<p>Accurate prediction of peptide–T cell receptor (TCR) binding is vital for immunotherapy, vaccine design and diagnostics. PanPep, a meta-learning framework, was developed to generalize diverse TCR binder predictions. Here we present a comprehensive and unbiased evaluation of PanPep’s reusability and practical utility. We reproduced its reported performance on original datasets and further benchmarked it against the control tools using both classification metrics and virtual screening enrichment evaluations. Leveraging a newly curated independent dataset, we demonstrated that PanPep generalizes better than other methods to unseen antigens with few or no known TCR binders under a background-drawn negative sampling strategy; however, this advantage diminished under reshuffled negatives, which present a more challenging evaluation setting by introducing harder negative examples. We further extended PanPep to peptide–TCRα and peptide–TCRαβ binding prediction, demonstrating its applicability in more biologically and physiologically relevant contexts. Despite its strengths, PanPep exhibits limitations in early binder enrichment and reduced robustness to novel TCRs, indicating sensitivity of performance to model architecture, training data composition and negative sampling strategies. This work establishes a reproducible and extensible benchmarking framework for general peptide–TCR binding prediction. The results provide practical guidance for model selection and robustness assessment in real-world TCR binder discovery. At the same time, our findings highlight that accurate, generalizable TCR binding prediction remains an open challenge.</p>