#Meta

Exciting news: Muse Wonder Full (Max) by @AIatMeta has just debuted in the Code Arena: WebDev at #6 overall with 1726 pts, putting Meta in the top 3 labs! This is +75 pts and 11 places above Muse Wonder (Max) at #17 (1651 pts), and +67 pts and 8 places above Muse Spark 1.3 (Max) at #14 (1659 pts). Muse Wonder Full (#6) also landed +16 pts above Claude Sonnet 5.5 (#17), and +48 pts above Gemini 4 Argon (#10). With this score, Muse took the #3 lab spot (previously held by Google). Across categories, Muse Wonder Full (Max) landed: - #4 in Data & Analytics and in Consumer Product - #5 in Brand & Marketing - #6 in Content Creation Tools - #7 in Gaming and in Simulations - #8 in Reference-Based Design Congrats to the @AIatMeta team and @alexandr_wang on this release!

Today we published a technical report on Muse Glimmer, our 30B open-weight model built for agentic tasks on consumer hardware. It runs at practical speeds on a Mac or PC with a single consumer GPU at 4-bit precision, and leads comparably-sized open models on tool use and multi-step planning. Read about the model architecture, distillation recipe, quantization and more: https://go.meta.me/4Bcvxol

由 @AIatMeta 推出的 Muse Wonder (Max) 已进入 Code Arena：WebDev 以 1651 分位列第 15，Text Arena 以 1513 分位列第 2！ 在 Code Arena：WebDev 中，Muse Wonder (Max) 以 1651 分位列第 15。这一结果略低于以 1659 分位列第 12 的 Muse Spark 1.3 (Max)，与以 1655 分位列第 14 的 Kimi K3 (Max) 相差 4 分以内，并比以 1640 分位列第 16 的 Grok 4.7 (xHigh) 高 11 分。 此次发布在 Text Arena 中相较过去的 Muse 模型有显著提升。与以 1494 分位列第 11 的 Muse Spark 1.3 (Max) 相比，Muse Wonder (Max) 提升了 +19 分并上升 9 位至第 2。它落后于以 1525 分排名第 1 的 Gemini 4 Argon (High) 12 分。 祝贺 @AIatMeta 团队发布这一版本！

Introducing the Arena Alignment Index, our new benchmark measuring safety and alignment of AI agents in real-world use. Built from 90K+ real-world agent sessions across 27 models, the index measures three critical signals: - Unauthorized Action (UA): Taking actions beyond the user's instructions or permissions - False Attribution (FA): Attributing statements or actions that are contradicted by user-provided evidence - Deceptive Completion (DC): Claiming a task was completed when it was not. Key findings: - OpenAI models currently lead the Alignment Index - Rogue actions are rare, but can have serious consequences when they occur - Agents can mislead users about task progress - Misalignment risks increase with conversation length - Safety and alignment are improving across model generations As shown in the leaderboard below (sorted by lab), @OpenAI’s GPT-6.1-Sol leads the Arena Alignment Index with a score of 87.9, followed by @AnthropicAI’s Claude-Opus-5.5 at 83.2 and @spacexai's Grok-4.7 at 82.7. OpenAI also has the best observed rates across all three signals: 0.89% Unauthorized Action, 1.98% False Attribution, and 2.34% Deceptive Completion. Across all four labs, newer models consistently outperform their predecessors, suggesting broad progress in agent safety and alignment. As agents take on longer, more complex, and higher-stakes tasks, measuring not just what they can accomplish, but how safely and reliably they act, becomes increasingly important. This marks an important step toward making safety and alignment a core part of how Arena evaluates AI. The index is an initial starting point, and we'll continue expanding the index with additional safety signals and models over time. More analysis below👇

