Reconstructing the timeline of Systemic Anticancer Therapy (SACT) or “chemotherapy” from heterogeneous Electronic Health Record(EHR) notes is a challenging task. Rapid developments in Large Language Models (LLMs), including a range of architectural improvements and post-training refinements since the 2024 Chemotherapy Timelines Task could make this task more tractable. We evaluated the performance of 4 recently released LLMs (GPT-4.1-mini, Phi4 and 2 Qwen3 models) on this task. Our results indicate that even witha variety of prompt optimization and synthetic data training, more work is still needed to see a useful application of this work.