Muse Spark 1.1

Introducing Muse Spark 1.1 July 9, 2026 • 10 minute read

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.

今天，我们很高兴推出 Muse Spark 1.1，这是来自 Meta 超级智能实验室（Meta Superintelligence Labs）的最新模型，也是 Muse Spark 的重大升级。Muse Spark 1.1 是一款专为代理任务（agentic tasks）构建的多模态推理模型，在工具与计算机使用、编程以及多模态理解方面取得了显著进步。

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.

凭借这些改进，Muse Spark 1.1 推动了性能与效率的边界。随着本周 Muse Image 的发布，此次更新使我们离“个人超级智能”的愿景更近了一步：即通过模型帮助你追求目标、创造想象、加深人际关系，并为你最珍视的事物采取行动。

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.

伴随此次发布，我们还推出了全新 Meta Model API 的公开预览版，开发者可以通过该接口访问 Muse Spark 1.1。目前，该模型已在 Meta AI 应用及 meta.ai 网站的“思考”（Thinking）模式中可用。

Agents

智能体（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.

Muse Spark 1.1 在需要跨多个外部应用和服务进行规划与编排的个人代理任务中表现卓越。它能够零样本（zero-shot）泛化至新的原生工具、MCP 服务器以及自定义技能。

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.

它处理复杂项目的速度比 Muse Spark 快得多，因为它经过训练，能够编排多智能体系统以优化端到端延迟。作为主智能体，它可以收集上下文、制定计划，并将执行任务委派给并行运行的子智能体。作为子智能体，它能坚守职责、理解可用工具，并知道何时将任务升级回主智能体。

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 可以主动管理其 100 万 token 的上下文窗口。它能记住操作、检索早期工作中的信息，并以一种保留后续工作所需关键步骤的方式进行压缩。

Computer Use

计算机使用（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.

Muse Spark 1.1 擅长处理跨多个应用程序、且信息实时变化的计算机使用工作流。它能在长时间会话中保持上下文，适应不断变化的需求，并以极少的人工干预导航陌生的界面。

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.

Muse Spark 1.1 不会机械地对桌面上的每一步操作进行逐次点击推理，而是能够理解何时该自动化，何时该直接操作界面。我们训练该模型在自动化效率更高时编写脚本，在直接交互更简单时进行点击，并在每一步生成批量操作。

Coding

编程（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.

Muse Spark 1.1 在涉及大型复杂代码库的实际任务中，编程性能有了实质性提升。它能够诊断并修复复杂漏洞，在企业级系统中实现新功能，并执行大规模代码迁移。在创建 Web 应用和端到端问答等用例中，Muse Spark 1.1 相比我们的首个模型表现出了巨大的进步。

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.

我们训练模型以平滑适应各种开发环境，并可靠地处理复杂的多轮交互动态。Muse Spark 1.1 在主流的代理编程设置中表现良好，支持规划模式、目标条件设定、子智能体委派和上下文压缩等常用功能。

Multimodal

多模态（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.

除了编程和代理能力外，Muse Spark 1.1 在感知、多模态推理和工具使用方面也表现出色。它能够与真实环境交互并生成有据可依的输出，在视觉转代码（visual-to-code）工件生成、超详细的图像与视频描述，以及多模态用例的代理工作流执行方面具有优势。

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.

当感知与行动需要同步进行时，Muse Spark 1.1 的多模态能力尤为宝贵。该模型可以检查视觉和音频信息，在长工作流中保留细节，并在代表用户操作计算机时利用这些细节。

Safety

安全性（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.

在部署之前，我们遵循“高级 AI 扩展框架”（Advanced AI Scaling Framework）进行了广泛的安全评估，该框架为我们最先进的模型定义了评估标准、威胁模型和部署阈值。

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.

在所有前沿风险类别（化学与生物、网络安全、失控风险）中，我们的评估显示 Muse Spark 1.1 在安全范围内运行。Muse Spark 1.1 对直接越狱以及来自不可信数据、提示词注入和开发者提示词攻击的间接攻击表现出强大的防御能力。因此，它展现出了更好的对抗鲁棒性、更低的幻觉率以及更少的迎合倾向（sycophancy）。

Availability

可用性（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.

开发者首次可以通过全新的 Meta Model API 开始使用 Muse Spark 1.1 进行开发，该 API 现已开放公开预览。Muse Spark 1.1 的早期合作伙伴称赞该模型是一个完整的代理基础，并结合了长上下文能力。