  Background: Rapid advancements in natural language processing have led to the
development of large language models with the potential to revolutionize mental
health care. These models have shown promise in assisting clinicians and
providing support to individuals experiencing various psychological challenges.
  Objective: This study aims to compare the performance of two large language
models, GPT-4 and Chat-GPT, in responding to a set of 18 psychological prompts,
to assess their potential applicability in mental health care settings.
  Methods: A blind methodology was employed, with a clinical psychologist
evaluating the models' responses without knowledge of their origins. The
prompts encompassed a diverse range of mental health topics, including
depression, anxiety, and trauma, to ensure a comprehensive assessment.
  Results: The results demonstrated a significant difference in performance
between the two models (p > 0.05). GPT-4 achieved an average rating of 8.29 out
of 10, while Chat-GPT received an average rating of 6.52. The clinical
psychologist's evaluation suggested that GPT-4 was more effective at generating
clinically relevant and empathetic responses, thereby providing better support
and guidance to potential users.
  Conclusions: This study contributes to the growing body of literature on the
applicability of large language models in mental health care settings. The
findings underscore the importance of continued research and development in the
field to optimize these models for clinical use. Further investigation is
necessary to understand the specific factors underlying the performance
differences between the two models and to explore their generalizability across
various populations and mental health conditions.
