An Illustrated Guide to AI Agents

Chapter 3. Reasoning Large Language Models

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LLMs such as DeepSeek-R1, OpenAI GPT-5, and Google Gemini are prime examples of how LLMs can be scaled to new heights through reasoning frameworks. Reasoning in LLMs attempts to mimic human thinking by generating thoughts through tokens before giving a final answer. As shown in Figure 3-1, these tokens explain LLMs’ chain-of-thought and allow reasoning LLMs to break down a problem into smaller steps (often called reasoning steps or thought processes).

Figure 3-1. The differences between a non-reasoning and reasoning LLM. Note that although we expect reasoning LLMs to perform better, they can still arrive at the same conclusions.

Reasoning LLMs first “think” and generate intermediate information before finally answering the query.

Interestingly, the differences between non-reasoning and reasoning LLMs can be viewed through the lens of human cognition, ...