Large language models (LLMs) like GPT-5 are becoming increasingly complex, but small language models (SLMs) are emerging as viable alternatives for enterprise applications. SLMs, which have fewer parameters, can be more cost-effective, quicker to train, and easier to customize for specific tasks. They outperform LLMs in domain-specific accuracy and adaptability, making them attractive for companies needing rapid deployment. While LLMs remain crucial for complex tasks, a mixed model approach could be the future for enterprises.