models

Meta: Muse Spark Open

Markdown · JSON

Meta: Muse Spark Open

meta/muse-spark-open
MetaTextInput $0.0025 / 1KOutput $0.0085 / 1K

Meta’s open-weight model for programming, multistep reasoning and working with a large text context.

A Meta model for development and long-running tasks

Muse Spark Open is a version of Muse Spark with published weights, focused on programming and agentic workflows. Meta highlights tasks that require maintaining a goal across several steps: understanding a project, planning a change, preparing code and aligning the result with the original requirements. In Ailoot API, the model accepts text messages and returns text.

According to Meta, Muse Spark Open has 816 billion total parameters and 42 billion active parameters per token. These figures describe the model’s architecture: its overall size and the portion used to process each token. Published weights do not mean that users need to download them or arrange local execution to work with the model through Ailoot API.

Working with code and requirements

A text request can contain the task definition, source-code fragments, environment details and expected behavior. When investigating an error, include the actual message, a minimal example and constraints on changes. The model can prepare an explanation, a proposed fix and verification suggestions; running the code and checking the result remain separate workflow actions.

When designing a change, specify the order of the output: first identify the affected parts, then describe the solution and only then present the code. For a review, provide the requirements alongside the changed section so that the discussion is grounded in system behavior as well as coding style. These approaches reflect the model’s stated focus on programming and multistep problem-solving.

Context in Ailoot API

Muse Spark Open has a stated context of 1,048,576 tokens. It can hold substantial text material and task history. The large window is useful for considering requirements, documentation and several related modules together; preserve file names and fragment boundaries explicitly so the response can refer to them.

The underlying Muse Spark Open model is multimodal, but the current Ailoot API methods for this listing work with text. This description does not claim analysis of attached images, audio or video, or control of software. Meta’s agent demonstrations show the model in a separate environment with tools; a text response by itself does not establish that a change has been made in an application or repository.

Available through Ailoot API

Ultra Anthropic MessagesPOST /v1/messages
Input types
text
Output types
text
Context
Not specified in the catalogue
Max. output
Not specified in the catalogue
Input
$0.0025 / 1K
Output
$0.0085 / 1K
Cache read
$0.0003 / 1K
Ultra OpenAI ResponsesPOST /v1/responses
Input types
text
Output types
text
Context
Not specified in the catalogue
Max. output
Not specified in the catalogue
Input
$0.0025 / 1K
Output
$0.0085 / 1K
Cache read
$0.0003 / 1K
Ultra OpenAI ChatPOST /v1/chat/completions
Input types
text
Output types
text
Context
Not specified in the catalogue
Max. output
Not specified in the catalogue
Input
$0.0025 / 1K
Output
$0.0085 / 1K
Cache read
$0.0003 / 1K
Ultra Gemini generateContentPOST /v1beta/models/meta/muse-spark-open:generateContent
Input types
text
Output types
text
Context
Not specified in the catalogue
Max. output
Not specified in the catalogue
Input
$0.0025 / 1K
Output
$0.0085 / 1K
Cache read
$0.0003 / 1K
meta/muse-spark-open · JSON
{"about":"### A Meta model for development and long-running tasks\n\nMuse Spark Open is a version of Muse Spark with published weights, focused on programming and agentic workflows. Meta highlights tasks that require maintaining a goal across several steps: understanding a project, planning a change, preparing code and aligning the result with the original requirements. In Ailoot API, the model accepts text messages and returns text.\n\nAccording to Meta, Muse Spark Open has 816 billion total parameters and 42 billion active parameters per token. These figures describe the model’s architecture: its overall size and the portion used to process each token. Published weights do not mean that users need to download them or arrange local execution to work with the model through Ailoot API.\n\n### Working with code and requirements\n\nA text request can contain the task definition, source-code fragments, environment details and expected behavior. When investigating an error, include the actual message, a minimal example and constraints on changes. The model can prepare an explanation, a proposed fix and verification suggestions; running the code and checking the result remain separate workflow actions.\n\nWhen designing a change, specify the order of the output: first identify the affected parts, then describe the solution and only then present the code. For a review, provide the requirements alongside the changed section so that the discussion is grounded in system behavior as well as coding style. These approaches reflect the model’s stated focus on programming and multistep problem-solving.\n\n### Context in Ailoot API\n\nMuse Spark Open has a stated context of 1,048,576 tokens. It can hold substantial text material and task history. The large window is useful for considering requirements, documentation and several related modules together; preserve file names and fragment boundaries explicitly so the response can refer to them.\n\nThe underlying Muse Spark Open model is multimodal, but the current Ailoot API methods for this listing work with text. This description does not claim analysis of attached images, audio or video, or control of software. 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Questions

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First identify the required input and output: text, image, audio or video. Then compare compatible API methods, the exact model ID or current alias target, price and billing unit. GET /v1/models is free and lists models available to your key.

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