Tokenization can significantly impact the performance of language models. Good tokenization can enhance the model's ability to capture semantic nuances, understand syntactic structures, and deal with out-of-vocabulary words. Conversely, incorrect or inadequate tokenization can lead to misinterpretation, loss of information, or reduced overall performance in NLP tasks.