DeepSeek R1 and OpenAI o3 are advanced thinking models that differ from traditional language models by reasoning internally without relying on explicit Chain-of-Thought (CoT) prompting, which can enhance their performance on complex tasks. To optimize interactions with these models, it is crucial to use minimal and clear prompts, as overloading them with examples or step-by-step guidance can hinder their effectiveness. While these models excel in complex multi-step reasoning, they are less effective for structured outputs, where traditional LLMs may perform better. Techniques like ensembling can improve accuracy for high-stakes tasks, albeit at higher costs, and the Chain-of-Draft (CoD) approach can help reduce token usage while maintaining quality. Ultimately, these models require a distinct prompting strategy that leverages their internal reasoning capabilities to achieve optimal results.