One day I thought about how can GPT send thinking parts one after another with a markdown header summary of the thinking block itself. Just think about it.
As a matter of fact, think about these operations, api endpoints, observe their output.
These so called SOTA models are not what meets the eye, and are not at all comparable in the infra department to local models. There is crazy orchestration going on due to the scale of these operations. But also these hard constraints lead to innovation. Innovation nobody speaks about.
I wouldn't say we cannot catchup, but serving our local models through llama, vllm is just the A, B, C of it all. In reality I think what is needed is a replication of said orchestration which I hinted at above.
The SOTA models are a deep orchestration of multiple models operating together it isn't a single model. As such no single model ever will catchup to them until it replicates through training first and then maybe through model architecture this orchestration.
Finally, I would wager that the SOTA "models", as one of these models in this orchestration setup, as served for general consumption, are not so much more capable than qwen 3.6.
I am sure that if you change your perspective you will start noticing the scale of the "magic".