Why "Thinking" Matters
When you ask a friend to solve a tricky math problem, they do not just blurt out an answer. They work through it step by step, maybe scribbling on a napkin, crossing things out, trying a different approach. That process of working through a problem is what we call reasoning.
For most of AI's history, language models did not reason. They predicted the next word based on patterns they had seen in training data. If you asked "What is 47 times 83?" the model would try to pattern-match its way to an answer rather than actually multiplying. Sometimes it got lucky. Often it did not.
Reasoning models changed that. They introduced a way for AI to slow down, think step by step, and arrive at answers through a structured process rather than pure pattern recognition. This chapter explains how that works, why it matters, and what it costs.
What "Reasoning" Means for AI
Let us be precise about what reasoning means here, because the word carries a lot of weight. When researchers say an AI model can "reason," they do not mean it has consciousness or understanding the way a human does. They mean the model can break a complex problem into smaller steps, work through those steps in sequence, and arrive at a conclusion that follows logically from the steps before it.
Think of it like the difference between a student who memorizes answers to practice tests and one who learns the underlying method. The memorizer might do well on questions they have seen before, but struggle with new variations. The student who learned the method can adapt.
Traditional language models were more like the memorizer. Reasoning models are more like the methodical student. They do not truly "understand" the way a human does, but they can apply structured thinking in ways that produce dramatically better results on complex problems.
Chain-of-Thought Prompting: The Breakthrough
The story of reasoning in AI starts with a surprisingly simple idea called chain-of-thought prompting, published by researchers at Google in 2022.
The concept is straightforward. Instead of asking a model to jump straight to an answer, you ask it to show its work. You might add the phrase "Let's think step by step" to your prompt, or you might show it an example of a problem being solved with intermediate steps.
Here is a classic illustration. Suppose you ask a standard language model:
"A store sells apples for $2 each. Maria buys 3 apples and pays with a $10 bill. How much change does she get?"
A standard model might get this right because it is simple enough. But make the problem slightly more complex, and things fall apart. Add a discount, a tax rate, and a buy-one-get-one deal, and the model starts guessing.
With chain-of-thought prompting, you encourage the model to write out each step:
- Step 1: Calculate the cost of 3 apples at $2 each = $6
- Step 2: Maria pays with $10
- Step 3: Change = $10 - $6 = $4
This might seem trivial for a simple problem, but the technique scales. When applied to complex multi-step problems in math, logic, coding, and science, chain-of-thought prompting improved accuracy dramatically. In some benchmarks, it doubled or tripled the model's performance on reasoning tasks.
The key insight was that language models are better at producing correct final answers when they generate the intermediate steps first. The act of "writing out their thinking" helps the model stay on track, much like how you might talk yourself through a complicated recipe rather than trying to remember all the steps at once.
OpenAI's o1 and o3: Purpose-Built Reasoning
Chain-of-thought prompting was a technique anyone could use with any language model. But what if you built a model specifically designed to reason? That is the idea behind OpenAI's o1 family of models, released starting in late 2024, and later the o3 series.
These models are fundamentally different from standard chatbots like ChatGPT's default mode. When you send a question to an o1 or o3 model, something interesting happens: the model pauses. Instead of immediately streaming a response, it enters what OpenAI calls an "extended thinking" phase. During this phase, the model is generating a long internal chain of thought, working through the problem before presenting you with a polished answer.
You can sometimes see a summary of this thinking process, labeled something like "Thinking for 30 seconds..." in the interface. Behind the scenes, the model might generate hundreds or even thousands of words of internal reasoning before producing its final response.
The results are striking. On graduate-level science questions, competitive programming problems, and advanced mathematics, o1 and o3 models significantly outperformed their predecessors. In some cases, these models achieved scores comparable to PhD students on domain-specific tests.
But how do they actually work? While OpenAI has not published the full technical details, the general approach involves training the model with reinforcement learning to develop better reasoning strategies. The model learns that certain thinking patterns lead to correct answers and is rewarded for using them. Over time, it develops what you might call "thinking habits" — structured approaches to breaking down problems.
Tree of Thought: Exploring Multiple Paths
Chain-of-thought prompting follows a single line of reasoning from start to finish. But what if the model could explore multiple possible approaches simultaneously, like a chess player considering several different moves before choosing the best one?
