Deleted Tweet: Many API users underestimate the power of frontier models in native harnesses
English summary
Ethan Mollick (emollick) deleted a tweet stating that API users often fail to understand how much more powerful frontier AI models are when used in their native harnesses compared to bare API access. He removed the post because the character limit prevented him from distinguishing between those who carefully evaluate models in different harnesses for tasks and those who simply use the naked API. The observation points to a common misperception about model performance tied to deployment context.
Chinese summary
Ethan Mollick删除了一条推文，其中指出API用户往往不理解前沿AI模型在原生部署环境中的表现远强于直接调用裸API。他因字数限制无法区分那些认真评估不同工具链的用户和仅使用裸API的用户，故删除了该推文。此观察点明了部署环境对模型性能认知的常见误解。
Key points
Frontier models can demonstrate significantly higher capabilities when run in their native, optimized harnesses than through simple API calls.
前沿模型在原生优化部署环境下可以展现出远超简单API调用的强大能力。
Many API users remain unaware of this performance gap, creating a distorted understanding of model capabilities.
许多API用户并不知晓这一性能差距，从而导致对模型能力的理解存在偏差。
The author deleted the tweet to avoid blurring the line between careful evaluators and those using raw API without testing alternative harnesses.
推文作者为避免混淆认真测试不同部署方式的评估者与仅使用裸API的用户，主动删除了原帖。