Build with Meta Model API and Muse Code

Your next build starts with Muse. Meta Model API is the inference platform for the latest models from Meta including Muse Spark, and Muse Code is a coding agent built for Muse Spark. Call the API to build your own app or agent, or run Muse Code for a ready-made agent in your terminal — same models, same auth, same billing.

Muse Spark is served through Model API, with agent-ready primitives and no extra setup: parallel tool calls, streamed tool-call arguments, reasoning that carries across turns, and a 1M-token context window.

Muse Image is served through Model API too. It generates and edits images from a text prompt and refines them across turns.

Muse Voice Transcribe is a speech-to-text model on Model API. It transcribes streaming and non-streaming audio, with speaker attribution and turn detection in the model.

Segment Anything Model 3.1 (SAM 3.1), an open-weight model from Meta, is now available on Model API. It finds and outlines objects in images and video from a short text prompt, returning boxes and masks.

Muse Glimmer takes a different path: you download the open weights and run it on your own hardware instead of calling it through Model API. It delivers strong performance for its size class under a permissive Apache 2.0 license.

Muse Code is a coding agent from Meta for the terminal and CI, built for Muse Spark and powered by Model API. Install it, sign in, and run it in a project. It plans, edits, and runs commands to do tasks, with approvals and an OS sandbox on from the first run.

Build with Muse Code

Install the CLI on macOS or Linux:

bash
curl -fsSL https://dev.meta.ai/install.sh | sh

Or install it on Windows with PowerShell:

powershell
irm https://dev.meta.ai/install.ps1 | iex

Start building:

bash
muse   # start Muse Code; on first run, choose a browser sign-in or paste an API key
Muse Code or the API

Muse Code and the API are two ways to use the same model. Run Muse Code for a ready-made agent at the command line or in CI; call the API directly when you build your own agent or app.

Learn more in the Muse Code overview.

Build with Muse Spark

Call Model API directly, run Muse Code, or connect the coding agent you already use. All three drive the same model over the same auth and billing.

At a glance

Everything you need to make your first call.

Base URLhttps://api.meta.ai/v1
Standard tier modelsmuse-spark-1.3, muse-spark-1.2, muse-spark-1.1
Contributor tier modelsmuse-spark-1.3-contributor, muse-spark-1.2-contributor
Context window1,048,576 tokens
AuthBearer token (MODEL_API_KEY)
PricingPay-as-you-go; see Pricing and rate limits

Call the API directly

Model API is drop-in compatible with the OpenAI SDK, the Anthropic SDK, and OpenAI-compatible agent CLIs. Set your client's base URL, add your key, and keep the rest of your code.

Walk through your first call in the quickstart, or compare request formats in Choosing an API.

Connect your coding agent

Already have a coding agent? Connect it to Model API and keep working. Muse Spark drives the same agentic loop of file edits, shell commands, and tool calls over an OpenAI- or Anthropic-compatible surface.

Build with Muse Image

Muse Image generates and edits images through Model API. Send a text prompt and get an image back, refine it across turns, or edit an existing image. It uses the same base URL and auth as Muse Spark.

At a glance

Everything you need to make your first image request.

Base URLhttps://api.meta.ai/v1
Modelmuse-image-1.0
Endpoints/v1/images/generations, /v1/images/edits
Multi-turn editingResponses API
OutputImage, as base64 or a signed URL
AuthBearer token (MODEL_API_KEY)

Generate your first image and learn multi-turn editing in the Image generation guide.

Build with Muse Voice Transcribe

Muse Voice Transcribe brings speech-to-text to Model API. Use it to transcribe live audio streams or supported audio files with one model that can detect speech turns, attribute speakers, and bias recognition toward your vocabulary.

At a glance

Base URLhttps://api.meta.ai/v1
Modelmuse-voice-transcribe-1.0
Realtime endpointwss://api.meta.ai/v1/asr/realtime
File endpointPOST /v1/asr/transcribe
InputAudio
OutputTranscript text with turn-level timing and optional speaker labels
Pricing$0.18 per hour; see Pricing and rate limits

Muse Voice Transcribe is built for developers adding real-time speech to voice agents, meeting and call intelligence, live transcription, dictation, captioning, and high-volume transcription products.

