Three completely different numbers people conflate when talking about LLM "size" — and mixing them up leads to genuinely wrong predictions about what a model can do.
"How big is this model?" sounds like one question. It isn't. A model's parameter count, its context window, and the token count of whatever you happen to be sending it right now are three unrelated numbers that live on three different layers of the system — one fixed at training time, one fixed by the model's architecture, and one that changes with literally every request. Treating them as interchangeable is how people end up assuming a "bigger" model can automatically read a longer document, or that a huge input means the model just got smarter. Neither follows.