Muse Spark 1.1Meta
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Related Products
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About
Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets. We are happy to receive feedback and contributions. Deequ depends on Java 8. Deequ version 2.x only runs with Spark 3.1, and vice versa. If you rely on a previous Spark version, please use a Deequ 1.x version (legacy version is maintained in legacy-spark-3.0 branch). We provide legacy releases compatible with Apache Spark versions 2.2.x to 3.0.x. The Spark 2.2.x and 2.3.x releases depend on Scala 2.11 and the Spark 2.4.x, 3.0.x, and 3.1.x releases depend on Scala 2.12. Deequ's purpose is to "unit-test" data to find errors early, before the data gets fed to consuming systems or machine learning algorithms. In the following, we will walk you through a toy example to showcase the most basic usage of our library.
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About
Muse Spark 1.1 is a multimodal reasoning model from Meta Superintelligence Labs built for agentic tasks, coding, computer use, tool use, and multimodal understanding. The model improves on the original Muse Spark with stronger performance in planning, orchestration, long-context work, coding workflows, and external app interactions. Muse Spark 1.1 can manage a 1 million token context window, remember earlier actions, retrieve important information, compact context, and delegate tasks across parallel subagents. It is designed to operate across tools, MCP servers, custom skills, browsers, native apps, scripts, images, video, PDFs, and audio-based workflows. Developers can access Muse Spark 1.1 through the new Meta Model API public preview, while users can try it in Thinking mode in the Meta AI app and on meta.ai.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Anyone looking for an Unit Testing solution that measures data quality in large datasets
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Audience
Muse Spark 1.1 is best suited for developers, AI engineers, enterprises, agent builders, coding tool teams, research teams, and productivity-focused users that need a multimodal reasoning model for agentic workflows, coding, computer use, long-context tasks, tool orchestration, and advanced automation
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Free Trial
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Pricing
$1.25 per 1M tokens (input)
$1.25 per million tokens in input, and $4.25 per million tokens of output
Free Version
Free Trial
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Reviews/
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Reviews/
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Pros & Cons from Real UsersPros
Cons
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationDeequ
github.com/awslabs/deequ
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Company InformationMeta
Founded: 2004
United States
meta.ai
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Categories |
Categories |
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Integrations
Apache Spark
C++
Claude Agent SDK
Claude Code
Facebook
Facebook Messenger
Gray Swan
HTML
Java
Kotlin
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Integrations
Apache Spark
C++
Claude Agent SDK
Claude Code
Facebook
Facebook Messenger
Gray Swan
HTML
Java
Kotlin
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