An AI reasoning model is a type of large language model that works through a problem step-by-step before giving its final answer, rather than responding immediately. This makes reasoning models substantially more accurate on complex tasks — mathematics, code, logic, and multi-step planning — but also slower and more expensive than standard chat models.

How reasoning models work differently

Standard AI chat models generate responses token by token in a single pass. When you ask one to solve a hard math problem, it jumps straight to an answer — which is why even capable models sometimes get complex arithmetic wrong.

Reasoning models take a different approach. Before the visible reply appears, the model generates a hidden chain of “thinking tokens”: it works through the problem, checks its logic, and sometimes backtracks to reconsider. This technique builds on chain-of-thought prompting, which researchers found could dramatically improve AI accuracy on complex tasks. The extra computation happens at inference time — when the model is running — so it can be scaled up or down depending on how hard the problem is.

In practice, this thinking process can take anywhere from a few seconds to over a minute, and the thinking tokens that power it appear on your usage bill even though they don’t appear in the chat window.

What they excel at

Reasoning models significantly outperform standard models on tasks that require keeping many facts in mind or verifying logic at each step:

  • Mathematics and science — multi-step calculations, proofs, graduate-level science problems
  • Code — writing complex algorithms, debugging tricky logic, reviewing large codebases
  • Analysis and planning — breaking down a lengthy document, designing a project plan, evaluating tradeoffs
  • Logic puzzles — tasks with a clearly right or wrong answer where several constraints must all hold at once

On these task types, reasoning models can improve accuracy by 10–30 percentage points over standard models.

When a standard model is the better choice

For quick, everyday tasks — drafting an email, summarizing a paragraph, answering a simple question — a standard model is faster, cheaper, and equally accurate. Spending reasoning compute on a task that needs no reasoning is wasteful. Most AI platforms now let you choose: pick a standard model for simple work and switch to a reasoning model when the task genuinely demands it.

Which reasoning models can you use today

Several major AI labs now offer reasoning models:

  • OpenAI o-series (o3, o4-mini) — OpenAI’s dedicated reasoning line. Available on ChatGPT Plus and via the OpenAI API.
  • Claude Extended Thinking — Anthropic’s models support an extended thinking mode that can make the reasoning chain visible. Full details in Anthropic’s documentation.
  • Google Gemini Thinking — Google offers reasoning-focused variants of its Gemini models, including a Flash Thinking version optimized for cost.
  • DeepSeek R1 — An open-source reasoning model from the Chinese AI lab DeepSeek, available to run locally or via API.

As of mid-2026, reasoning capabilities have become a standard feature of frontier AI models rather than a niche add-on.

What does it cost?

Reasoning models cost more than standard models because they generate additional thinking tokens. Pricing varies across providers and is evolving quickly — check each provider’s current pricing page before building anything that depends on a specific cost. OpenAI publishes its API rates at platform.openai.com/docs/pricing. For most consumers, the cost is invisible: a ChatGPT Plus or Claude Pro subscription includes access to reasoning models at a flat monthly fee.

In the news

OpenAI this week released GPT-5.6 in a government-gated preview, introducing three new sub-models — Sol, Terra, and Luna — that extend the company’s reasoning capabilities to specialized government applications. See our full report for details.

FAQ

What is the difference between a reasoning model and a regular AI model?
A standard model generates its answer in one pass. A reasoning model first produces hidden intermediate steps — checking logic, exploring alternatives — before committing to a final answer. This makes it more accurate on hard, multi-step problems.

Can I use reasoning mode on ChatGPT?
Yes. ChatGPT Plus subscribers can select OpenAI’s o3 or o4-mini reasoning models from the model picker in the chat interface.

Does reasoning always give a better answer?
No. For simple tasks — a quick summary, a factual lookup — a standard model is faster and equally good. Reasoning models shine when the problem genuinely requires working through multiple steps or checking for mistakes.

Are the thinking steps private?
OpenAI does not show users its thinking tokens. Anthropic’s Claude can display its reasoning chain, giving you a window into how the model reached its conclusion.

Sources: Chain-of-Thought Prompting (Wikipedia) · OpenAI Reasoning Models Guide · Anthropic Extended Thinking Docs