Image classification is one of the fundamental tasks in computer vision (CV) and has numerous practical applications. Traditionally, machine learning and deep learning methods such as k-Nearest Neighbors (kNN), decision trees, and Convolutional Neural Networks (CNN) have been widely used to perform this task. However, with the recent emergence of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), originally designed for natural language processing, their cross-domain applications, including in CV, are now being explored. In this paper, we investigate the capabilities of GPT-4o, a variant of the GPT model, for image classification on the Fashion-MNIST dataset. By using carefully designed prompts, we evaluate GPT-4o’s performance and compare it with more traditional models. Our study offers insights into the cross-domain potential of GPT models, explores how prompt engineering can enhance GPT’s performance on image classification tasks, and suggests new avenues for developing more flexible and adaptable multimodal LLM systems. The code can be found at https://github.com/Tanghaha1424/gpt-fashionmnist .