Muse models from Meta

Note: Meta models are not a Google product, and their availability in Gemini Enterprise Agent Platform is subject to the terms for "Separate Offerings" in the AI/ML Services section of the Service Specific Terms, and separate terms found in the relevant model card.

Muse models from Meta are available for use as managed APIs on Agent Platform. You can stream your responses to reduce the end-user latency perception. A streamed response uses server-sent events (SSE) to incrementally stream the response.

Managed Meta models

The following models are available from Meta to use in Agent Platform. To find and enable a Meta model, go to its model card in Model Garden.

Muse Wonder from Meta

Muse Wonder from Meta is a high-capability reasoning model built for complex software engineering and long-horizon agentic work. In multi-step coding, autonomous agent, and computer-use workflows, it plans tasks, delegates to sub-agents, calls tools in parallel, and recovers from errors.

Muse Ember from Meta

Muse Ember from Meta is an efficient reasoning model built for high-volume agent tasks, including tool calls, coding subtasks, routing, extraction, and long-document processing. Pair it with Muse Wonder or Muse Spark 1.3 for multi-step planning and reasoning.

Muse Spark 1.3 from Meta

Muse Spark 1.3 from Meta is a reasoning model trained for agentic workflows and competitive coding. It supports built-in tool calling with Model Context Protocol (MCP) support and a 1M-token context window for multi-step tasks.

Use Meta models

Muse models from Meta are served through the stateless OpenAI-compatible Responses API on the global endpoint. Stateful response storage (store: true) isn't supported. Send POST requests to https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/publishers/meta/v1/responses and pass the bare model ID (muse-wonder-1.0, muse-ember-1.0, or muse-spark-1.3) in the model field.

The following curl example sends a prompt request to Muse Wonder on the global endpoint:

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
  -H "Content-Type: application/json" \
  "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/publishers/meta/v1/responses" \
  -d '{
    "model": "muse-wonder-1.0",
    "input": "Write a Python binary search function with type annotations.",
    "max_output_tokens": 4096,
    "stream": false
  }'

Replace PROJECT_ID with your Google Cloud project ID.