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Muse Spark 1.2

Muse Spark 1.2 is Meta's coding-focused reasoning model for complex agentic tasks, reading text, images, video, audio, and PDFs across a context window of 1.0M tokens. Your use is subject to Meta's Terms & Privacy Policies.

ReasoningTool UseImplicit CachingFile InputVision (Image)
Use with AI GatewayView docs
import { streamText } from 'ai'


const result = streamText({
  model: 'meta/muse-spark-1.2',
  prompt: 'Why is the sky blue?'
})
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Copy link to headingFrequently Asked Questions

  • What is Muse Spark 1.2 built for?

    Complex agentic tasks with a coding focus. It runs as a main agent that plans and delegates, or as a subagent executing in parallel.

  • What input types does Muse Spark 1.2 accept?

    Text, images, video, audio, and PDF documents. It returns text.

  • What is the context window for Muse Spark 1.2?

    The context window is 1.0M tokens, with up to 1.0M tokens per response.

  • How is Muse Spark 1.2 different from muse-spark-1.2-contributor?

    Same weights and same capabilities. The contributor slug bills far less in exchange for Meta using your inputs and outputs to train its models. Decide on data governance first.

  • How does Muse Spark 1.2 compare to Muse Spark 1.1?

    It is the coding-focused successor, built around harness patterns including context compaction, subagents, whole-repository work, and long-running tasks.

  • Does Muse Spark 1.2 support parallel tool calls?

    Yes, along with structured output and configurable reasoning effort.

  • Does Muse Spark 1.2 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.