The rapid evolution of natural language processing has seen significant advancements in language models, particularly for languages with simpler orthographies. However, challenges persist in accurately processing and understanding languages with complex morphological structures, such as Chinese, due to the limitations of traditional tokenization methods. Introducing mega tokenization, which involves significantly larger tokens, represents a novel and transformative approach that enhances semantic preservation and contextual coherence in sophisticated character sequences. The study compares the performance of an adapted language model with mega tokenization against a standard model, demonstrating substantial improvements across tasks such as machine translation, text summarisation, and question answering. Through rigorous evaluation and statistical analysis, the adapted model shows superior performance metrics, indicating the effectiveness of mega tokenization in addressing the unique challenges posed by the Chinese language. The implications of this approach extend to various applications, underscoring its potential to revolutionise language processing in multilingual and high-stakes environments. Future research directions are proposed to further optimise and expand the applicability of mega tokenization across diverse linguistic contexts.