Meta releases its first frontier model, Muse Wonder

Meta’s Superintelligence Lab released a pair of new state-of-the-art models on Monday, Muse Wonder and Muse Wonder Full, representing the company’s first attempt at a frontier model. Where Muse Spark aimed to be a lightweight model that could perform at low latency, the Wonder models are larger and said to be more capable, with higher benchmark scores and better results on complex tasks like coding.

Meta 超级智能实验室周一发布了一对全新的顶尖模型：Muse Wonder 和 Muse Wonder Full，这是该公司首次尝试开发前沿模型。与旨在实现低延迟的轻量级模型 Muse Spark 不同，Wonder 系列模型规模更大，据称能力更强，在基准测试中得分更高，且在编程等复杂任务中表现更佳。

According to Meta’s testing (presented in the model card), Wonder and Wonder Full achieve comparable benchmark scores to Fable 5.1 and GPT-6 Astra. “Together, they mark the next step on our scaling ladder and a major leap toward personal superintelligence,” Meta’s blog post about the models reads.

根据 Meta 的测试（详见模型卡），Wonder 和 Wonder Full 的基准测试得分与 Fable 5.1 和 GPT-6 Astra 相当。Meta 关于这些模型的博文写道：“它们共同标志着我们扩展阶梯上的下一步，也是迈向个人超级智能的一大步。”

That scale is reflected in the pricing: Accessed through the Meta Model API, Wonder will cost $4 per million input tokens and $20 per million output tokens, making it roughly equivalent in cost to GPT-6 Sol or Opus 5.5. Notably, Wonder Full will only be available to Muse Code subscribers and will not be served through the API. Both models will also be used to power the Muse agent.

这种规模也体现在定价上：通过 Meta 模型 API 访问，Wonder 的价格为每百万输入 token 4 美元，每百万输出 token 20 美元，成本大致相当于 GPT-6 Sol 或 Opus 5.5。值得注意的是，Wonder Full 仅向 Muse Code 订阅者提供，不会通过 API 服务。这两款模型都将用于驱动 Muse 智能体。

In keeping with Meta’s interest in developing personal assistants that can perform tasks over a long period, the company placed particular emphasis on the new models’ ability to retain and synthesize information given by the user, a property Meta calls “active memory.” To achieve better recall, Meta highlighted the MemoryBench benchmark, which challenges continual learning and memory construction frameworks.

为了契合 Meta 开发能够长期执行任务的个人助理的愿景，该公司特别强调了新模型保留和综合用户所提供信息的能力，Meta 将这一特性称为“主动记忆”。为了实现更好的召回效果，Meta 重点介绍了 MemoryBench 基准测试，该测试旨在挑战持续学习和记忆构建框架。

According to the company’s testing, Muse Wonder now scores higher on MemoryBench than Fable, Opus, or Astra. “We put a tremendous amount of effort into making sure your model can remember what’s important to you over days, weeks, months, and beyond,” said research scientist Kavya Kopparapu in a video accompanying the launch.

根据该公司的测试，Muse Wonder 在 MemoryBench 上的得分目前高于 Fable、Opus 或 Astra。研究科学家 Kavya Kopparapu 在发布会随附的视频中表示：“我们投入了巨大的努力，确保您的模型能够在数天、数周、数月乃至更长的时间里记住对您重要的事情。”

The new models are the first frontier model release since Scale AI founder Alexandr Wang restructured Meta’s AI efforts in August of last year. Since then, Meta has released its lightweight Muse Spark model and Muse agent but has avoided the kind of state-of-the-art model releases OpenAI and Anthropic are known for. According to the post, the models were trained at Meta’s Prometheus data center in central Ohio, which came online earlier this year.

这是自 Scale AI 创始人 Alexandr Wang 于去年 8 月重组 Meta 的 AI 业务以来，该公司发布的首批前沿模型。此后，Meta 虽然发布了轻量级的 Muse Spark 模型和 Muse 智能体，但一直避免发布 OpenAI 和 Anthropic 所擅长的那种顶尖模型。据博文称，这些模型是在 Meta 位于俄亥俄州中部的 Prometheus 数据中心训练的，该中心于今年早些时候投入使用。