There is no doubt that for many tasks the SotA models of OpenAI and Anthropic are better than the available open weights models.

Nevertheless, I do not believe that either OpenAI or Anthropic or Google know any secret sauce for better training LLMs. I believe that their current superiority is just due to brute force. This means that their LLMs are bigger and they have been trained on much more data than the other LLM producers have been able to access.

Moreover, for myself, I can extract much more value from an LLM that is not constrained by being metered by token cost and where I have full control on the harness used to run the model. Even if the OpenAI or Anthropic models had been much better in comparison with the competing models, I would have still been able to accomplish more useful work with an open-weights model.

I have already passed once through the transition from fast mainframes and minicomputers that I was accessing remotely by sharing them with other users, to slow personal computers over which I had absolute control. Despite the differences in theoretical performance, I could do much more with a PC and the same is true when I have absolute control over an LLM.