Right that is a good distinction. Fair enough. Still stand that you could train a worse model depending on the task. Translation, Nuanced Classification are all instances where i've not seen bespoke models outright better than GPT-4. although, like i said it could still be good enough for speed, compute requirements.
There's a key distinction between smaller models trained with transfer-learning, and just fine-tuning a smaller LLM and still using in-context learning.
Transfer learning means you're training an output network specifically for the task you're doing. So like, if you're doing classification, you output a vector with one element per class, apply a softmax transformation, and train on a negative log likelihood objective. This is direct and effective.
Fine-tuning a smaller LLM so that it's still learning to do text generation, but it's better at the kinds of tasks you want to do, is a much more mixed experience. The text generation is still really difficult, and it's really difficult to learn to follow instructions. So all of this still really favours size.