Compare the Top Large Language Models that integrate with Muse as of October 2026

This a list of Large Language Models that integrate with Muse. Use the filters on the left to add additional filters for products that have integrations with Muse. View the products that work with Muse in the table below.

What are Large Language Models for Muse?

Large language models are artificial neural networks used to process and understand natural language. Commonly trained on large datasets, they can be used for a variety of tasks such as text generation, text classification, question answering, and machine translation. Over time, these models have continued to improve, allowing for better accuracy and greater performance on a variety of tasks. Compare and read user reviews of the best Large Language Models for Muse currently available using the table below. This list is updated regularly.

  • 1
    Muse Wonder
    Muse Wonder is a frontier artificial intelligence model developed by Meta for personal assistance, professional workflows, software development, and autonomous agent tasks. The model combines Active Memory, which retrieves relevant information from previous conversations, with Active Focus, which supports sustained execution of complex, multistep assignments. Muse Wonder can assist with research, document analysis, financial and legal information, software engineering, and business process automation. Its agentic capabilities enable it to work across applications, use contextual information, adapt to obstacles, and request clarification or approval when necessary. The model supports coding activities ranging from small application development to complex software projects involving repeated testing and revision. Muse Wonder is available through the Meta Model API and Muse Code, with additional integration into Meta's Muse assistant planned for future releases.
    Starting Price: $4 per 1M tokens (input)
  • 2
    Muse Spark 1.3
    Muse Spark 1.3 is an AI model with improved performance across agentic and coding tasks, designed to be smarter and more practical for real-world work. It sustains longer-horizon tasks by collaborating with users and managing multiple workflows in a single, long thread. Given an open-ended objective, it uses tools to build context across messy or conflicting sources, correct gaps in its plan, track what it has learned, and produce a final deliverable. It asks clarifying questions when prompts are ambiguous, requests help when stuck, and confirms before taking consequential actions. The model follows complex, long-form instructions more reliably, preserving detailed requirements across multi-step tasks without dropping constraints or drifting from the requested workflow. Improved multitasking allows it to map incoming prompts to the correct task even when users interrupt or redirect previous requests.
    Starting Price: $1.25 per 1M tokens (input)
  • 3
    Muse Wonder Full
    Muse Wonder Full is an advanced artificial intelligence foundation model developed by Meta for complex reasoning, autonomous agent workflows, software engineering, and professional knowledge work. As part of the Muse Wonder model family, it is designed to handle demanding tasks that require sustained reasoning, contextual understanding, and multistep execution. The model incorporates Active Memory to retain important information across conversations and retrieve relevant context when needed. Its Active Focus capabilities support long-running assignments by exploring alternative approaches, resolving obstacles, and adapting to changing requirements. Muse Wonder Full also supports software development, legal and financial analysis, business workflow automation, and agentic interactions with external tools. The model is available to Muse Code subscribers and has been evaluated on benchmarks covering coding, memory, professional tasks, and general problem-solving capabilities.
    Starting Price: $50/month
  • 4
    Muse Spark
    Muse Spark is a multimodal AI reasoning model developed by Meta as part of its push toward personal superintelligence. It integrates text, images, and tools to deliver advanced reasoning and interactive capabilities. The model supports features like visual chain-of-thought and multi-agent orchestration. Users can leverage Muse Spark for tasks such as problem-solving, content creation, and real-world troubleshooting. Its Contemplating mode enables multiple AI agents to reason in parallel for improved performance. Muse Spark also demonstrates strong capabilities in areas like health insights and visual understanding. Overall, it represents a significant step toward more intelligent and personalized AI systems.
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