Introducing Muse Spark 1.1

July 9, 2026

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.

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.

Computer Use

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.

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.

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.

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.

Multimodal

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.

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.

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.

Partner quotes include Amjad Masad (Replit CEO), Saoud Rizwan (Cline CEO), Yashodha Bhavnani (VP of AI Products at Box), and Dave Morin (OpenClaw Foundation).

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.