Over the past few years, discriminative and generative large language models (LLMs) have emerged as the predominant approaches in natural language processing. However, despite significant advancements, there remains a gap in comparing the performance of discriminative and generative LLMs in cross-lingual biomedical concept normalization. In this paper, we perform a comparative study across several LLMs on the challenging task of cross-lingual biomedical concept normalization via dense retrieval. We utilize the XL-BEL dataset covering 10 languages to evaluate the model’s capacity to generalize across various linguistic contexts without further adaptation. The experimental findings demonstrate that e5, a discriminative model, exhibited superior performance, whereas BioMistral emerged as the top-performing generative LLM. The code for reproducing the experiments is available at: https://github.com/hrouhizadeh/zsh_cl_bcn.