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AI Quick Tips 178: Reasoning models

AI Quick Tips

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.

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