Muse Spark 1.1
Muse Spark 1.1 is Meta's multimodal reasoning model for agentic tasks, with a context window of 1.0M tokens and support for text, image, video, PDF, and audio input, available through AI Gateway via Meta. Your use is subject to Meta's Terms & Privacy Policies.
import { streamText } from 'ai'
const result = streamText({ model: 'meta/muse-spark-1.1', prompt: 'Why is the sky blue?'})Playground
Try out Muse Spark 1.1 by Meta. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.
Muse Spark 1.1
Muse Spark 1.1
Providers
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.
Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.
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| 1M | 3.2s | 121tps | $0.10/M | $0.20/M | Read:$0.0/M Write:— | — | +2 | | 08/05/2026 | ||||
| 1M | 3.3s | 218tps | $1.25/M | $4.25/M | Read:$0.15/M Write:— | — | +2 | | 08/05/2026 | ||||
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About Muse Spark 1.1
Muse Spark 1.1, released July 9, 2026, is a multimodal reasoning model from Meta built for agentic tasks. It accepts text, image, video, PDF, and audio input, and supports a context window of 1.0M tokens.
Muse Spark 1.1 plans work and then routes it across the tools and services you expose. It holds either position in an agent hierarchy: a main agent that delegates, or a subagent that takes an assignment and reports back. That flexibility matters when you compose a system from several models and want one of them coordinating the rest.
Muse Spark 1.1 works with new tools, Model Context Protocol (MCP) servers, and custom skills without examples. You describe the interface and Muse Spark 1.1 uses it, so adding a tool doesn't mean writing few-shot demonstrations for every call pattern. Parallel tool calling runs independent calls at once instead of in sequence, which shortens the critical path in a multi-tool step.
Structured output keeps responses machine-readable, so a downstream stage parses a schema instead of scraping prose. Built-in search returns citations with its results, which gives you a source to check when an answer depends on current information.
Mixed input types cut preprocessing. A product spec that arrives as a PDF, a bug report with a screen recording attached, and a voice note from a customer call all go into the same request. None of them need a separate extraction step before Muse Spark 1.1 sees them.
Set the model to meta/muse-spark-1.1 and call it with the AI SDK or any supported interface like Chat Completions, Responses, or Messages. AI Gateway routes across Meta with automatic failover. Muse Spark 1.1 supports a context window of 1.0M tokens and completions up to 1.0M tokens per request, at $1.25 per million input tokens and $4.25 per million output tokens.
What To Consider When Choosing a Provider
- Configuration: Muse Spark 1.1 accepts five input types, and each one consumes context differently, so a request that mixes video and PDFs reaches the window of 1.0M tokens faster than a text prompt of similar apparent length. Budget tokens against representative payloads rather than word counts. Parallel tool calling means several calls can land at once, so confirm your tool handlers tolerate concurrent invocation. Built-in search attaches citations to its results, so decide early how your product surfaces those sources to users.
- Zero Data Retention: AI Gateway does not currently support Zero Data Retention for this model. See the documentation for models that support ZDR.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
When to Use Muse Spark 1.1
Best for
- Agentic Orchestration: Planning work and delegating it across the tools you expose
- Subagent Roles: Handling delegated assignments inside a larger multi-agent system
- Zero-Example Tool Adoption: New tools, MCP servers, and custom skills without few-shot demonstrations
- Mixed-Media Requests: PDFs, video, audio, and images arriving in a single call
- Cited Research Steps: Built-in search that returns sources alongside its results
Consider alternatives when
- Text-Only Workloads: A Llama model covers pure text pipelines at lower cost
- Larger Context Needs: Llama 4 Scout supports a 10M token window for bigger corpora
- Single-Turn Prompts: Orchestration and reasoning add overhead when no tools are involved
- Cost-Sensitive Traffic: A smaller Llama model may meet your quality bar for less
Conclusion
Muse Spark 1.1 is the Meta model to reach for when an agent has to plan, delegate, and work across tools rather than answer a single prompt. The context window of 1.0M tokens, five input types, parallel tool calling, and cited search cover most of what an orchestration layer asks for. Set the model to meta/muse-spark-1.1 and AI Gateway handles provider routing and failover.