Why AI models give different answers: ChatGPT vs Gemini vs Claude

The same buying question can produce different companies, sources and recommendations across ChatGPT, Gemini, Claude, Perplexity and other AI assistants. The UK's new draft guidance on efficient AI use makes the model-choice issue unusually visible. Here is why answers differ and what businesses should measure instead.

In short: Different AI models and assistants can give different answers because they do not all share one model, one search index, one retrieval system, one source set or one fixed generation path. For a business, that means one ChatGPT check is not an AI visibility measurement. The useful question is whether your business appears consistently when the same real buying questions are asked across the AI systems customers actually use.

The UK Government has just provided an unusually good illustration of the problem.

Its draft guidance on using AI ethically and sustainably says users should consider whether a simpler tool such as a spreadsheet or search engine can do the job. If AI is appropriate, it recommends choosing “the smallest one which will do what you need”, with examples including Gemini Flash instead of Gemini Pro and GPT Instant instead of GPT Thinking.

The same guidance also says something important that received much less attention: you may get a different response if you ask the same question again.

That creates a useful question for any organisation using AI for research, comparison or recommendations:

What does “works” mean when changing the AI can change the answer?

For businesses trying to understand whether AI recommends them, that question is commercial, not theoretical.

Why do different AI models give different answers?

There is no single cause.

What a user sees can be affected by the underlying model, the product around it, the instructions the provider gives it, whether web search is used, which sources are retrieved, the conversation context and the variability built into generative output.

A useful way to think about it is:

This is why “same prompt” does not necessarily mean “same evidence in, same reasoning, same answer out.”

It is also why businesses should be careful with screenshots of a single AI response. They show what happened in that run. They do not establish what every customer will see.

First, an AI assistant is not the same thing as an AI model

People often use “ChatGPT”, “Gemini”, “Claude” and “AI model” as if they mean the same thing.

For visibility measurement, that can be misleading.

ChatGPT is a product. Gemini is a product family. Claude is a product and model family. Perplexity combines AI models with its own search infrastructure. A customer-facing assistant can also change the model or search path behind the scenes without the user thinking about it.

So when somebody searches for “why do different AI models give different answers?”, the practical answer is broader than model weights.

The result comes from the whole system:

question + product + model + retrieval + sources + context + generation

For businesses, that whole system is what decides whether your name appears.

Different AI products can search different parts of the web

The retrieval layer is one of the clearest reasons answers can diverge.

OpenAI documents a dedicated search crawler, OAI-SearchBot, used to surface websites in ChatGPT search results. It is separate from GPTBot, which OpenAI uses for content that may be used in foundation-model training.

Google’s AI experiences sit inside Google Search. Google says AI Overviews and AI Mode can use a variety of sources, including web sources, and normal Google crawl and index eligibility still matters.

Perplexity publicly describes a different architecture again. Its AI-first Search API uses its own index, hybrid lexical and semantic retrieval, multi-stage ranking and dynamic content parsing.

Those public documents are enough to establish the important point without pretending we can see inside every provider:

AI assistants do not all reach an answer through one universal search and retrieval pipeline.

For the underlying mechanics, see how AI retrieval works from query to answer.

If the candidate sources differ, the businesses available to be recommended can differ too.

The same buying question can produce a different commercial outcome

Imagine a buyer asks:

Who are the best outsourced marketing companies for a growing UK business?

One AI assistant might name four firms.

A second might name two of those firms plus three others.

A third might lean heavily on an industry directory or comparison article that the first assistant did not surface.

A fourth might not name your business at all.

All of them may have produced plausible answers.

From the buyer’s perspective, the task was completed.

From the businesses being considered, the outcomes were very different.

That is the part conventional rank tracking does not capture.

There is no universal “position three in AI” that applies across every assistant. The more useful measurements are whether you were named, how often you were named, which competitors appeared instead and which sources repeatedly surrounded those answers.

Why the same AI can change its answer

Differences are not limited to one provider versus another.

The UK Government’s draft guidance explicitly warns that asking the same question again may produce a different response.

That is normal for generative systems.

A response is generated rather than fetched from a fixed answer table. The product may also search the web again, retrieve a different set of pages or work with changed information.

For AI visibility, that creates a measurement problem.

Suppose ChatGPT recommends your business on Monday and does not mention it on Tuesday.

Which result is “your ranking”?

Neither on its own.

A better approach is repeated measurement. Keep the question stable, run it more than once and look for patterns rather than treating one answer as the truth.

Does using a smaller AI model change the answer?

It can.

The Government’s efficiency principle is reasonable: do not use more computing power than a task requires.

For a tightly bounded task such as reformatting a list, extracting fields or sorting information, a smaller model may produce exactly the output you need.

But “does the job” has to be defined by the outcome.

If the task is supplier research, competitor analysis or a recommendation, a smaller and larger model do not have to produce identical reasoning or identical names.

That does not mean the larger model is automatically better.

It means model selection should be tested against the job you actually care about.

For a business measuring AI visibility, the job is not “did the model return some text?”

