It is changing so often that to keep the costs and quality at a pareto level you need to experiment with a lot of different providers. And the pain you are describing should be a normal process in AI. I use model pools where the tasks are optimized for multiple AI providers. For example I was experimenting with GPT Luna and it turned out that the model is good but tool shy so I had to improve the instructions. Now this model is my main model for the chat in my app. Next week it can be a different model.