Reasoning model
Also known as: reasoning models, thinking model, large reasoning model, LRM
A reasoning model is a large language model trained to 'think' before it answers, generating an internal chain of reasoning steps, checking its work, and trying different approaches before giving a final response.
This makes it much better at math, coding, science, and multi-step planning. While reasoning models are more accurate on hard problems, they're slower and more expensive per question, and their visible 'thinking' isn't always a faithful account of how they reached an answer.
Reasoning model, explained at three levels
At home
A regular AI chatbot answers right away, like blurting out the first thought. A reasoning model takes a moment to work through the problem, like doing math on scrap paper first.
At work
Reasoning models are useful for complex analysis, coding, and planning tasks, where accuracy matters more than instant replies.
At home
You may see a 'thinking' indicator while the model works. It generates many hidden steps, which takes longer but often catches mistakes a quick answer would miss.
At work
These models are trained with reinforcement learning on problems with checkable answers, such as math and code tests. Many APIs let you set a 'reasoning effort' or thinking budget. Thinking tokens are usually billed, so costs can be several times higher than a standard response.
At home
The model's displayed reasoning can sound convincing even when the final answer is wrong. In my opinion, it's still wise to check important answers yourself.
At work
Key decisions include routing simple requests to cheaper models, setting thinking budgets, latency tolerance, and evaluating on your own tasks. Research shows chain-of-thought text may not faithfully reflect the model's actual process, which matters for auditing and safety monitoring.
Real-world examples
At home
Nadia's son used a reasoning model to check his calculus homework. It took about 30 seconds to answer, but it explained each step and found the error in his solution.
At work
Thomas is a data analyst at a Regina utility. He uses a reasoning model for complicated rate calculations and a faster standard model for routine email drafts, keeping costs in check.
How it compares
| feature | Reasoning model | Standard language model |
|---|---|---|
| Response time | Seconds to minutes | Near-instant |
| Hard math and code | Strong | Weaker |
| Cost per answer | Higher | Lower |
| Best for | Complex, multi-step problems | Simple questions, writing, chat |
Reasoning models trade speed and cost for accuracy on hard problems.
For analysts
I'd use reasoning models selectively, for tasks where mistakes are expensive. Routing everyday requests to faster models keeps both costs and wait times down.
History & origin
Researchers at Google showed in 2022 that prompting models to write out steps, known as chain-of-thought prompting, improved reasoning. OpenAI released o1-preview in September 2024, the first widely used model trained with reinforcement learning to reason at length before answering. In January 2025, China's DeepSeek released R1, an open-weight reasoning model that rivalled o1 at much lower reported cost, contributing to a one-day drop of roughly 17 percent in Nvidia's share price. Anthropic, Google, and others added 'extended thinking' modes to their models through 2025, and many models now switch between fast and deliberate modes automatically. In my view, reasoning models marked the shift from AI that sounds right to AI that more often is right, though not always.