It is closer to:

When a real buyer asks this question, which businesses make the answer?

Why one ChatGPT check is not enough

Opening ChatGPT and asking whether it recommends your company is useful as a quick check.

It is not a complete AI visibility audit.

A single result leaves several unanswered questions:

• Would Gemini name you?
• Would Claude?
• Would Perplexity?
• Would Grok or DeepSeek?
• Would Google AI Overviews or AI Mode surface the same businesses?
• Would the result change on a second run?
• Which sources are repeatedly influencing the answer?
• Which competitors appear when you do not?

This is why SearchScore separates website readiness from live answer visibility.

A website audit can tell you whether your own site has observable problems with crawl access, structure, entity clarity, authority signals and other readiness factors. Our AI visibility audit guide shows how to build that baseline.

Live answer testing tells you something different: what happens when the buyer question is actually put to the AI.

You need both views.

The sources behind the answer matter as much as the answer

Suppose your business is missing from a buying question across several AI assistants.

The next question should not simply be:

What should we add to our homepage?

Look at the sources appearing around the answers.

If several systems repeatedly surface the same comparison site, directory, specialist publication or industry guide, that source may be describing the market without you in it.

That creates a different action.

Your own website might need improvement.

But you may also need to be included, reviewed or accurately represented on the independent sites that AI systems keep finding.

Our guide to finding the sources AI uses around your market explains how to separate those opportunities from noise.

The chain is straightforward:

buyer question -> AI answer -> businesses named -> sources surfaced -> action

That is much more useful than trying to “rank in ChatGPT” as if it were a conventional ten-link search result.

How to measure AI visibility properly

If you want to know whether different AI systems recommend your business, use a repeatable test rather than an occasional manual search.

1. Start with real buying questions

Use questions a prospective customer would genuinely ask while choosing a supplier, product or service.

“Who are the best commercial solicitors for an acquisition in Manchester?” is more useful than “Tell me about Example Solicitors.”

The first tests discovery.

The second gives the AI your brand name in advance.

2. Keep the wording consistent

If you ask every assistant a substantially different question, you cannot tell whether the system changed the result or your wording did.

Use the same approved question set wherever the product allows it.

3. Test more than one AI assistant

Do not assume ChatGPT represents Gemini, Claude, Perplexity, Grok, DeepSeek or Google’s AI search experiences.

The point is not to declare one assistant “correct”.

It is to understand the range of answers buyers can encounter.

4. Run the test more than once

Generative answers can vary.

Repeated runs reduce the risk of turning one unusual response into a business conclusion.

5. Record businesses and sources separately

Track:

• whether your business was named
• which competitors were named
• which sources were surfaced
• whether the sources mention you
• whether the pattern repeats across assistants

A source can matter even when it is not your own website.

6. Separate diagnosis from measurement

A readiness score tells you what may be making your site harder to access, understand or trust.

A live answer check tells you whether you actually appeared.

Do not confuse the two.

7. Repeat over time

Models change. Search indexes change. Websites change. Competitors publish new material.

AI visibility is not a one-time property.

If the buying questions matter commercially, track them. The AI Search Tracker guide explains the difference between a one-off check and repeated monitoring.

How SearchScore tests the problem

SearchScore’s free AI check starts with one real buying question and puts it to six AI assistants: ChatGPT, Claude, Gemini, Perplexity, Grok and DeepSeek.

That is deliberately a quick check, not a claim that one question settles your AI visibility.

For a deeper view, the AI Visibility Playbook uses 10 buyer questions that the customer reviews and approves, checked twice across eight AI experiences. That produces 160 searches: 120 across the six assistants, plus 40 across Google AI Overviews and Google AI Mode.

The purpose of the repeat is simple.

We do not want one answer from one system to become the whole conclusion.

The output then connects the questions to the businesses named, the sources surfaced and the actions worth taking.

See how the AI Visibility Playbook works.

So which AI answer should a business optimise for?

There is no useful universal answer to that question.

Your customers may use different assistants. The products may change their models. Search and retrieval behaviour can change. A recommendation can vary between runs.

The better objective is consistency across the buying journey.

You want the information about your business to be:

• accessible to the relevant search and retrieval systems
• clear enough to understand
• supported by credible first-party and third-party evidence
• present in the sources that matter around your market
• strong enough that your business repeatedly enters the recommendation set

That is a more durable goal than trying to reverse-engineer one ChatGPT response.

Check rather than guess

The UK Government’s advice to use the smallest AI model that does the job is sensible as an efficiency principle.

For businesses, it also exposes an important measurement issue.

Different AI systems can do the job and still give the customer different answers.

If that answer determines which company gets considered, the difference matters.

Do not guess from one screenshot.

Ask a real buying question.

Compare the assistants.

Look at who gets recommended.

Then look at the sources behind those answers.

Check your business across six AI assistants ->

Sources

• UK Government AI Knowledge Hub: Using AI ethically and sustainably
• OpenAI: Overview of OpenAI crawlers
• Google Search Central: Generative AI in Search and web sources
• Perplexity: Architecting and Evaluating an AI-First Search API