TL;DR: What is a reasoning model in AI? It is a large language model trained to work through a problem step by step, often in a hidden “chain of thought,” before it gives a final answer. Reasoning models such as OpenAI’s o1 series and DeepSeek-R1 are usually trained with reinforcement learning on problems that can be checked automatically, like math and code. They tend to be much better at multi-step problems, but they are slower, use more tokens and cost more per answer than standard chat models.
If you have used ChatGPT, Claude or Gemini recently, you have probably seen a “thinking” indicator or a choice of “reasoning effort.” That is a reasoning model at work. This guide explains what a reasoning model in AI is, how it differs from a regular chatbot model, how these models are trained, and when it is worth paying for the extra thinking.
Reasoning models in plain English
A standard large language model predicts text one token at a time and starts answering almost immediately. A reasoning model is still a language model, but it has been trained to spend extra tokens “thinking” first: breaking a problem into steps, trying an approach, checking it, and backtracking when something doesn’t add up. Only after that does it write the answer you see.
OpenAI put it this way when it introduced o1 in September 2024: the model “thinks before it answers” and “can produce a long internal chain of thought before responding to the user” (OpenAI, “Learning to reason with LLMs”).
Reasoning model vs standard LLM
| Standard LLM | Reasoning model | |
|---|---|---|
| How it answers | Starts the reply right away | Generates intermediate reasoning first, then answers |
| Best at | Writing, summaries, chat, quick lookups | Math, coding, logic, planning, multi-step analysis |
| Speed | Fast | Slower, sometimes much slower |
| Cost per answer | Lower | Higher, because thinking tokens are usually billed |
| Typical training extra | Instruction tuning and preference training | Large-scale reinforcement learning on checkable problems |
How reasoning models work
1. Chain of thought
The idea grew out of “chain-of-thought prompting,” described in a 2022 paper by Jason Wei and colleagues at Google (arXiv:2201.11903). They showed that when you prompt a model with examples that spell out intermediate steps, it does better on arithmetic and logic problems. A separate 2022 paper by Takeshi Kojima and colleagues (arXiv:2205.11916) found that even a simple instruction like “Let’s think step by step” helped. Reasoning models take that behavior and train it in, so you no longer have to coax it out with prompt engineering.
2. Reinforcement learning on verifiable problems
The big shift came from training. Instead of only copying human-written answers, the model attempts a problem, gets a reward if the final answer is correct, and is nudged toward the reasoning patterns that led to correct answers. This works best where answers can be checked by a program: a math result can be compared with the known answer, and code can be run against test cases.
DeepSeek’s paper on DeepSeek-R1 is the clearest public description. Its R1-Zero model was trained with reinforcement learning, using rewards based only on whether the final answer was correct plus a formatting rule, without first fine-tuning on human reasoning examples. According to the paper, its average pass@1 score on the AIME 2024 math competition rose from 15.6% to 77.9% during training. The authors also report that the model started producing longer answers and spontaneously learned to re-check its work, including an “aha moment” where it began writing “wait” and re-evaluating steps.
3. Test-time compute
Reasoning models also get better when they are allowed to think longer at answer time. OpenAI wrote that o1’s performance “consistently improves with more reinforcement learning (train-time compute) and with more time spent thinking (test-time compute).” That is why many products now offer settings like low, medium and high reasoning effort.
What the gains look like
The clearest example is still OpenAI’s o1 launch data. On the 2024 AIME exams, OpenAI said GPT-4o solved on average 12% of problems, while o1 averaged 74% with a single attempt per problem. Those are OpenAI’s own figures from 2024, and models have moved on a lot since, but the size of the jump is why every major lab now ships reasoning modes.
Common tasks where reasoning models tend to help:
- Multi-step math and word problems
- Debugging code and writing code that must pass tests
- Logic puzzles and constraint problems
- Planning tasks with many steps, like agent workflows
- Analyzing a long document to reach a conclusion rather than just summarizing it
The downsides
- Cost and speed: thinking consumes tokens. A reasoning answer can use many times more tokens than a direct one, and those tokens usually count toward your bill and your context window.
- Overthinking: the DeepSeek-R1 paper notes “instances of excessive reasoning,” or overthinking, on simple questions.
- Still not always right: step-by-step reasoning reduces some errors but does not eliminate hallucinations. A confident chain of reasoning can still reach a wrong answer.
- Hidden reasoning: some providers do not show the raw chain of thought. OpenAI said in 2024 that it chose to show a model-generated summary instead of the raw reasoning for the o1 series.
- Prompt sensitivity: DeepSeek reported that few-shot prompting “consistently degrades” R1’s performance and recommends describing the problem directly.
When should you use a reasoning model?
- Use one when the task has several steps, a right answer that can be checked, or a costly mistake: code, math, data analysis, contract comparisons, complex planning.
- Skip it for quick rewrites, brainstorming, translation, casual chat or simple facts. A standard model is faster and cheaper.
- Turn the effort dial if your tool offers it. Start low and raise it only when answers are wrong.
- Keep prompts simple. State the problem, the constraints and the output format. You generally don’t need to tell a reasoning model to “think step by step.”
Want to go deeper?
For readers who code, AI researcher Sebastian Raschka recently published a free video course, “Building Reasoning Models from the Ground Up”, which walks through evaluation, inference-time scaling and reinforcement learning in PyTorch. The DeepSeek-R1 paper is also readable for a technical audience and explains the training pipeline in detail.
The bottom line
A reasoning model is a language model trained to think before it speaks. That makes it far stronger on problems with many steps and checkable answers, at the price of more time and more tokens. Knowing when to switch it on, and when a standard model is enough, is one of the easiest ways to get better results from AI tools without overspending.
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