Why You Should Care About Reasoning Models

ChatGPT now has 700 million weekly active users. Of those, 10 million are paying Plus subscribers, representing about $2.4B in annual revenue. Roughly 5 million are paying Team or Enterprise business users. ChatGPT processes an estimated 2.5 billion prompts per day, showing how deeply it has integrated into daily work and life.

Here is a surprising statistic. Of paid Plus subscribers, only 7 percent were using reasoning models (the “o-series”) before the GPT-5 launch. Among free users, that number drops to just 1 percent.

Why so few? Most people do not understand what a reasoning model is or why it matters.

How Humans Reason (and How AI Mimics It)

Imagine your car needs new tires and the air conditioning repaired. You have owned it for years and it is paid off. You think through multiple possibilities:

• Should I buy a new car and take on a payment?
• Should I fix it even if repairs cost more than its resale value?
• How would each choice affect my budget and other priorities?
• Do I love this car enough to keep it running?

This mental process of weighing options, trade-offs, and long-term implications is reasoning.

What Reasoning Models Do

Reasoning models are thinking models. When you use one, you can see it step through its logic in a process called chain-of-thought reasoning.

Traditional large language models are trained on massive datasets to predict the next word. Reasoning models are trained with reinforcement learning, which means they:

• Try different approaches
• Identify errors
• Iterate to improve
• Break problems into step-by-step solutions

They do not rely on additional compute or more data to get better. Instead, they improve the more time they have to think at inference time.

Reasoning models can already solve PhD-level math, coding, and science problems. They also excel at decisions with many variables.

Why Businesses Should Care

For organizations, reasoning models are most useful when:

• Complex decisions require weighing multiple trade-offs
• Multiple datasets must be integrated into one decision flow
• Accuracy and transparency are critical in problem solving

Examples:

• Retail forecasting – A reasoning model can analyze historical sales, weather data, promotions, and supply chain constraints to recommend optimal inventory levels.
• Customer support – It can reason through troubleshooting steps while factoring in previous interactions and known product issues.

The better your data infrastructure and the richer your contextual data, the more valuable these models become for business decision-making.

 How Individuals Can Benefit

If you pay for ChatGPT Plus, the reasoning feature is one of the most underused benefits. Here are a few ways to ensure you use it:

• Project planning – Ask it to reason through dependencies, risks, and alternative timelines before committing to a plan.
• Career decisions – Have it compare job offers based on salary, growth potential, culture fit, and commute.
• Financial trade-offs – Use it to reason through “buy vs. rent” or “invest vs. pay down debt” scenarios.

If you already subscribe, not using reasoning models means you are leaving value on the table (FYI, ChatGPT 5 is making the selection for you now).

Bottom line: Reasoning models do not just give you answers. They help you think better. In a world powered by AI, that is a competitive advantage.