Large language models (LLMs) like GPT-4, Claude, and LLaMA are crucial to the effectiveness of voice agents, with each offering distinct advantages and challenges. GPT-4 is known for its deep reasoning, accuracy across languages, and safety features, making it ideal for complex tasks in compliance-sensitive environments, though it suffers from higher latency and costs. Claude excels in conversational speed and handling interruptions, making it suitable for customer service and real-time translation, although it may struggle with complex reasoning. LLaMA provides unmatched control and cost efficiency for teams with the infrastructure to host and fine-tune, though it requires significant setup and may lack advanced reasoning capabilities. The choice of LLM depends on the specific needs of the voice agent application, such as speed, reasoning, multilingual support, and cost considerations, while orchestration and integration with other systems like speech-to-text and text-to-speech are also critical for optimal performance.