Meta 发布首个前沿模型 Muse Wonder 与 Muse Wonder Full
Meta releases its first frontier model, Muse Wonder
- 1Meta 超级智能实验室发布 Muse Wonder 与 Muse Wonder Full，是该公司首次推出前沿模型
- 2Meta 自测称两款模型基准分数与 Fable 5.1、GPT-6 Astra 相当（官方口径，暂无独立验证）
- 3Wonder 经 Meta Model API 定价每百万输入 token $4.00、输出 token $20.00，与 GPT-6 Sol 或 Opus 5.5 成本相近
- 4Meta 主打 active memory 长期记忆能力，自测称 Wonder 在 MemoryBench 得分超过 Fable、Opus 与 Astra
Meta 在 Alexandr Wang 重组 AI 业务后重返前沿模型竞争，直接对标第一梯队定价，把差异化重点放在长期记忆能力。
- 影响：定价与 GPT-6 Sol、Opus 5.5 同级，模型选型多一个同价对比项；官方主打长期记忆，指向长周期任务型助手场景；Wonder Full 不开放 API，采购时需留意渠道限制。
- 参考：可考虑把 Wonder 加入现有模型评测清单，用自有任务跑一轮基准与成本对比；需要长周期记忆的场景值得单独测试 active memory 表现；注意 Wonder Full 仅随 Muse Code 订阅提供。
- 依据：基准相当与 MemoryBench 领先均为 Meta 官方自测口径，暂无独立验证；定价与渠道限制为官方发布信息，由 TechCrunch 转述。
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.
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.
That scale is reflected in the pricing: Accessed through the Meta Model API, Wonder will cost $4.00 per million input tokens and $20.00 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.
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. 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.
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.
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Russell Brandom has been covering the tech industry since 2012, with a focus on platform policy and emerging technologies. He previously worked at The Verge and Rest of World, and has written for Wired, The Awl and MIT’s Technology Review. He can be reached at [email protected] or on Signal at 412-401-5489.
来源：TechCrunch · AI · techcrunch.com