This paper explores the integration of large language models (LLMs) into building energy modeling (BEM) to enhance efficiency, accessibility, and performance. The study highlights the transformative potential of LLMs in addressing the challenges of BEM, which traditionally requires extensive expertise and specialized knowledge. LLMs can streamline BEM processes by automating tasks such as simulation input generation, output analysis, error identification, co-simulation, knowledge extraction, and optimization. The paper outlines potential applications of LLMs in BEM, including the use of prompt engineering, retrieval-augmented generation (RAG), and multi-agent LLMs to improve performance and reduce engineering efforts. The study presents three case studies demonstrating the effectiveness of LLMs in BEM. The first case study shows how LLMs can generate and modify EnergyPlus input files (IDF) with improved accuracy through prompt engineering. The second case study explores the use of LLMs in visualizing simulation outputs, demonstrating their ability to generate accurate plots with minimal user input. The third case study illustrates how LLMs can enhance the educational experience by transforming existing knowledge bases into interactive learning platforms using RAG, making BEMcyclopedia more accessible and user-friendly. The results indicate that LLMs can significantly improve the efficiency and effectiveness of BEM processes. However, challenges such as high computational demands, self-consistency issues, and the need for fine-tuning remain. The study emphasizes the importance of selecting the appropriate LLM techniques for specific tasks, such as using RAG for tasks requiring external knowledge and multi-agent LLMs for complex, multi-step processes. Future research should focus on addressing these challenges and exploring the development of domain-specific LLMs tailored for BEM, such as "BEMGPT," to further enhance the application of LLMs in sustainable building practices. The integration of LLMs into BEM holds significant promise for advancing energy-efficient and sustainable building designs.This paper explores the integration of large language models (LLMs) into building energy modeling (BEM) to enhance efficiency, accessibility, and performance. The study highlights the transformative potential of LLMs in addressing the challenges of BEM, which traditionally requires extensive expertise and specialized knowledge. LLMs can streamline BEM processes by automating tasks such as simulation input generation, output analysis, error identification, co-simulation, knowledge extraction, and optimization. The paper outlines potential applications of LLMs in BEM, including the use of prompt engineering, retrieval-augmented generation (RAG), and multi-agent LLMs to improve performance and reduce engineering efforts. The study presents three case studies demonstrating the effectiveness of LLMs in BEM. The first case study shows how LLMs can generate and modify EnergyPlus input files (IDF) with improved accuracy through prompt engineering. The second case study explores the use of LLMs in visualizing simulation outputs, demonstrating their ability to generate accurate plots with minimal user input. The third case study illustrates how LLMs can enhance the educational experience by transforming existing knowledge bases into interactive learning platforms using RAG, making BEMcyclopedia more accessible and user-friendly. The results indicate that LLMs can significantly improve the efficiency and effectiveness of BEM processes. However, challenges such as high computational demands, self-consistency issues, and the need for fine-tuning remain. The study emphasizes the importance of selecting the appropriate LLM techniques for specific tasks, such as using RAG for tasks requiring external knowledge and multi-agent LLMs for complex, multi-step processes. Future research should focus on addressing these challenges and exploring the development of domain-specific LLMs tailored for BEM, such as "BEMGPT," to further enhance the application of LLMs in sustainable building practices. The integration of LLMs into BEM holds significant promise for advancing energy-efficient and sustainable building designs.