Spoken Language Understanding (SLU) is crucial for enabling natural voice interactions with modern devices. However, traditional supervised models fail to generalize to new domains due to two key challenges: the prohibitive cost of data annotation and the inherent difficulty of transferring domain-specific intents. While the rise of Large Language Models (LLMs) offers a promising solution through zero-shot inference, the zero-shot SLU capabilities of emerging speech-enabled LLMs have remained largely unexplored. To address this gap, this paper provides the first comprehensive assessment, focusing on intent classification (IC), the first key sub-task of SLU, across 13 languages. We systematically evaluate a range of architectures, including cascaded, end-to-end, and hybrid systems for zero-shot SLU. Our analysis identifies the hybrid approach as the most effective architectural design for end-to-end SLU, and assesses multilingual transfer capabilities. The findings offer a detailed map of the challenges and opportunities, highlighting which models and settings are most promising for zero-shot SLU.