Headnotes are concise summaries of court decisions that distill key legal points, enabling legal practitioners to navigate complex rulings efficiently. In this work, we evaluate the performance of the open-source, Swiss-focused Apertus family of models for the task of Swiss landmark decision summarisation. Our experiments establish the fully fine-tuned Apertus 8B model as the top performer within the Apertus family. It sets a robust open-weight baseline that is competitive with leading proprietary models on key lexical similarity metrics. However, we identify a significant adaptation challenge: while fine-tuning successfully teaches the models to adopt the specific structural format of Swiss headnotes, they often struggle to maintain the underlying legal reasoning. This results in a performance “regret” where models prioritize superficial stylistic alignment over logical coherence, particularly in cross-lingual settings. Our code and fine-tuned checkpoints are publicly available