This study conducted an in-depth analysis of responses generated by ChatGPT, a popular language model-based chat application, in the context of mental health conversations. The analysis encompassed three key dimensions: word count, sentiment analysis, and response quality. ChatGPT consistently outperformed the dataset, delivering longer responses with an average word count of 352, compared to the dataset’s 70. This raises questions about the depth of information conveyed. ChatGPT responses demonstrated an average sentiment score of 0.53, surpassing the dataset’s average of 0.23. This heightened sentiment in ChatGPT responses, coupled with their more extensive word count, suggests a potential for enhanced emotional engagement and nuanced communication, particularly crucial in mental health support scenarios. Finally, ChatGPT responses exhibited a more substantial incorporation of relevant information, showcased a higher degree of empathy toward user queries, and elevated sentiment scores, positioning it as a potentially effective mental health assistant. These findings offer valuable insights into the potential role of ChatGPT as a mental health assistant. However, it is imperative to emphasize the need for further research employing larger and more diverse datasets to comprehensively assess its practical applicability. Additionally, ethical considerations surrounding chatbots in mental health support warrant careful examination in future studies.