It supports streaming speaker diarization, native endpointing and voice activity detection, contextual and keyword biasing, and 25 evaluated languages with code-switching. It returns turn-level timestamps, but not word-level timestamps. It is speech-to-text only; it does not synthesize speech or provide a speech-to-speech conversation API.

Start with the Muse Voice Transcribe guide.

Build with Segment Anything Model

SAM 3.1 segments images and video through Model API. Name an object in a short text prompt and get back a box and a pixel-accurate mask for every match, and in video follow each object across frames. It uses the same base URL and auth as Muse Spark.

At a glance

Everything you need to make your first segmentation request.

Base URLhttps://api.meta.ai/v1
Modelsam-3.1
EndpointsPOST /v1/responses, POST /v1/files
InputText prompt, image, video
OutputBoxes and masks
AuthBearer token (MODEL_API_KEY)

Segment your first image in the Media segmentation guide.

Build locally with Muse Glimmer

Muse Glimmer is an open-weight multimodal model, distilled from Muse Spark and built to run on your own hardware. Unlike the models you call over Model API - Muse Spark, Muse Image, Muse Voice Transcribe, and SAM - you run Muse Glimmer yourself: you download the weights and serve it through a runtime such as vLLM, SGLang, llama.cpp, or ExecuTorch.

Because it's self-hosted, Muse Glimmer has its own documentation section covering how to get the model, prompt it, deploy it, and customize it - rather than the API tiers and specs listed above.

Explore the docs

Jump to the surface you need.

Quickstart Set your key, call the API, and read your first response.
Models Muse Spark, Muse Image, Muse Voice Transcribe, SAM, and Muse Glimmer specs, plus pricing tiers.
SDKs and libraries Official SDKs and the community libraries that work with Model API.
Cookbook Copy-paste recipes for primitives, agent loops, and use cases.
Pricing and rate limits Per-token, per-image, and per-hour audio pricing, plus rate limits.
API reference Full request and response schemas for every endpoint.

Capabilities

Muse Spark, Muse Image, Muse Voice Transcribe, and SAM ship production capabilities through Model API. Each page explains the concept, shows code, and covers constraints. For the Muse Glimmer equivalents, see the prompting guide.

Speech to text Transcribe streaming audio and supported files with Muse Voice Transcribe.
Tool calling Connect the model to your APIs with parallel, streamed tool calls.
Tool search Discover and load deferred tools on demand to save tokens.
Search grounding Real-time answers with inline citations, no retrieval stack to build.
Image understanding Read photos, charts, documents, and screenshots.
Image generation Generate and edit images with Muse Image, and refine them turn by turn.
Video understanding Summarize clips, ask questions about footage, and transcribe speech.
Media segmentation Find and outline objects in images and video with SAM.
File handling Upload once and reference files by ID across requests.
Reasoning Dial reasoning effort up or down per request.
Structured output Return valid JSON that matches your schema every time.
Prompt caching Cache repeated prefixes to cut latency and cost.
Token counting Count input tokens before you send to estimate cost and fit context.

Protocols

Pick the request format your code already speaks: same models, same auth, same cost per token. These are Model API surfaces; a local Muse Glimmer server exposes its own OpenAI-compatible endpoint through vLLM or llama.cpp. See Choosing an API to compare them.

Responses API Agentic, multi-step workloads with reasoning replay and server-managed state.
Chat Completions API The OpenAI-compatible messages-array endpoint.
Messages API The Anthropic Messages-compatible endpoint.

Agent guides

Go deeper on building agents and integrations.

Coding agents Wire Muse Spark into OpenCode, Codex, and other coding harnesses.
Agent frameworks Build with LangChain, LlamaIndex, and the Vercel AI SDK.
Computer use Drive a desktop from screenshots with a computer-use agent.

Get unblocked

Browse the Help Center for accounts, API keys, billing, and rate-limit questions. For anything it doesn't cover, contact support through the Help Center.