AI has completely taken over my mobile phone and computer work environment, handling more and more of my work. However, one question has puzzled me for a long time: why does the same AI model seem different when used in different places? Sometimes the answers are okay, but it freezes when I need to call a tool; even though I've set the output length, it still doesn't listen; and when I input a large amount of material, it seems like I've only read half of it. My first reaction to these situations is usually: has the model become stupid again, or did I write the wrong prompts? Or is it just my imagination? After some research, I found that this difference could stem from the product's system prompts, network capabilities, memory, and context processing, or it could be due to the provider of the inference service. Especially now that many tools and platforms call models via APIs, it's difficult to know which company is actually providing the service. From training to actually providing an answer, there are deployment, inference software, parameter processing, and tool compatibility issues. The same model name doesn't mean these aspects are done equally well. Even if the model hasn't changed, service configuration and software problems can still negatively impact the experience. Of course, the possibility of model replacement also needs to be checked; conclusions shouldn't be drawn based on a single poor response. @miranetwork's recently launched Sterling provides a "verified inference service": Mira tests and publicly scores the service providers behind each model, then prioritizes routing requests to qualified providers based on the verification results. Specifically, it checks whether the model output matches the reference sample, whether the tool call is normal, whether the parameters are executed as required, and whether there are any silent failures such as "the interface shows success but actually returns empty content." Context length is also being checked, but some endpoints I checked currently show that this is not yet enabled. Verification results can be viewed here: https://sterling.mira.network/verification/ In actual use, Sterling provides more than just a scoring report. It connects to different models through an API key; developers only need to adjust the base URL to continue calling the original model. Currently, it supports Claude, GPT, and Gemini. Of course, "verified" does not mean absolutely reliable. Sterling is currently in beta, and the testing involves sampling; there's no guarantee that every request will perfectly meet expectations, nor does it mean the AI's answer will always be correct. For me, the most practical value is that when choosing AI services in the future, besides considering the model name, price, and user experience, I can also look at publicly available quality assurance records. I also want to know if the money I spend has bought me the promised capabilities. This direction is still very valuable and worth continuing to observe.