Muse Spark 1.1
Muse Spark 1.1
Model Overview
Muse Spark 1.1 is a frontier-class, multimodal reasoning model developed by Meta Superintelligence Labs, released on July 9, 2026. Built specifically for agentic tasks rather than standard conversational chat, it acts as an autonomous "contractor." It possesses the ability to independently plan, orchestrate multi-agent systems, delegate execution, and use computer interfaces. It features a massive 1-million-token context window with active memory management. It is a closed-source model available for consumers via Meta AI's "Thinking" mode and for developers via the Meta Model API.
Capabilities
- Agentic Browser & Computer Control: Capable of autonomously automating web tasks (e.g., social media messaging) and visually verifying UI changes by taking and analyzing application screenshots.
- Multimodal Debugging: Can ingest codebases and correlate application screenshots directly with source code to visually and programmatically debug software.
- Long-Horizon Planning: Engineered for complex workflows, it can manage multi-step projects, delegate tasks to subagents, and maintain focus over long sessions.
- Active Memory Management: Utilizes a 1M token context window by dynamically remembering, retrieving, and compacting information to avoid losing track of critical steps in prolonged tasks.
Example Use Cases
- Autonomous Software Testing: Browsing a web application, executing QA tests, capturing screenshots of bugs, and tracing them back to the exact lines of code that need fixing.
- Multi-Agent Orchestration: Serving as the "lead developer" in a local environment, breaking down a large software project and delegating sub-tasks to smaller, specialized models.
- Workflow Automation: Navigating complex browser-based tools, logging into platforms, extracting data, and automating social media or administrative tasks end-to-end.
Performance & Benchmarks
Meta's internal coding benchmarks claim top-tier, frontier-level performance, particularly showing massive improvements over the previous Muse Spark 1.0 in scientific reasoning and agentic knowledge work. However, independent third-party leaderboards suggest that its raw coding and reasoning capabilities currently perform closer to advanced open-weight models (such as GLM 5.2), excelling primarily in its specialized agentic tooling and multi-modal integrations rather than raw zero-shot logic.
Intended Use & Limitations
Muse Spark 1.1 is intended for software engineers, enterprise automation workflows, and developers building complex AI agent systems via the Meta Model API. Due to its design as an autonomous agent ("contractor"), it may be overly complex or expensive for simple conversational queries. While it supports vision, audio, text, and PDFs, its autonomous browser-control features require careful sandboxing by developers to prevent unintended actions on live systems.
About Meta
Meta (formerly Facebook) is a global technology conglomerate and social media pioneer. Through Meta Superintelligence Labs, the company develops cutting-edge artificial intelligence systems. While Meta is highly recognized for its open-source contributions via the Llama family of models, the Muse Spark lineage represents its proprietary push into autonomous, frontier-class agentic AI designed for complex orchestration and enterprise-level automation.
Key Features
Multimodal debugging by correlating application screenshots directly with source code
Agentic browser control for automating web tasks (e.g., social media messaging)
Long-horizon planning for autonomous execution
You might also want to compare
Verified Sources
Tags
Model Specs
Parameters
Undisclosed
Context Window
undisclosed
License
Proprietary
Deployment
Resources & Links
Lineage
Model Family
Part of the Muse Spark family
Only release in this line currently tracked.
Curator Notes
While internal Meta coding benchmarks claim top-tier performance, independent leaderboards suggest it currently performs closer to open-source models like GLM 5.2.
Compare Specs
Compare parameters, context windows, modalities, and benchmark scores of this model side-by-side with others.
Compare Model