These days, practically everyone uses tools like Claude, GPT, and Gemini. If you've been using them long-term, have you noticed that despite paying for Claude or GPT, you're not actually running the same API? The same model running on different service providers can perform drastically differently. Some people use the same open-source model in benchmarks, and switching providers can boost performance from 37% to 93%. Some secretly use smaller versions at the same price; some tools are unreliable, failing at crucial points or faking success; some quietly halve the context, still returning a 200 error, which you can't detect. One team monitored 189 endpoints for four months and discovered 37 unannounced changes, all silently implemented, with seemingly normal responses. @miranetwork just launched Sterling, specifically for this reason. It doesn't run the model itself; instead, it tests at five endpoints at different providers to verify if the model is correct, if the tool calls are working correctly, if the parameters are responsive, if there are any silent failures, and if the context length is accurate. After testing, the scores are publicly available. When you're adjusting them, priority is given to the one that passes the test; those that don't pass are ranked later. The first batch includes Claude, GPT, and Gemini, with DeepSeek and GLM following later. You only need to change the base URL with a key; the code is basically untouched. During the initial launch, some models are discounted by up to 50%, with pre-paid top-ups charged per use; you can top up with USDC or MIRA. AI professionals are currently focused on acquiring models; very few people are seriously working on this kind of product verification. Those interested can learn more; check out the scoring page yourself. http://sterling.mira.network