Which Do You Need: Reasoning Model or Non-Reasoning Model?

Choosing the right AI model can make a huge difference in getting the results you want.

Currently, there are two main types of models: reasoning models and non-reasoning GPT models. And understanding which one fits your needs will save you a lot of time, money, and headaches.

That’s because each type of model has its own strengths, and the best choice for you depends on what matters most to you.

If you’re not sure which one you need, here are 4 questions you can ask yourself.

TLDR

• The goal: Figure out if you need a reasoning or non-reasoning model
• The tactic: Ask yourself 4 questions to drive your decision
• The result: Know which type of model you need

Reasoning models vs non-reasoning GPT models

If you’re trying to figure out whether you need a reasoning model or a non-reasoning model, one isn’t necessarily better than the other.

They’re just different.

And as a result, they carry out their task differently.

Reasoning models

Reasoning models think through problems, connect ideas, and make a logical decision based on its information and the context you provide.

Generally speaking, they’re useful for tasks that require:

• Deep problem-solving (e.g. calculating a math problem)
• Logical reasoning (e.g. optimizing and debugging code)
• Multi-step thinking (e.g. developing a strategy)

Examples: o1 & o3

GPT models

Non-reasoning GPT models focus on providing a quick, simple response. They rely on patterns in data, but they don’t analyze deeply or reason through problems.

Some tasks they’re useful for are:

• Fast responses (e.g. addressing a question to customer support)
• Simple tasks (e.g. creating the answer to a frequently asked question)
• Creative content (e.g. writing straightforward marketing copy)

Examples: GPT-4o & GPT-4.5

Question 1: What are your speed and cost requirements?

One of the biggest factors in choosing is how fast you need results and how much you’re willing to spend.

GPT models are faster and tend to cost less.

That’s because non-reasoning models prioritize speed and efficiency over careful analysis.  

By comparison, reasoning models take extra time to think longer and harder about each query. This makes them useful for intensive work, but it also means they’re slow to generate a response and they use more computing power (which increases cost).

Question 2: What task are you trying to execute?

While cost and speed are big factors, the task you want to complete also has a large impact on your choice. Some tasks demand deep thought, while others just need a quick pattern match.

GPT models are better for clear tasks with even clearer requirements.

If your task is straightforward and doesn’t require deep reasoning, like categorizing emails or writing product descriptions, then you’re likely better off with a GPT model. That way you also reap the benefits of speed and cost efficiency.

But if your task requires multiple steps to process information, you’ll probably want a reasoning model. A few examples might be data analysis or making product recommendations using a set of criteria.

Question 3: What degree of accuracy do you need?

Not every task requires perfect accuracy, especially when people start using the ‘lack of errors’ as a marker of AI content. For some tasks, you can get by with quick, approximate answers while others need greater precision.

The trick is creating guardrails that prevent major problems or hallucinations.

Reasoning models are more reliable at making decisions.

By design, reasoning models carefully look at the problem at hand, and while creating an answer, they look for ways to reduce mistakes and improve accuracy. While these extra steps ensure greater accuracy, they consume more time and resources.

Consequently, reasoning models are best when:

• Errors cause problems
• Fact-checking is essential
• Answers must be logical

GPT models generate answers based on patterns, which means they don’t usually analyze the “correctness” of their response.

In this situation, GPT models work well when:

• Minor mistakes don’t matter (e.g. social media captions, brainstorming ideas)
• Speed is more important than precision (e.g. text auto-completion, email summarization)
• You can double-check the output (e.g. blog content, sales copy)

Question 4: How complex is your task?

Last (but not least), if you haven’t figured out which model you’ll need, ask yourself about its complexity or ambiguity.

Reasoning models are better at working through ambiguity and complexity.

This proves useful if you’re trying to find the proverbial needle in a haystack. That’s because reasoning models can look at large amounts of unstructured information and pull out the most relevant insights to answer your question. Even if it’s an information-dense document like a contract or a business industry report.

Reasoning models also shine at taking limited, disconnected pieces of information and figuring out your intent. They may even ask clarifying questions before providing you an answer.

But if your task is straightforward and requires little to no deep thought, a non-reasoning GPT model is usually enough. And it will save you time and money.

Result

Unless you’re a specialist in a specific field, such as a marketing copywriter, chances are you won’t rely solely on one type of model.

As a rule of thumb, you’ll want to use:

• Reasoning models for making decisions and planning steps
• GPT models for executing specific tasks

Here are a few examples of how you might employ each model within the same company role:

The challenge is that you can’t use the same prompts for both types of models.

While you might prefer (and get away with) using context-rich “Boomer” prompts with GPT models, especially if you’re carrying out a creative task, you’ll want to write prompts super simple and direct with reasoning models. 

For even more clarity, you’re recommended to use delimiters like markdown, XML tags, and section titles to make it easier for a model to read and reason through your prompt.

This is the kind of stuff our GTM engineering team does every day to make sure AiSDR creates sales emails that convert your leads into demos.