This is the idea behind tree-of-thought reasoning. Instead of following one path from problem to solution, the model branches out, exploring several possible approaches in parallel. At each branching point, it evaluates which paths seem most promising and focuses its effort there.
Imagine you are trying to plan a road trip from New York to Los Angeles. A chain-of-thought approach would be like picking one route and following it. A tree-of-thought approach would be like sketching out three or four possible routes, evaluating each one for distance, scenery, and road conditions, and then choosing the best option.
In practice, tree-of-thought reasoning allows models to:
- Backtrack when they hit dead ends. If a line of reasoning is not working, the model can abandon it and try a different approach, rather than stubbornly pushing forward with a flawed strategy.
- Compare alternatives. By exploring multiple paths, the model can weigh different solutions against each other and pick the strongest one.
- Handle ambiguity better. When a problem could be interpreted in multiple ways, the model can explore each interpretation and see which one leads to a coherent answer.
This technique is particularly powerful for creative tasks, strategic planning, and any problem where the first approach you try might not be the best one.
How Reasoning Models Think Step by Step
To make reasoning models more concrete, let us walk through an example of what happens when you ask a reasoning model a complex question.
Suppose you ask: "My small business made $500,000 in revenue last year. We spent $200,000 on salaries, $80,000 on rent, $50,000 on materials, and $30,000 on marketing. We are considering hiring a new employee at $60,000 per year. Can we afford it, and what would our profit margin look like?"
A standard language model might give you a quick, roughly correct answer. A reasoning model would work through it methodically:
Internal reasoning (simplified):
- First, let me calculate current expenses: $200,000 + $80,000 + $50,000 + $30,000 = $360,000
- Current profit: $500,000 - $360,000 = $140,000
- Current profit margin: $140,000 / $500,000 = 28%
- With new hire: Total expenses would be $360,000 + $60,000 = $420,000
- New profit: $500,000 - $420,000 = $80,000
- New profit margin: $80,000 / $500,000 = 16%
- The business can technically afford it (still profitable), but the profit margin drops from 28% to 16%, which is significant.
- I should also consider whether this hire might increase revenue, since that context matters for the decision.
Final answer: The model then presents a clear, well-organized response incorporating all of these calculations, along with caveats about whether the new hire might generate additional revenue.
The key difference is reliability. The reasoning model is much less likely to make arithmetic errors or skip important considerations because it is forced to work through the problem explicitly rather than trying to jump to the end.
When Reasoning Helps vs When It Hurts
Reasoning models are not always the right tool. Like a power drill, they are fantastic for certain jobs and overkill for others.
Where Reasoning Models Shine
Complex math and logic problems. Any task that requires multiple steps of calculation or logical deduction benefits enormously from structured reasoning. This includes everything from tax calculations to scientific analysis.
Coding and debugging. Writing code requires thinking through data flow, edge cases, and how different parts of a program interact. Reasoning models are significantly better at producing correct, well-structured code, especially for complex programming challenges.
Strategic analysis. Questions like "Should our company expand into this new market?" require weighing multiple factors, considering tradeoffs, and thinking through consequences. Reasoning models handle these more thoughtfully.
Standardized tests and academic problems. On benchmarks that test graduate-level reasoning in science, math, and law, reasoning models dramatically outperform standard models.
Where Reasoning Can Hurt
Simple questions. If someone asks "What is the capital of France?" a reasoning model that spends thirty seconds thinking about it is just wasting time and money. The standard model can answer instantly and correctly.
Creative writing. When you want a poem, a story, or a creative brainstorm, the structured step-by-step approach of reasoning models can actually make the output feel stiff and formulaic. Creative tasks often benefit from the more fluid, associative thinking of standard models.
Casual conversation. For everyday chatbot interactions — customer service, general knowledge questions, friendly banter — reasoning models are unnecessarily heavy.
Time-sensitive tasks. If you need an answer in milliseconds for a real-time application, the "pause and think" approach of reasoning models introduces latency that might be unacceptable.
The practical advice is simple: match the tool to the task. Use reasoning models for hard problems where accuracy matters. Use standard models for everything else.
The Cost of Reasoning: More Tokens, More Money
Here is something that often gets overlooked in breathless headlines about AI reasoning breakthroughs: reasoning costs significantly more money.
