic tasks. Reported gains over the first Muse Spark sit in tool use, computer use, coding, and multimodal understanding. The context window is 1,000,000 tokens. Meta Model API docs list 1,048,576.

Because it is a reasoning model, so it thinks before answering. Furthermore, this reasoning effort is adjustable per request. Inputs span text, images, video, and documents; output is text. The API also exposes structured output, parallel tool calling, a Files API, and prompt caching. Adding a web_search tool to a Responses API call returns cited answers.

Access splits two ways. Consumers get it free in ‘Thinking’ mode in the Meta AI app and on meta.ai. Developers pay $1.25 per million input tokens and $4.25 per million output tokens. New accounts get $20 in free credits. Initial launch post describes the public preview as US-only, with no EU access yet.

With the spec in place, the numbers explain the positioning. To illustrate this, Meta published a launch table, and the table splits cleanly.

Meta-reported, with rivals shown in their strongest modes. Muse Spark 1.1 leads the tool-use and tool-augmented reasoning rows. It places third on coding and multimodal. Consequently, this is an orchestration model, not a coding-accuracy leader. Meta also chose the benchmark set and ran the harness.

Beyond the scores, orchestration behavior explains the tool-use results. The model actively manages its million-token context window. It remembers actions, retrieves information from much earlier work, and compacts what it keeps.

Delegation is the second half. As a main agent, it gathers context, plans, and delegates execution across parallel subagents. As a subagent, it adheres to its job, understands available tools, and escalates back when needed. The research team also reports zero-shot generalization to new native tools, MCP servers, and custom skills.

Computer use follows the same logic. The model was trained to write scripts when automation is faster. It clicks when direct interaction is simpler. It generates batches of actions at each step.