All the things you mentioned make it a platform, but even as a model, none of the smaller open-source models come close to GPT 3.5 or 4 in my experience. You can test it by using the GPT3.5 or 4 with their API. They outputs are waaaay better than anything I get from the open source models.
I am not doubting you and my experience has been the same. My current home lab has a pretty good Jupyter server where I experiment with different local models vs GPT using LangChain and the simple chains can achieve some impressive parity with GPT3.5 depending on the use case and local model. Things do break down when we I do more complex things due to compute capacity. Im still running all of the local models on CPU mind you. I have not gotten to the point of testing on a high end GPU yet but based on what ive seen so far, it won't take much more to run smaller local models that are good enough. This is they key. On the client side, we want smaller more focused models. This is what the post linked in this thread hints at and I agree. We are months, if not weeks, if not days...and maybe hours (at this pace!) where those smaller more domain specific models are common. Still, they won't solve the issues I mentioned above. You will likely need to build your own platform around it, or pay exorbitant fees to host it in the Cloud.
