Klu has developed QUAKE, a private benchmark designed to evaluate the real-world capabilities of large language models (LLMs) in practical tasks that an average college-educated person might encounter, such as content creation, data analysis, and customer support. Despite impressive results on standardized benchmarks, these models struggle with real-world applications, averaging only a 28% success rate on QUAKE tasks. The findings highlight the substantial gap between benchmark performance and practical utility, emphasizing the importance of prompt engineering and targeted optimizations to enhance model effectiveness. Current benchmarks do not accurately reflect the challenges faced in commercial applications, and the study suggests that significant model refinement is necessary for LLMs to become reliable and monetizable tools. Additionally, the study anticipates future improvements in LLM performance, with a potential release of a more advanced GPT-5 model, while underscoring the need for new evaluations that better capture real-world use cases.