Researchers have extended LiveCodeBench (LCB) to support multiple programming languages, addressing a significant limitation of the original benchmark, which was restricted to Python. This development, known as Multi-LCB, enables a more comprehensive evaluation of large language models (LLMs) on code-generation tasks across various programming languages. By incorporating a diverse set of languages, Multi-LCB provides a more nuanced understanding of LLMs' coding capabilities, allowing for contamination-aware evaluation and a holistic assessment of their performance. The extension of LCB to multiple languages has significant implications for the development and evaluation of LLMs, as it enables a more thorough examination of their strengths and weaknesses1. This matters to practitioners and researchers because it can inform the development of more effective and language-agnostic coding models, ultimately impacting the security and reliability of AI-powered systems.