There's an issue in the AI industry that I think deserves attention: when calling a model API, besides focusing on the model's capabilities, we should also pay attention to the actual service provider. The same model name doesn't necessarily mean that different service providers will perform identically. Details such as tool calls, context length, and model version can all affect the final user experience. This is why I'm paying attention to Mira's recent launch of Sterling. According to the official introduction, Sterling checks service providers in five aspects: model identity, tool calls, parameter compliance, silent failures, and context length, and publicly verifies and scores them before routing requests based on the verification results. Its approach is noteworthy: besides competing on model capabilities, inference costs, and speed, the ability to check the service process and compare the performance of different providers should also be considerations for developers when choosing services. Of course, the actual effectiveness of a product still needs to be judged through real-world use and continuous testing; official descriptions are not equivalent to independent verification conclusions. For AI developers, whether this type of infrastructure can truly improve the transparency of model calls remains to be seen.