A reasoning model is an AI language model that works through a problem step by step before it gives its final answer. Ask a standard chatbot "Which of these three suppliers is cheapest after shipping and tax?" and it may answer instantly. A reasoning model will first break the question into parts, do the math for each supplier, check its work and then reply.
For businesses, reasoning models matter because they power many of the most capable AI tools, including research agents that run many web searches and compare sources before answering. When those tools decide which businesses to recommend, they weigh evidence more deliberately. Clear, consistent and verifiable information on your site gives them more to work with.
What is a reasoning model?
A reasoning model is a type of large language model trained to "think" before it responds. OpenAI introduced the idea to a wide audience with its o1 model on September 12, 2024. In its announcement, OpenAI said o1 was trained with reinforcement learning to produce a long internal chain of thought, learning to break hard problems into simpler steps, catch its own mistakes and try a different approach when one isn't working.
Other AI companies offer similar capabilities under different names. Anthropic calls it extended thinking or adaptive thinking in Claude. Google offers thinking models in Gemini. Many current flagship models can switch between quick answers and deeper reasoning depending on the task.
Reasoning models tend to help most with:
- Math, logic and multi-step calculations
- Coding and debugging
- Analysis that compares many pieces of information
- Long, multi-step agent tasks, such as research across many web pages
Why reasoning models matter
They drive deeper AI research tools. OpenAI's deep research agent launched in February 2025 powered by a version of the o3 reasoning model optimized for web browsing and data analysis. Google's Deep Search in AI Mode, announced at Google I/O in May 2025 and rolled out in July 2025, runs hundreds of searches and reasons across the results to build a cited report. These tools read far more sources per question than a typical person would.
They look harder at the evidence. Reasoning models are designed to plan, check intermediate results and abandon approaches that don't hold up. Applied to research, that means comparing claims across sources rather than taking the first one found. For your business, that means vague marketing language is less useful than specific facts: prices, service areas, credentials, policies and dated information.
They change how AI searches. AI search tools often break one question into many smaller searches, a process called query fan-out, and research tools built on reasoning models take this further. A buyer's single question can lead the AI to look up reviews, comparisons, pricing and specific features. Pages that answer those smaller questions directly have more chances to be found and cited.
How reasoning models work
Thinking before answering
When a reasoning model gets a prompt, it first generates internal reasoning, sometimes called a chain of thought. OpenAI's documentation describes these as reasoning tokens that the model uses to plan, weigh alternatives and work through multi-step tasks before writing a response. Anthropic describes Claude restating the question, trying approaches, checking intermediate results and abandoning paths that don't hold up.
Hidden or summarized reasoning
Users usually don't see the raw reasoning. OpenAI said at o1's launch that it chose not to show the raw chain of thought and shows a model-generated summary instead. OpenAI's API documentation says reasoning tokens are not visible through the API. Anthropic's documentation says the text in Claude's thinking blocks is a summary of its reasoning.
More thinking costs more
Reasoning takes time and tokens. Both OpenAI and Anthropic bill reasoning tokens as output tokens, even when the reasoning text isn't returned. Reasoning also uses space in the model's context window. Developers can usually set an effort level: lower effort for speed and cost, higher effort for harder problems.
Test-time compute
OpenAI reported that o1's performance improved both with more training and with more time spent thinking when answering. This idea, often called test-time compute, is why reasoning models can give better answers on hard problems by thinking longer.
How to make your content useful to reasoning models
- State facts plainly. Put prices, service areas, hours, guarantees and qualifications in clear text that can be checked, not only in images or vague phrases.
- Keep facts consistent everywhere. A reasoning model comparing your website, Google Business Profile and directory listings may notice conflicts. Fix mismatches.
- Answer the sub-questions. Think about the follow-up questions a buyer would ask, such as cost, timeline, alternatives and who it's for, and answer each one on the relevant page.
- Show your evidence. Cite sources, include dates and explain how you reached claims. Reasoning models are built to weigh evidence.
- Make comparisons honest. If you publish comparison pages, be fair and specific. One-sided pages are easy for a careful model to discount.
- Keep pages crawlable. Reasoning only helps if the AI can reach your content. Make sure AI crawlers and search engines can access and index your key pages.
Common mistakes and misconceptions
- "Reasoning models don't make mistakes." They make fewer errors on some tasks, but they can still produce AI hallucinations. Verify important outputs.
- "You can see exactly how the model decided." Most providers show a summary of the reasoning, not the full process. Treat summaries as a helpful view, not a complete record.
- "Always use the most reasoning possible." Higher effort is slower and costs more. Simple tasks like rewriting an email rarely need it.
- "Reasoning models need special SEO tricks." There is no separate optimization playbook. Clear, accurate, well-structured content that is easy to verify serves both people and models.
Reasoning model vs. standard LLM
A standard large language model generates its answer directly, one token after another, without a separate planning phase. It is fast and works well for writing, summarizing and simple questions. A reasoning model adds a thinking phase first, which makes it slower and more expensive but better at multi-step problems. The line is blurring: many current models can do both and decide how much to think based on the task or a setting chosen by the developer.
How Butter approaches reasoning models
At Butter, we don't treat reasoning models as a separate channel with its own tricks. We treat them as a reason to get the fundamentals right. In our monthly AI prompt testing, we include the kinds of detailed, multi-part questions that research tools and reasoning models handle, and we note which sources the answers rely on.
From there, we recommend specific improvements: clearer facts on key pages, consistent business details across the web, content that answers the follow-up questions buyers ask and an indexing strategy so those pages can be found. We can't control how any model reasons, but we can make your information easier to verify.
Frequently asked questions
What is an example of a reasoning model?
OpenAI's o1, released in September 2024, was one of the first widely available reasoning models. Anthropic's Claude with extended or adaptive thinking and Google's Gemini thinking models offer similar step-by-step reasoning.
Are reasoning models better than regular AI models?
They are better at multi-step problems like math, coding and detailed analysis. For quick tasks like drafting or summarizing, a standard model is often faster and cheaper with similar quality.
Can I see a reasoning model's thinking?
Usually only in part. Providers such as OpenAI and Anthropic show a summary of the reasoning rather than the full raw chain of thought.
Do reasoning models affect which businesses AI recommends?
They can. Research tools built on reasoning models search more widely and compare sources more carefully. Clear, consistent and verifiable information gives your business a better chance of being included.