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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 MuSES?

MuSES sets a new standard for precision in electro-optic and infrared visualizations by initiating a meticulous procedure that begins with detecting heat sources such as engines, exhaust systems, bearings, and electronic components, followed by an exhaustive in-band diffuse radiosity solution. Once your sensor is positioned at the desired distance, you can produce multi-bounce radiance values that have been spectrally summed, employing DeltaT-RSS contrast metrics for nuanced examination. If a sensor response curve is accessible, you can import it to reveal insights that may have previously gone unnoticed, enhancing your understanding of the thermal landscape. With MuSES, the exploration of reality is taken to an extraordinary level of detail. The software is equipped to fully consider the physics behind heat sources and the effects of environmental factors, allowing for effective management of thermal signature contrasts and evaluation of control kits crucial for low observable design in various geographical settings. You can perform thorough assessments of heat shields, cooling techniques, and camouflage surface treatments for in-band radiance while also factoring in the atmospheric attenuation present along the sensor’s line-of-sight. By focusing on engineering priorities with MuSES at the beginning of your project development cycle, you enable your team to make well-informed decisions that optimize overall design efficacy. This proactive approach not only enhances the efficiency of the development process but also leads to improved outcomes for your projects, ensuring that every detail is accounted for and meticulously analyzed. Ultimately, MuSES empowers users to navigate complex thermal environments with confidence and precision.

Media

Media

Integrations Supported

Bash
CSS
Claude Agent SDK
Claude Code
Facebook
Facebook Messenger
Hermes Agent
JavaScript
Kotlin
Model Context Protocol (MCP)
Muse Video
Odysseus
OpenAI Agents SDK
OpenAI Codex
OpenClaw
PowerShell
R
SQL
Scala
YAML

Integrations Supported

Bash
CSS
Claude Agent SDK
Claude Code
Facebook
Facebook Messenger
Hermes Agent
JavaScript
Kotlin
Model Context Protocol (MCP)
Muse Video
Odysseus
OpenAI Agents SDK
OpenAI Codex
OpenClaw
PowerShell
R
SQL
Scala
YAML

API Availability

Has API

API Availability

Has API

Pricing Information

$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 Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

meta.ai

Company Facts

Organization Name

ThermoAnalytics

Date Founded

1996

Company Location

United States

Company Website

www.thermoanalytics.com/muses

Categories and Features

Simulation

1D Simulation
3D Modeling
3D Simulation
Agent-Based Modeling
Continuous Modeling
Design Analysis
Direct Manipulation
Discrete Event Modeling
Dynamic Modeling
Graphical Modeling
Industry Specific Database
Monte Carlo Simulation
Motion Modeling
Presentation Tools
Stochastic Modeling
Turbulence Modeling

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