It probably depends what your objective is. One of the benefits you get from running less capable models is that it's easier to understand what their limitations are. The shortcomings of more powerful models are harder to see and understand, because the models themselves are so much more capable.
If you have no interest in the inner workings of LLMs and you just want the machine to spit out some end result while putting in minimal time and effort, then yes, absolutely don't waste your time with smaller, less capable models.
You can kinda get a feel for what they're good at, if you get what I mean?
Even the big online models have very specific styles and preferences for similar tasks. You can easily test this by giving them all some generic task without too many limits, each of them will gravitate towards a different solution to the same problem.
If you have no interest in the inner workings of LLMs and you just want the machine to spit out some end result while putting in minimal time and effort, then yes, absolutely don't waste your time with smaller, less capable models.