To understand why, you need to know how AI pricing works. Most AI providers charge by the "token," which is roughly a word or piece of a word. You pay for both the tokens you send to the model (your prompt) and the tokens the model generates (its response).
When a reasoning model "thinks," it is generating tokens during that thinking phase. Even though you might not see all of those internal reasoning tokens, they still count. A standard model might generate 200 tokens to answer a question. A reasoning model might generate 2,000 tokens of internal reasoning plus 200 tokens of final answer, meaning you are paying for 2,200 tokens total — roughly ten times more.
Let us put real numbers on this. As of early 2026, using a standard AI model for a typical business task might cost a fraction of a cent per query. Using a reasoning model for the same task might cost several cents per query. That does not sound like much, but at scale it adds up fast. A company processing 100,000 customer queries per day would see their AI costs multiply dramatically if they switched everything to reasoning models.
This is why the "when to use reasoning" question matters so much in practice. Smart AI deployment means routing easy questions to cheaper, faster standard models and only using expensive reasoning models for tasks that genuinely need them.
The Speed Tradeoff
Cost is not the only consideration. Reasoning models are also slower. That thinking phase takes real time — sometimes seconds, sometimes over a minute for very complex problems. In applications where users expect instant responses, this lag can be a dealbreaker.
Some companies address this by running reasoning in the background. For instance, a coding assistant might use a fast standard model for simple autocomplete suggestions but switch to a reasoning model when you ask it to architect an entire system or debug a complex issue.
The Reasoning Spectrum
It is helpful to think of AI reasoning as a spectrum rather than an on/off switch.
At one end, you have pure pattern matching — the model just predicts the next most likely word based on its training. This is fast, cheap, and surprisingly effective for many tasks.
In the middle, you have chain-of-thought prompting with standard models. You can coax any language model into showing its work, and this often improves results without requiring a specialized reasoning model.
Further along, you have purpose-built reasoning models like o1 and o3, which are trained to reason extensively and deeply.
At the far end, you have emerging approaches that combine reasoning with tools — models that can not only think step by step but also run calculations, search for information, and verify their own work as they go.
The trend in the field is clearly moving toward more sophisticated reasoning. Each new generation of models gets better at structured thinking, and the techniques developed for reasoning are being incorporated into standard models as well. What was cutting-edge reasoning capability two years ago is now a baseline expectation.
What This Means for Everyday Users
If you use AI tools regularly, reasoning models affect you in several practical ways.
Choosing the right model matters more than ever. Many AI platforms now offer a choice between standard and reasoning models. Understanding when each is appropriate helps you get better results and avoid unnecessary costs.
Prompt design still matters. Even with reasoning models, how you frame your question influences the quality of the answer. Being specific about what you need, providing relevant context, and breaking complex requests into clear sub-questions all help.
Expect pricing tiers to expand. As reasoning capabilities improve, AI providers are offering more granular pricing tiers. You might see options like "light reasoning" for moderately complex tasks and "deep reasoning" for the hardest problems, each at different price points.
Verification remains important. Reasoning models are much more reliable than their predecessors, but they are not infallible. They can still make errors, especially on problems that require knowledge they were not trained on. Always verify important results, particularly for financial, legal, or medical decisions.
The Road Ahead
Reasoning in AI is one of the fastest-moving areas of research. Several trends are worth watching.
Reasoning is getting cheaper. Through a combination of more efficient architectures, better training techniques, and hardware improvements, the cost of AI reasoning is dropping steadily. Tasks that were prohibitively expensive a year ago are now affordable for many applications.
Reasoning is merging with other capabilities. The line between "reasoning model" and "standard model" is blurring. Major AI labs are incorporating reasoning capabilities into their general-purpose models, so you get better thinking without needing to explicitly choose a reasoning mode.
Self-verification is emerging. Some of the most exciting work involves models that can check their own reasoning, catch their own mistakes, and correct course. This moves us closer to AI systems you can trust for high-stakes decisions.
The bottom line is this: reasoning models represent a genuine step forward in what AI can do. They are not magic, and they come with real costs and limitations, but for complex problems that require structured thinking, they deliver results that would have seemed impossible just a few years ago.
See This in the News
The competition between reasoning models is one of the hottest stories in AI. To see how the leading frontier models stack up against each other, including their reasoning capabilities, read Claude 4 vs GPT-5: Frontier Models Compared on AIWire.