Structuring the reasoning in prompts helps the model perform better, even if it's not strictly reasoning as we define it.

Reasoning is expected to be a core capability of future models, and the two paradigms of unsupervised pre-training and specialized reasoning complement each other well.

The ability to allow AI models to reason for extended periods, rather than just providing quick answers, can lead to significantly better outcomes for complex questions.

The emergence of reasoning models is expected to enable AI agents to perform actions on behalf of users, enhancing their reliability and effectiveness in real-world applications.

Chain of thought prompting, where you instruct the model to think step-by-step, is less necessary with modern reasoning models but can still be useful in some cases. If you're running thousands or millions of inputs through your prompt, you'll still need to use classical prompting techniques to make your prompt more robust.

The model will show an interleaved chain of thought, reasoning through the sources it encounters and the tool calls made, such as searching or analyzing with Python.

The reasoning models push the capabilities of GPT models further, allowing for better performance by thinking step by step.

The OER models excel at orchestrating and executing multi-step reasoning tasks, which is crucial for combining various information sources into cohesive work products.

The reasoning model of DeepSeek allows for deeper answers by thinking through the steps of a problem, which was not released with previous models like OpenAI's.