What input types does Muse Spark 1.1 accept?

Text, image, video, PDF, and audio. All five go into the same request, so a spec document, a screen recording, and a voice note can arrive together without separate extraction steps.

What makes Muse Spark 1.1 suited to agentic tasks?

Muse Spark 1.1 plans and orchestrates work across tools and services rather than answering in one shot. It runs as a main agent that delegates or as a subagent that reports back, and it supports parallel tool calling for independent steps.

Does Muse Spark 1.1 support structured output?

Yes. Structured output constrains a response to a schema your code parses directly, which keeps downstream stages from scraping prose.

How does built-in search work with Muse Spark 1.1?

Built-in search returns citations alongside its results, so you can check the source behind an answer. Use it for steps that depend on current information.

What is the context window for Muse Spark 1.1?

1.0M tokens, with completions up to 1.0M tokens per request.

Does Muse Spark 1.1 support zero data retention?

Zero Data Retention is not currently available for this model. Zero Data Retention is offered on a per-provider basis. See https://vercel.com/docs/ai-gateway/capabilities/zdr for details.

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.

View API reference

Input and output price: Input $1.25, Output $4.25, Per 1M tokens
24h uptime: Loading AI Gateway uptime

Get API key Read the docs

import { streamText } from 'ai'

const result = streamText({
  model: 'meta/muse-spark-1.1',
  prompt: 'Why is the sky blue?'
})

Read docs

Overview Playground Providers Uptime Status Throughput Latency API Similar About FAQ

Copy link to headingPlayground

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

Copy link to headingProviders

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.

Checking availability for your team

Provider

Context	Max Output	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	ZDR	No Training	Free Tier	Release Date

Getting started

Call Muse Spark 1.1 through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.

Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.

index.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'meta/muse-spark-1.1',
    prompt: 'Why is the sky blue?',
  });

  console.log(result.text);
}

main().catch(console.error);

Top-level parameters

The same Muse Spark 1.1 request in each API format AI Gateway supports.

top-level-params.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'meta/muse-spark-1.1',
    system: 'You are a concise technical assistant.',
    prompt: 'Summarize the tradeoffs between static generation and SSR.',
    maxOutputTokens: 1024,
  });

  console.log(result.text);
}

main().catch(console.error);

Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.

Parameter	Type	Required	Description
`model`	`string`	Yes	Model ID in the form `creator/model`, e.g. `meta/muse-spark-1.1`. AI Gateway routes the request to an available provider.
`maxOutputTokens`	`number`	No	Hard cap on generated tokens. Muse Spark 1.1 supports up to `1,048,576` output tokens. Reasoning tokens count toward this limit.
`reasoning`	`'provider-default' \| 'none' \| 'minimal' \| 'low' \| 'medium' \| 'high' \| 'xhigh'`	No	Provider-agnostic reasoning effort, available in AI SDK 7 or later. Maps to the provider’s native reasoning configuration; reasoning settings under `providerOptions` take precedence when both are set. See the Reasoning section below.
`providerOptions`	`Record<string, JSONValue>`	No	AI Gateway routing options under `gateway`, plus any provider-native options under the provider’s own namespace — see the table below.

Input limits

Input	Formats	Sources	Max count	Max size	Limits
Text	—	—	—	—	Prompt and response share the 1M-token context window
Image	—	URL, base64, Uint8Array	—	—	Sent as image parts in messages; counts as input tokens
PDF	—	URL, base64, Uint8Array	—	—	Sent as file parts in messages; counts as input tokens

Provider options

Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.

Learn more in the AI SDK provider docs.

provider-options.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'meta/muse-spark-1.1',
    prompt: 'Why is the sky blue?',
    providerOptions: {
      gateway: {
        only: ['meta'],
      },
    },
  });

  console.log(result.text);
}

main().catch(console.error);

These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.

Parameter	Type	Required	Description
`providerOptions.gateway.only`	`string[]`	No	Restrict routing to these provider slugs. Requests fail over only within the listed providers.
`providerOptions.gateway.order`	`string[]`	No	Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks.
`providerOptions.gateway.sort`	`'cost' \| 'ttft' \| 'tps'`	No	Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order.
`providerOptions.gateway.zeroDataRetention`	`boolean`	No	Route only to providers with a zero-data-retention policy for this model.

Routing across providers

AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.

Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.

Reasoning

AI Gateway bridges reasoning across every API format. The AI SDK exposes a provider-agnostic top-level reasoning level (none, minimal, low, medium, high, or xhigh); the Chat Completions and Responses formats take the same effort under reasoning.effort; and the Anthropic Messages format uses a native thinking token budget. Whichever you send, the gateway maps it to the target model’s native configuration, converting between effort levels and token budgets as needed. Reasoning-related settings under providerOptions take full precedence over the top-level reasoning value and are never merged. Reasoning tokens typically count toward your output-token usage, though how they’re reported and billed varies by provider.

