In this study, we aimed to compare GPT models fine-tuned on antimicrobial peptide sequence data with models that have been previously used to predict antimicrobial activity and hemolysis of peptide sequences. Alongside evaluating the performance of the fine-tuned GPT models, we also seek to explore the advantages and disadvantages they offer in terms of time and cost effectiveness. Furthermore, we compare the performance of models trained on amino acid sequences to a support-vector machine (SVM) trained on the MAP4C fingerprint.