Why the Five Engines Give Different Answers

Same question, five answers

This course focuses on five engines: ChatGPT, Google AI Overviews, Gemini, Perplexity and Claude. Ask all five the same question and you will almost never get five matching answers.

One names your brand first, another leaves it out. One lists its sources, another doesn't. This isn't a malfunction. The engines are built from different parts.

This lesson covers the main reasons for the difference. The goal isn't to know how each engine works inside, which nobody outside can fully see. The goal is to read the differences correctly.

Reason 1: different models, different training

Each engine runs on a different language model or model family. Those models were trained by different companies, on different text, at different times.

As a result:

• One model may know your brand while another doesn't.
• Cutoff dates differ, so one model may know about your latest product while another has never heard of it.
• Each model has its own style and its own idea of a good answer. One gives a short list, another writes several paragraphs.

As we saw in Lesson 1, the part of an answer that comes from training data is a different "memory" for every model.

Reason 2: different search behaviour

The engines also use live search differently. Read this as "usually", because features change over time. This lesson reflects the products as of October 2026.

• Google AI Overviews are summaries shown above Google search results. They don't appear on every search. When they do, they usually draw on web results and show links.
• Perplexity usually builds its answer from web sources and shows them next to the answer.
• ChatGPT, Gemini and Claude may answer from training data alone or with a web search, depending on the question, the product version and the settings.

Even when two engines both search, they may use different search systems, read a different number of pages and pick different ones. So they can rely on different sources at the same moment.

Reason 3: wording, language, place and time

Some of the variation comes from the question, not the engine. The same engine can answer differently when:

• the question is worded slightly differently ("best" versus "most affordable");
• it is asked in another language;
• the user is in another city or country;
• it is asked on another day;
• the model version is updated.

That's why a single screenshot isn't a lasting ranking. It's a dated observation, taken under specific conditions.

Example: one question across five engines

The table below is representative. "Northwind Boards" is a fictional product. The engines are deliberately labelled A–E: these results don't describe how any real engine behaves.

Question: "Which project management tools work well for remote design teams?"

What can we read from it?

• B didn't mention the product and showed no sources; it most likely answered from training data, and the product didn't come up in this answer.
• A and C showed sources; they most likely searched, and they named the product in different parts of the answer.
• E showed sources but didn't mention the product; it most likely relied on other sources.
• D produced no AI answer at all. That doesn't mean the product is invisible. There was simply no answer to measure.

An empty answer is not a zero

That last point matters. An engine may produce no AI answer for a question. Google not showing an AI Overview for a search is one example. An engine may also refuse to answer.

Neither is the same as "your brand didn't appear". To say a brand didn't appear, there has to be an answer first. Keeping that distinction is the first step to drawing the right conclusions. More detail: Invalid answers are not zero.

In the same way, a result on one engine isn't a rule for the others. You need to look at each engine's results on their own.

SOV Tracker is a measurement tool you can use to track these differences on the same questions over time; five-engine measurement is on paid plans, and the free scorecard covers ChatGPT only.

Summary

• The five engines answer differently because their models were trained on different data at different times.
• They use live search differently: when they search and which sources they pick both vary.
• Even one engine can answer differently when the wording, language, location, date or model version changes.
• A single screenshot is a dated observation, not a ranking.
• "No AI answer" and "refused" are not visibility zeroes. There is no answer to measure.