This study investigates the biometric capabilities of ChatGPT-4o, evaluating its performance on age estimation, gender classification, and identity verification across two challenging datasets: the ITWCC (images of children aged 6–17) and a pediatric surgery dataset. By leveraging tailored prompts that bypass safety filters, ChatGPT-4o outperformed conventional CNN-based models such as DeepFace, achieving higher accuracy and offering interpretable, rationale-rich outputs. Specifically, it delivered a mean absolute error of 1.8 years in age estimation, 96–100% gender classification accuracy, and over 85% identity continuity recognition, even across surgical transformations. The findings demonstrate the potential of multimodal LLMs to complement or exceed traditional approaches in face analysis tasks, though the study notes the importance of expanding demographic diversity, refining prompt strategies, and ensuring fairness and robustness in real-world settings.