Best AI Models for Muse Code
View:
Compare the Top AI Models that integrate with Muse Code as of September 2026
Sort By:
This a list of AI Models that integrate with Muse Code. Use the filters on the left to add additional filters for products that have integrations with Muse Code. View the products that work with Muse Code in the table below.
What are AI Models for Muse Code?
AI models are systems designed to simulate human intelligence by learning from data and solving complex tasks. They include specialized types like Large Language Models (LLMs) for text generation, image models for visual recognition and editing, and video models for processing and analyzing dynamic content. These models power applications such as chatbots, facial recognition, video summarization, and personalized recommendations. Their capabilities rely on advanced algorithms, extensive training datasets, and robust computational resources. AI models are transforming industries by automating processes, enhancing decision-making, and enabling creative innovations. Compare and read user reviews of the best AI Models for Muse Code currently available using the table below. This list is updated regularly.
-
1
Muse Spark 1.1
Meta
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.Starting Price: $1.25 per 1M tokens (input) -
2
Muse Spark 1.2
Meta
Muse Spark 1.2 is Meta’s coding-focused model update designed to power Muse Code and improve software engineering workflows. The model is built for code generation, complex debugging, codebase understanding, long-horizon development tasks, and end-to-end developer workflows. Muse Spark 1.2 was co-trained with Muse Code to improve performance inside the terminal coding agent environment. It supports planning, goal conditioning, context compaction, subagent coordination, and iterative coding workflows across large repositories. The model was trained with expanded coding compute, diverse development environments, self-improvement loops, and long-running engineering tasks. Built for AI developers and software teams, Muse Spark 1.2 helps agents plan, write, validate, debug, and optimize code with greater autonomy.Starting Price: $1.25 per 1M tokens (input) -
3
Muse Glimmer
Meta
Muse Glimmer is a 30-billion-parameter open-weights model from Meta Superintelligence Labs, optimized for always-on local agent workflows. Small enough to run on a Mac or PC with a single consumer GPU, it is designed for local agents, function calling, coding, and LLM-as-a-judge evaluation without depending on cloud infrastructure or network access. The model combines long-horizon execution, precise tool calling, multimodal understanding, long-context memory, and instruction following. It can complete end-to-end agentic tasks, sustain multi-step reasoning across extended workflows, recover from failed or unexpected tool calls, and accept interleaved text and images through a dedicated perception encoder for interpreting screenshots, charts, and documents. Muse Glimmer works with OpenClaw and other agentic orchestration patterns, supports controllable reasoning effort, and is trained on data from more than 100 languages.Starting Price: Free -
4
Muse Spark 1.3
Meta
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) -
5
Muse Spark
Meta
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.
- Previous
- You're on page 1
- Next
