--- meta: title: Models description: Available models on Meta Model API and guidance on choosing the right model for your workload. keywords: models, Muse Spark, model lineup, capabilities, context window cms: alias: /model-api/docs/getting-started/models target: aidmc --- # Models Meta Model API runs Meta's latest models for agentic and coding work — multi-step tool loops, software engineering assistants, and long-context reasoning. This page shows what's available today and what each model can do. ## Available models {#available-models} | Model ID | Input modalities | Output modalities | Context window | | :---- | :---- | :---- | :---- | | `muse-spark-1.1` | Text, image, video, PDF | Text | 1,048,576 tokens | ### Muse Spark 1.1 {#muse-spark} **Model ID:** `muse-spark-1.1` Muse Spark 1.1 is multimodal: it takes text, image, video, and PDF as input and generates text. Use it for [chat completion](/docs/features/chat-completion), [image understanding](/docs/features/image-understanding), [video understanding](/docs/features/video-understanding), [tool calling](/docs/features/tool-calling), [structured output](/docs/features/structured-output), and [search grounding](/docs/features/search-grounding). | Context window | Input modalities | Output modalities | | :---- | :---- | :---- | | 1,048,576 tokens | Text, image, video, PDF | Text | **Best for:** Agentic [tool calling](/docs/features/tool-calling) and multi-step workflows, coding and technical assistants, [structured output](/docs/features/structured-output), [image understanding](/docs/features/image-understanding) and [video understanding](/docs/features/video-understanding), document processing, real-time chat, and high-frequency sub-agent loops. ## List models via the API {#list-models} Query the catalog programmatically when you need to check what's enabled for your team: ```python title="Python (OpenAI SDK)" import os from openai import OpenAI client = OpenAI( base_url="https://api.meta.ai/v1", api_key=os.environ["MODEL_API_KEY"], ) response = client.models.list() print(response.model_dump_json(indent=2)) ``` ```python title="Python (requests)" import json import os import requests response = requests.get( "https://api.meta.ai/v1/models", headers={"Authorization": f"Bearer {os.environ['MODEL_API_KEY']}"}, ) response.raise_for_status() print(json.dumps(response.json(), indent=2)) ``` ```shell title="curl" curl -X GET "https://api.meta.ai/v1/models" \ -H "Authorization: Bearer $MODEL_API_KEY" ``` The response returns models sorted newest-first by creation time, with the model ID as a stable tiebreaker. Each object includes a `created` field — the Unix timestamp in seconds for when the model was added to the registry. `GET /v1/models/{model}` returns the same `created` value for a single model. ## Next steps - [Get started](/docs/getting-started/overview#first-call): make your first call with Muse Spark - [Pricing and rate limits](/docs/getting-started/pricing-rate-limits): see rates and retry guidance - [Chat completion](/docs/features/chat-completion): start generating with the core conversational endpoint