Best AI Models for Facebook

Muse Spark 1.2 is Meta’s newest coding-focused model, released alongside Muse Code as part of Meta’s AI developer platform. The model improves on Muse Spark 1.1 with stronger code generation, complex debugging, codebase understanding, and full developer workflow performance. Muse Spark 1.2 powers Muse Code, a terminal coding agent that can plan changes, write code, validate results, and coordinate persistent background subagents. The model was co-trained with Muse Code so it performs well inside the agentic coding runtime and tool environment. Its training included scaled coding compute, broader training environment diversity, rejection-sampled harness trajectories, recipe optimizations, and Muse Code toolset integration. Muse Spark 1.2 is designed for long-horizon coding tasks such as whole-repository generation, large end-to-end projects, auto-research, and extended optimization work. It uses planning to sequence work, goal conditioning to stay aligned with the user’s objective, and context compaction to preserve useful knowledge over long sessions. The model also benefits from a self-improvement loop where Muse Spark 1.1 generated challenging coding environments and instruction-following templates for training. By combining coding specialization, agentic workflow support, long-horizon training, subagent compatibility, and Meta Model API availability, Muse Spark 1.2 helps developers build, debug, and optimize software more effectively.

Muse Spark 1.1 is Meta’s upgraded multimodal reasoning model designed to support advanced agentic workflows, coding tasks, computer use, and complex tool orchestration. Developed by Meta Superintelligence Labs, it builds on Muse Spark with major gains in planning, tool use, long-context reasoning, multimodal perception, and real-world task execution. The model can work across external apps and services, native tools, MCP servers, custom skills, browsers, scripts, images, video, PDFs, and audio inputs. Muse Spark 1.1 can act as a main agent by gathering context, creating a plan, and delegating work to parallel subagents, or operate as a subagent that follows instructions and escalates when needed. Its 1 million token context window allows it to retain earlier actions, retrieve information from long workflows, and compact context while preserving critical details. The model is also trained for computer-use tasks, deciding when to automate with scripts and when to interact directly with an interface. In coding workflows, Muse Spark 1.1 can diagnose bugs, implement features, migrate large codebases, generate web applications, take screenshots, identify UI issues, and validate fixes. Its multimodal strengths include visual-to-code generation, detailed image and video captioning, grounded perception, and workflows where seeing, reasoning, and acting happen together. Available through the Meta Model API public preview and in Thinking mode inside Meta AI, Muse Spark 1.1 gives developers and users a more capable foundation for building agents, automations, coding assistants, and multimodal productivity tools.

Muse Spark is Meta’s first model in the Muse family, designed as a natively multimodal AI system focused on advanced reasoning and real-world applications. It combines text, visual understanding, and tool usage to provide more interactive and context-aware responses. The model introduces capabilities like visual chain-of-thought reasoning and multi-agent orchestration for complex problem-solving. Its Contemplating mode allows multiple AI agents to work in parallel, improving accuracy on challenging tasks. Muse Spark performs strongly across domains such as STEM reasoning, health insights, and multimodal perception. It can analyze images, generate interactive outputs, and assist with tasks like troubleshooting or educational content. The model is trained using improved pretraining, reinforcement learning, and efficient test-time reasoning techniques. It is designed to scale efficiently while delivering high performance with optimized compute usage. Safety measures include strong refusal behavior and alignment safeguards across high-risk domains. Overall, Muse Spark is a foundational step toward building personalized, highly capable AI systems.

Muse Image is Meta’s first image generation model from Meta Superintelligence Labs, designed to make Meta AI a more capable creative assistant for visual content creation. The model allows users to generate images from simple prompts, edit existing photos, blend multiple images, remove unwanted background elements, and create polished visuals that can be shared across chats, stories, feeds, and other Meta surfaces. It supports a wide range of creative styles, including photorealistic portraits, Renaissance paintings, 16-bit characters, claymation scenes, stickers, movie posters, product shots, room makeovers, infographics, and stylized illustrations. Muse Image is built to reason through prompts before creating an image, using Muse Spark to plan composition, incorporate real-time web context, and combine different visual references into a coherent output. Meta AI also includes presets to help users start quickly, such as restoring an old family photo, trying a new hairstyle, reimagining a person as a game character, or generating a themed visual effect. Users can personalize images by @-mentioning public Instagram profiles in the Meta AI app and can control whether their own content is available for this kind of AI creation. The editing experience lets users circle, sketch, or mark up changes directly on an image while Meta AI keeps track of the conversation context. Muse Image is available in Meta AI and also powers new creative tools in Instagram Stories and WhatsApp, with Facebook, Messenger, and advertiser availability planned. By combining generation, editing, personalization, and sharing, Muse Image gives users a flexible way to turn everyday ideas into high-quality visual content.

Muse Video is Meta’s upcoming AI video generation model developed by Meta Superintelligence Labs as part of the company’s new media generation lineup. Previewed with Muse Image, the model is built on the same pretraining base and is designed to produce visually detailed videos with native audio support. Muse Video is focused on generating clips that follow prompts closely, maintain strong visual fidelity, and preserve temporal consistency across motion and scene changes. It can create realistic short videos with clear beginnings, actions, and payoffs, such as animals moving through a scene, handheld first-person footage, product commercials, and UGC-style social ads. The model supports audio-rich outputs that may include environmental sound, foley, music, voiceover, and synchronized spoken dialogue. Meta highlights Muse Video’s ability to handle cinematic prompts, vertical ad formats, realistic camera movement, product demonstrations, and emotionally engaging creative concepts. The company is still investing in improvements for difficult areas such as audio-video sync and physically accurate fast motion. Muse Video is expected to become available to creators and in Meta AI, expanding Meta’s generative AI tools from image creation into video. As part of Meta’s broader creative ecosystem, Muse Video is built to help users, creators, and businesses turn prompts into dynamic, shareable video content.