Cervical cancer remains a leading cause of mortality among women, underscoring the need for accurate and reliable screening tools. While deep learning (DL) models have shown promise in cervical cancer classification, most approaches overlook the crucial aspect of uncertainty quantification, which is essential for trustworthy predictions in clinical settings. In this study, we explore the application of the UNI histopathology foundation model to cytology data, comparing its performance with two states of the art convolutional neural networks (CNNs): ResNet50 and DenseNet121. We assess each model on classification performance, calibration and their uncertainty as well as their ability to represent class separability using t-SNE visualizations. Our results demonstrate that the UNI model significantly outperforms ResNet50 and DenseNet121, achieving near-perfect accuracy and calibration. t-SNE plots further reveal that the UNI model produces well-defined, distinct clusters, indicating superior feature representation. Furthermore, the analysis of predictive entropy shows that the UNI model exhibits lower uncertainty in well-classified samples and effectively distinguishes between correct and incorrect predictions, reinforcing its reliability. These findings suggest that the UNI model is a highly effective and reliable tool for cervical cancer screening, paving the way for future advancements in uncertainty-aware diagnostic applications and the adoption of Histopathology model in Cytopathology.