In a groundbreaking study named 'SimpleBench', researchers sought to fairly evaluate Large Language Models (LLMs) by standardizing the prompts used across all models. The approach involved instructing LLMs to choose the most realistic answer through a step-by-step method known as Chain of Thought (COT). Additionally, the study included benchmarks using specifically engineered prompts for selected models. This investigation revealed that while custom prompts showed modest performance enhancements, inherent limitations within the models remain. This suggests that tailored prompts can improve outcomes but might not overcome fundamental deficits in existing LLM architectures. A key revelation of the study was the unexpected underperformance of a specific LLM, GPT4o. Researchers hypothesized that the model's optimization for particular industrial uses, like mathematics and programming, might detract from its overall reasoning capabilities. This observation highlights a trade-off that exists in AI development, where a focus on niche applications could impede the broader applicability and intelligence of a model. The results underscore the importance of balanced optimization to foster versatile AI capabilities. The study's comprehensive findings open new discussions on how AI models are evaluated. Standardization in testing is imperative to gaining a true understanding of an AI's capabilities and limitations. While prompt engineering offers a pathway for improved AI interactions, it also underscores that certain challenges remain unsolved. For those interested in a detailed exploration of methods and findings, the authors encourage reviewing the full technical report, which delves deeper into the implications and methodology of their work.