Today, we’re excited to introduce Muse Spark 1.1, the latest model from Meta Superintelligence Labs and a significant upgrade from Muse Spark. Muse Spark 1.1 is a multimodal reasoning model built for agentic tasks, with major gains in tool and computer use, coding, and multimodal understanding.
With these improvements, Muse Spark 1.1 advances the performance-efficiency frontier. Together with this week’s launch of Muse Image, this release brings us closer to our vision of personal superintelligence: models that help you pursue your goals, create what you imagine, deepen your relationships, and take action on what you value most.
Along with this release, we are launching a public preview of the new Meta Model API where developers can access Muse Spark 1.1. The model is available now in "Thinking" mode in the Meta AI app and on meta.ai.
Evaluations
Agents
Muse Spark 1.1 delivers exceptional performance in personal agentic tasks that require planning and orchestration across a range of external apps and services. It zero-shot generalizes to new native tools, MCP servers, and custom skills.
It tackles complex projects significantly faster than Muse Spark, as it is trained to orchestrate multi-agent systems to optimize end-to-end latency. As the main agent, it can gather context, make a plan, and delegate execution across parallel subagents. As a subagent, it adheres to its job, understands available tools, and knows when to escalate back to the main agent.
Muse Spark 1.1 can actively manage its context window of 1 million tokens. It remembers actions, retrieves information from much earlier work, and compacts in a way that keeps the critical steps needed for later work.
Muse Spark 1.1 excels at computer-use workflows that unfold across multiple applications with information changing on-the-fly. It maintains context across extended sessions, adapts to evolving requirements, and navigates unfamiliar interfaces with minimal human intervention.
Rather than reasoning through every desktop step one click at a time, Muse Spark 1.1 understands when to automate and when to use the interface directly. We trained the model to write scripts when automation is faster, click when direct interaction is simpler, and generate batches of actions at each step.
Agentic dinner party organization: In real-world applications, new context arises that changes the task. Muse Spark 1.1 notices these changes when placing the dinner order and makes necessary updates without user intervention.
Coding
Coding performance for Muse Spark 1.1 improved substantially on real-world tasks involving large, complex codebases. It can diagnose and fix complex bugs, implement new features in enterprise-grade systems, and execute large code migrations. In use cases like creating web applications and end-to-end question answering, Muse Spark 1.1 shows large gains over our first model.
We trained our model to smoothly adapt to diverse harnesses and reliably handle complex multi-turn dynamics. Muse Spark 1.1 performs well with popular agentic coding setups, supporting common features like planning mode, goal conditioning, subagent delegation, and context compaction.
Debugging demo in OpenCode: Muse Spark 1.1 builds a chat web app, takes automated screenshots to identify user-visible failures, traces issues back to relevant code to implement fixes, and validates these changes. The model seamlessly combines coding, multimodal understanding, and tool calling.
Across Meta, developers and researchers are using Muse Spark 1.1 daily to build faster and work smarter. On our primary internal coding evaluation, Meta Internal Coding Bench, Muse Spark 1.1 significantly improves upon Muse Spark and is competitive with leading alternatives.
Researchers are now also automating model development and evaluation tasks by leveraging Muse Spark 1.1 in their workflows.
DeepSWE evaluation in OpenCode: Muse Spark 1.1 evaluates itself on a subset of DeepSWE tasks across different reasoning strengths and produces an analysis dashboard based on the results.
Along with coding and agentic capabilities, Muse Spark 1.1 excels in perception, multimodal reasoning, and tool use. It can interact with real environments and produce grounded outputs with strengths in visual-to-code artifact generation, ultra-descriptive image and video captioning, and agentic workflow execution for multimodal use cases.
Muse Spark 1.1’s multimodal capabilities are especially valuable when perception and action need to happen together. The model can inspect visual and audio, preserve details across a long workflow, and use those details while operating computers on the user’s behalf.
Facebook Marketplace agent: Using video shot from a smartphone, Muse Spark 1.1 extracts useful photos and reasons about the product to operate a user's browser and make a Facebook Marketplace listing on the user's behalf.
Safety
We conducted extensive safety evaluations before deployment, following the Advanced AI Scaling Framework, which defines evaluations, threat models, and deployment thresholds for our most advanced models.
Across all frontier risk categories — Chemical & Biological, Cybersecurity, and Loss of Control — our evaluations show Muse Spark 1.1 operates within safe margins. Muse Spark 1.1 demonstrates strong resistance to direct jailbreaks and indirect attacks from untrusted data, prompt injection, and developer-prompt attacks. Consequently, it shows better adversarial robustness, lower hallucination rates, and reduced sycophancy.
Our full safety posture for 1.1 is documented in our Muse Spark 1.1 Evaluation Report.
Availability
For the first time, developers can begin building with Muse Spark 1.1 via the new Meta Model API, now in public preview. Early partners of Muse Spark 1.1 praise the model as a complete agentic foundation, pairing long context handling with strong coding and reasoning capabilities to handle large-scale agentic workloads.
“What’s most impressive about Muse Spark is how much it packs into one model: massive million-token context, full multimodal support (images, video, PDFs), built-in search with citations, strong reasoning, top-tier coding abilities (particularly frontend and design), structured output, and parallel tool calling — all in a clean OpenAI-compatible package. A complete agentic foundation."
— Amjad Masad, CEO of Replit
“Meta is clearly building for serious agentic coding – strong tool use at a price point that makes it viable to run real coding workloads at scale. That combination is rare, and it’s exactly why we wanted Cline developers to have access early.”
— Saoud Rizwan, CEO of Cline
“When tested against Box’s enterprise work evaluation set, Muse Spark delivered enterprise capabilities competitive with today's leading frontier models. That level of intelligence, combined with its strengths in structured, procedural workflows across industries such as professional services, public sector, and industrial operations, makes it a compelling choice for organizations.”
— Yashodha Bhavnani, VP of AI Products at Box
We're thrilled to be releasing Muse Spark 1.1, a testament to our research momentum. We have even more capable models in training and look forward to sharing what’s to come.
Written by:
Meta Superintelligence Labs