Reasoning Model

What Are Reasoning Models?

A reasoning model is a type of LLM that internally generates an explicit Chain of Thought before producing an answer, solving complex problems such as mathematical proofs and code generation in a step-by-step manner.

While a standard LLM predicts the next token "intuitively," a reasoning model decomposes a problem and reaches a final answer through self-verification of intermediate steps. It can also be seen as a design that embodies the concept of Test-time Compute scaling.

Representative Models

Examples include OpenAI's o series, DeepSeek-R1, and Anthropic Claude's extended thinking mode. All of these tend to achieve higher accuracy the more "thinking time" they are given, and a notable feature is that users can control the trade-off between the number of reasoning tokens and accuracy.

Identifying the Right Use Cases

Reasoning models are not a universal solution. For tasks that require immediate responses—such as simple summarization or translation—standard LLMs are more advantageous in terms of both cost and speed. In the author's experience, using a standard model for internal knowledge Q&A and a reasoning model for contract risk analysis tends to offer better cost-effectiveness.

The first step to adopting reasoning models is to clarify in advance which tasks genuinely require "thinking time."