---
title: "AI Quick Tips 178: Reasoning models"
description: Learn about reasoning models in AI, their benefits, drawbacks, and when to use them for complex tasks requiring logical steps and structured decision-making.
---

[Thoughts Brewing Blog](https://thoughtsbrewing.com/blog)

# [AI Quick Tips 178: Reasoning models](https://thoughtsbrewing.com/blog/ai-quick-tips-178-reasoning-models)

 Written by [Damien Griffin](https://thoughtsbrewing.com/blog/author/damien) | May 31, 2025, 3:45:00 PM

Reasoning models are trained and tuned for tasks requiring, well, *reasoning*.  Not helpful, I know.  

They do well with tasks that require logic and/or problems that are complex.  In this case, complex means having multiple steps.

Most of the major AI chatbot tools have at least one reasoning model - ChatGPT, Deepseek, Gemini, etc.

The reasoning models break down problems into logical steps and then work through the steps one at a time.  Chain-of-thought, if you’re familiar with the term.  Some of the models will show you this process and wait for your approval, and some hide the process from you.  

 

## Pros of reasoning models 

- They approach problems more like humans by breaking them down, working through them one step at a time, and exploring alternatives.
- They do better with logic problems than general-purpose models: 
    - Math
    - Coding
    - Structured decision making
    - Etc
- They do better with complex problems.
- They sometimes give you a breakdown of how they are working through a problem.

 

## Cons of reasoning models 

- They take longer to generate responses
- They cost more 
    - They use extra “thinking” tokens that will get added to your output token costs.
- They use more resources 
    - GPU
    - Memory
    - Etc
- They are less effective at smaller, simpler tasks - they “overthink” and can hallucinate more. 
    - Single answer
    - Single step
    - Etc

Reasoning models do a really good job if they are given the right kind of problems to work on.  If you aren’t sure if it is the right kind of problem, then start with a general-purpose model, and if the answers aren’t good enough, try a reasoning model.

[View full post](https://thoughtsbrewing.com/blog/ai-quick-tips-178-reasoning-models)

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