Muse Spark 1.1 Reviews & Ratings

Muse Spark 1.1

5.0 out of 5 stars

Ratings and Reviews 1 Rating

Total 5 out of 5 stars
ease 5 out of 5 stars
features 4 out of 5 stars
design 5 out of 5 stars
support 5 out of 5 stars

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features 0 out of 5 stars
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What is Muse Spark 1.1?

Muse Spark 1.1 is an advanced multimodal reasoning model from Meta Superintelligence Labs built for agentic work, coding, computer use, tool calling, and multimodal understanding. It is a major upgrade from Muse Spark and is designed to push the performance-efficiency frontier for AI systems that need to plan, reason, act, and coordinate across complex workflows. The model can operate across external apps, native tools, MCP servers, custom skills, browsers, scripts, images, videos, PDFs, audio, and developer environments. Muse Spark 1.1 is especially strong in agentic orchestration, where it can gather context, make plans, delegate work to parallel subagents, and manage execution across multiple steps. As a subagent, it can follow a defined role, use available tools appropriately, and escalate back to a main agent when needed. Its 1 million token context window helps it remember past actions, retrieve information from earlier in a project, and compact long sessions while keeping important details available for later work. For computer-use tasks, Muse Spark 1.1 can navigate unfamiliar interfaces, adapt to changing requirements, and choose whether to click through an interface or write scripts when automation is faster. In software engineering, the model can diagnose complex bugs, implement new features, perform large code migrations, build web applications, inspect screenshots, trace issues to code, and validate fixes. Its multimodal capabilities allow it to inspect visual and audio information, generate detailed image and video captions, create visual-to-code artifacts, and combine perception with action in practical workflows. Developers can access Muse Spark 1.1 through Meta’s new Model API public preview, and everyday users can try it in Thinking mode in the Meta AI app.

What is Llama 4 Scout?

Llama 4 Scout represents a leap forward in multimodal AI, featuring 17 billion active parameters and a groundbreaking 10 million token context length. With its ability to integrate both text and image data, Llama 4 Scout excels at tasks like multi-document summarization, complex reasoning, and image grounding. It delivers superior performance across various benchmarks and is particularly effective in applications requiring both language and visual comprehension. Scout's efficiency and advanced capabilities make it an ideal solution for developers and businesses looking for a versatile and powerful model to enhance their AI-driven projects.

Media

Media

Integrations Supported

C
C#
C++
CSS
Go
HTML
Java
JavaScript
Kotlin
Meta AI
Meta Model API
PHP
Python
R
Ruby
Rust
SQL
Scala
TypeScript

Integrations Supported

C
C#
C++
CSS
Go
HTML
Java
JavaScript
Kotlin
Meta AI
Meta Model API
PHP
Python
R
Ruby
Rust
SQL
Scala
TypeScript
Okara

API Availability

Has API

API Availability

Pricing Information

$1.25 per 1M tokens (input)
$1.25 per million tokens in input, and $4.25 per million tokens of output
Free Trial Offered?

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

meta.ai

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

ai.meta.com

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

Categories and Features

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

Small Language Models

Not specified

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