Large Language Models (LLMs) have emerged as promising tools to assist students while solving programming assignments. However, object-oriented programming (OOP), with its inherent complexity involving the identification of entities, relationships, and responsibilities, is not yet mastered by these tools. Contrary to introductory programming exercises, there exists a research gap with regards to the behavior of LLMs in OOP contexts. In this study, we experimented with three prominent LLMs - GPT-3.5, GPT-4, and Bard - to solve real-world OOP exercises used in educational settings, subsequently validating their solutions using an Automatic Assessment Tool (AAT). The findings revealed that while the models frequently achieved mostly working solutions to the exercises, they often overlooked the best practices of OOP. GPT-4 stood out as the most proficient, followed by GPT-3.5, with Bard trailing last. We advocate for a renewed emphasis on code quality when employing these models and explore the potential of pairing LLMs with AATs in pedagogical settings. In conclusion, while GPT-4 showcases promise, the deployment of these models in OOP education still mandates supervision.