We present a morphosyntactically controlled transformation dataset for Quechua Collao and evaluate large language models on a sentence-level transformation task under varying prompting conditions. Results show that performance depends on the interaction between model behavior, context size, and linguistic complexity, with smaller models benefiting more from additional examples and morphological hints providing selective gains.