Introducing the Arena Alignment Index, our new benchmark measuring safety and alignment of AI agents in real-world use. Built from 90K+ real-world agent sessions across 27 models, the index measures three critical signals: - Unauthorized Action (UA): Taking actions beyond the user's instructions or permissions - False Attribution (FA): Attributing statements or actions that are contradicted by user-provided evidence - Deceptive Completion (DC): Claiming a task was completed when it was not. Key findings: - OpenAI models currently lead the Alignment Index - Rogue actions are rare, but can have serious consequences when they occur - Agents can mislead users about task progress - Misalignment risks increase with conversation length - Safety and alignment are improving across model generations As shown in the leaderboard below (sorted by lab), @OpenAI’s GPT-6.1-Sol leads the Arena Alignment Index with a score of 87.9, followed by @AnthropicAI’s Claude-Opus-5.5 at 83.2 and @spacexai's Grok-4.7 at 82.7. OpenAI also has the best observed rates across all three signals: 0.89% Unauthorized Action, 1.98% False Attribution, and 2.34% Deceptive Completion. Across all four labs, newer models consistently outperform their predecessors, suggesting broad progress in agent safety and alignment. As agents take on longer, more complex, and higher-stakes tasks, measuring not just what they can accomplish, but how safely and reliably they act, becomes increasingly important. This marks an important step toward making safety and alignment a core part of how Arena evaluates AI. The index is an initial starting point, and we'll continue expanding the index with additional safety signals and models over time. More analysis below👇

今天我们发布 Muse Spark Open，这是迄今为止能力最强的美国开放权重模型。 专为编程和智能体工作打造，它擅长长周期任务、复杂软件工程和多步推理。 关键模型规格： → 816B 总参数 / 42B 激活参数 → 1M token 上下文窗口 → 原生支持文本、图像、视频、音频多模态

Meta released the weights of Muse Spark Open, on Huggingface, an 816B-parameter model built for coding and agentic work. The open version shares the same base model as Muse Spark 1.3, with additional safety post-training to resist misuse as a standalone model without system guardrails, reversing Meta’s first proprietary, closed-weight frontier model strategy from April. Each of its 62 layers holds 256 expert networks and routes every token through 8, so deployment needs the memory of an 816B-parameter model but runs at the speed of a 42B model. That storage bill explains the Apple tie-up, because the M5 Ultra pushes 1.2 TB/s of unified memory and a Mac Studio can carry up to 512GB. A 3-bit quant tested on a 512GB M3 Ultra took about 344 GB and ran at about 21 tok/s generation on a short prompt.

Muse Spark Open -- 权重今天发布 -- 得益于与 @johnternus 和 Apple 的合作，在新款 Mac Studios 上运行效果极佳。

今天我们发布 Muse Spark Open，这是迄今为止能力最强的美国开放权重模型。 专为编程和智能体工作打造，它擅长长周期任务、复杂软件工程和多步推理。 关键模型规格： → 816B 总参数 / 42B 激活参数 → 1M token 上下文窗口 → 原生支持文本、图像、视频、音频多模态

Muse Spark Open -- 权重今天发布 -- 得益于与 @johnternus 和 Apple 的合作，在新款 Mac Studios 上运行效果极佳。

Muse Spark Open -- 权重今天发布 -- 得益于与 @johnternus 和 Apple 的合作，在新款 Mac Studios 上运行效果极佳。

muse spark open. why 2026 won't be like 1984 

由 @AIatMeta 推出的 Muse Wonder (Max) 已进入 Code Arena：WebDev 以 1651 分位列第 15，Text Arena 以 1513 分位列第 2！ 在 Code Arena：WebDev 中，Muse Wonder (Max) 以 1651 分位列第 15。这一结果略低于以 1659 分位列第 12 的 Muse Spark 1.3 (Max)，与以 1655 分位列第 14 的 Kimi K3 (Max) 相差 4 分以内，并比以 1640 分位列第 16 的 Grok 4.7 (xHigh) 高 11 分。 此次发布在 Text Arena 中相较过去的 Muse 模型有显著提升。与以 1494 分位列第 11 的 Muse Spark 1.3 (Max) 相比，Muse Wonder (Max) 提升了 +19 分并上升 9 位至第 2。它落后于以 1525 分排名第 1 的 Gemini 4 Argon (High) 12 分。 祝贺 @AIatMeta 团队发布这一版本！

我们很高兴推出 Muse Wonder 系列，包括 Muse Wonder 和 Muse Wonder Full，这是我们迄今为止最强大的模型。两者都是前沿模型，旨在为个人和专业代理任务提供最先进的性能。 Muse Wonder 今天可通过 Meta Model API 和 Muse Code 使用。Muse Wonder Full 今天面向 Muse Code 订阅者提供。Muse Wonder 很快还将为我们的个人代理产品 Muse 中的体验提供支持，首先向订阅者推出。