Learn more in the AI Gateway reasoning guide.

reasoning.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'meta/muse-spark-1.1',
    prompt: 'Explain the Monty Hall problem step by step.',
    reasoning: 'high',
  });

  console.log(result.text);
}

main().catch(console.error);

Image input

Send images alongside text as message parts. Images count as input tokens.

image-input.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'meta/muse-spark-1.1',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Describe this image.' },
          { type: 'image', image: 'https://example.com/photo.jpg' },
        ],
      },
    ],
  });

  console.log(result.text);
}

main().catch(console.error);

PDF input

Attach PDFs as file parts. Their contents count as input tokens.

pdf-input.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'meta/muse-spark-1.1',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Summarize this document.' },
          {
            type: 'file',
            mediaType: 'application/pdf',
            data: 'https://example.com/document.pdf',
          },
        ],
      },
    ],
  });

  console.log(result.text);
}

main().catch(console.error);

Tool calling

Expose tools the model can call. Define each tool’s inputs with a Zod schema.

tool-calling.ts

import { generateText, tool } from 'ai';
import { z } from 'zod';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'meta/muse-spark-1.1',
    prompt: 'What is the weather in San Francisco?',
    tools: {
      getWeather: tool({
        description: 'Get the current weather for a location',
        inputSchema: z.object({ location: z.string() }),
        execute: async ({ location }) => ({ location, temperatureC: 18 }),
      }),
    },
  });

  console.log(result.text);
}

main().catch(console.error);

Copy link to headingMore models by Meta

Model

Context	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	Providers	ZDR	No Training	Free Tier	Release Date

meta/muse-image-1.0

—

$0.01/img

—

08/26/2026

meta/muse-glimmer-30b

131K

0.2 s

102 tps

$0.35/M

$1.50/M

Read$0.04/M

—

08/10/2026

meta/muse-spark-1.2-contributor

2.6 s

164 tps

$0.10/M

$0.20/M

Read$0.002/M

—

08/05/2026

meta/muse-spark-1.2

4.8 s

138 tps

$1.25/M

$4.25/M

Read$0.15/M

—

08/05/2026

meta/muse-spark-1.3-contributor

3.2 s

60 tps

$0.10/M

$0.20/M

Read$0.002/M

—

meta/muse-spark-1.3

3.0 s

47 tps

$1.25/M

$4.25/M

Read$0.15/M

—

Copy link to headingAbout 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.

Copy link to headingWhat 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: Zero Data Retention is offered on a per-provider and model basis. See the documentation for details.
Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

Copy link to headingWhen 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

Copy link to headingConclusion

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.

Copy link to headingFrequently Asked Questions

What input types does Muse Spark 1.1 accept?
Text, image, video, PDF, and audio. All five go into the same request, so a spec document, a screen recording, and a voice note can arrive together without separate extraction steps.
What makes Muse Spark 1.1 suited to agentic tasks?
Muse Spark 1.1 plans and orchestrates work across tools and services rather than answering in one shot. It runs as a main agent that delegates or as a subagent that reports back, and it supports parallel tool calling for independent steps.
Does Muse Spark 1.1 need examples to use a new tool?
No. Muse Spark 1.1 works with new tools, Model Context Protocol (MCP) servers, and custom skills without examples. Describe the interface and Muse Spark 1.1 calls it.
Does Muse Spark 1.1 support structured output?
Yes. Structured output constrains a response to a schema your code parses directly, which keeps downstream stages from scraping prose.
How does built-in search work with Muse Spark 1.1?
Built-in search returns citations alongside its results, so you can check the source behind an answer. Use it for steps that depend on current information.
What is the context window for Muse Spark 1.1?
1.0M tokens, with completions up to 1.0M tokens per request.
How do I use Muse Spark 1.1 on AI Gateway?
Use the identifier meta/muse-spark-1.1 with the AI SDK or any supported interface like Chat Completions, Responses, or Messages. AI Gateway routes across meta and handles failover automatically.
Does Muse Spark 1.1 support zero data retention?
Zero Data Retention is not currently available for this model. Zero Data Retention is offered on a per-provider basis. See https://vercel.com/docs/ai-gateway/capabilities/zdr for details.

Muse Spark 1.1

Copy link to headingPlayground

Copy link to headingProviders

Copy link to headingUptime24 hours

Copy link to headingThroughput24 hours

Copy link to headingLatency24 hours

Getting started

Top-level parameters

Input limits

Provider options

Routing across providers

Reasoning

Image input

PDF input

Tool calling

Copy link to headingMore models by Meta

Copy link to headingAbout Muse Spark 1.1

Copy link to headingWhat To Consider When Choosing a Provider

Copy link to headingWhen to Use Muse Spark 1.1

Best for

Consider alternatives when

Copy link to headingConclusion

Copy link to headingFrequently Asked Questions