The study compares zero-shot large language models (LLMs) to fine-tuned natural language understanding (NLU) classifiers for intent detection, concluding that the suitability of LLMs varies depending on the specific intent space. On comprehensive datasets like ATIS and CLINC150, the performance of LLMs relative to fine-tuned NLU classifiers depends on the context and type of intents involved.