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 Wonder and 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.
Muse Wonder is Meta's most capable model, built for complex software engineering and long-horizon agentic work. It makes changes across many files in real repositories, delegates to sub-agents, and works through long tasks with parallel tool calls and a 1M-token context window.
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
Or install it on Windows with PowerShell:
powershell
Start building:
bash
Learn more in the Muse Code overview.
Build with Muse Wonder
Run Muse Wonder for complex coding work and for agents that plan, delegate to sub-agents, and work through long tasks. Muse Wonder and Muse Spark take the same requests, so any example in these docs that uses Muse Spark also runs on Muse Wonder: change the model to muse-wonder-1.0. Muse Wonder takes on longer, more complex tasks; Muse Spark costs less and, on Model API, offers a discounted Contributor mode.
At a glance
| Base URL | https://api.meta.ai/v1 |
| Standard mode model | muse-wonder-1.0 |
| Context window | 1,048,576 tokens |
| Auth | Bearer token (MODEL_API_KEY) |
| Pricing | Pay-as-you-go; see Pricing and rate limits |
Muse Code subscription plans can also include Muse Wonder Full and Contributor mode; see Muse Wonder availability. To decide between Muse Wonder and Muse Spark, see Choose Muse Wonder or Muse Spark.
Muse Wonder is in preview.
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.
At a glance
Everything you need to make your first call.
| Base URL | https://api.meta.ai/v1 |
| Standard mode models | muse-spark-1.3, muse-spark-1.2, muse-spark-1.1 |
| Contributor mode models | muse-spark-1.3-contributor, muse-spark-1.2-contributor |
| Context window | 1,048,576 tokens |
| Auth | Bearer token (MODEL_API_KEY) |
| Pricing | Pay-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 URL | https://api.meta.ai/v1 |
| Model | muse-image-1.0 |
| Endpoints | /v1/images/generations, /v1/images/edits |
| Multi-turn editing | Responses API |
| Output | Image, as base64 or a signed URL |
| Auth | Bearer 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 URL | https://api.meta.ai/v1 |
| Model | muse-voice-transcribe-1.0 |
| Realtime endpoint | wss://api.meta.ai/v1/asr/realtime |
| File endpoint | POST /v1/asr/transcribe |
| Input | Audio |
| Output | Transcript 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 URL | https://api.meta.ai/v1 |
| Model | sam-3.1 |
| Endpoints | POST /v1/responses, POST /v1/files |
| Input | Text prompt, image, video |
| Output | Boxes and masks |
| Auth | Bearer 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 Wonder, 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 pricing and specs listed above.
- Muse Glimmer overview: variants, architecture, license, and launch partners.
- Get the model: download the weights and verify your setup.
- Run inference: pick a runtime and serve it locally.
Explore the docs
Jump to the surface you need.
Capabilities
Muse Wonder, 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.
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.
Agent guides
Go deeper on building agents and integrations.
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.