Where Do ChatGPT, Perplexity, Gemini, Claude and Google Get Their Information? (We Tested All Five)

Cross-Engine Citation Study · Round three

By Minel Gunesoglu, founder of Is My Brand in AI · Original research, June 19, 2026 · Round two August 9, 2026 · Round three collected 5 to 7 October 2026 · Updated October 10, 2026 · Cited by 3 publishers in 3 countries

TL;DR: Round three of the Cross-Engine Citation Study compares the source lists of five AI engines for the same 50 questions. On the 40 questions where Google AI Overviews, ChatGPT, Gemini, Perplexity and Claude (API) each cite a source, all five cite the same page in 2 and at least two of them in all 40. The shortest of the five lists is a single page in 19 of the 40, and ChatGPT's list was fully seen in 11.

When the same question goes to several AI engines, each answer comes with its own list of sources. This round asks two things about those lists: which sources each engine cites, and how far the engines share them.

This is a second analysis of the answers collected for The AI Answer Evidence Index. It is exploratory and outside that study's registration. It was decided on 6 October 2026, after six of the seven collection blocks, before any figure had been extracted and before any count of sources by engine had been made. The preregistration of 5 October named Google AI Overviews, ChatGPT and Gemini; Perplexity and Claude (API) were added on 6 October (deviation log, entries 3, 5 and 7).

Do AI engines cite the same sources for the same question?

On the 40 questions where all five engines each cite a source, all five cite the same page in 2 and the same domain in 5, and at least two of them cite the same page in all 40.

Source: table A2, table A2b and table A3.

The median overlap is 0 for pages and for domains.

Two facts set the frame for that count. The shortest of the five lists is a single page in 19 of the 40 questions, and ChatGPT's list was fully seen in 11.

Asked ten of the questions a second time, Google AI Overviews, ChatGPT and Gemini cited again 63%, 29% and 59% of the pages of their first answers (32 of 51, 5 of 17 and 17 of 29). So for these three engines one answer is one draw from a list that changes between askings. Perplexity and Claude (API) were asked each question once.

Counted per question, 733 of the 925 cited pages are cited by one engine only, 135 by two, 40 by three, 15 by four and 2 by all five.

What was measured?

The same 50 questions were put to five engines on 5 to 7 October 2026 (dates in UTC+3, the operator's time zone), each question once per engine. The questions ask for a price, a cost or another figure, in English, with New York as the location.

Source: table A1.

Perplexity lists the most pages per answer and ChatGPT the fewest. Perplexity's interface language was Turkish and its answers are in English; 5 of the 50 records carry a notice that a preview of the advanced search was switched on, and 10 further records note that the notice could not be observed reliably. For Claude (API) at most three searches were allowed, and one was used in 48 answers and none in 2; its answers are what the model returns through the API, not a reading of a consumer interface. The full route of each engine is on the method page, and how the engines differ as products is covered in AI search engines.

Three terms carry the counts:

• Page. For every answer the cited pages were reduced to the same key: host and path, without "www." and without the fragment. The query string is dropped, except the video id of a YouTube address.
• Domain. The registrable domain of the host, so the blogs hosted on medium.com or substack.com count under that one domain.
• Overlap. The Jaccard index: the sources all engines of a comparison cite for a question, divided by all sources any of them cites for it. Every overlap percentage on this page is a mean overlap: the mean of the per-question ratios.

How much could five lists share at most?

The all-engine overlap is bounded by the shortest list. Five lists cannot share more pages than the shortest of them holds.

• ChatGPT holds the shortest list, alone or tied, in 35 of the 40 questions.
• The shortest list is a single page in 19 of the 40.
• The list sizes allow a mean overlap of at most 15.2% for pages and 17.5% for domains.

The observed overlap is 0.3% for pages and 1.0% for domains. Without ChatGPT the bound for the other four engines is 23.8% for pages, and their observed overlap is 1.2%.

Source: table A2 and table A3.

The median page overlap is 0 in every comparison of the table. The median domain overlap is 0 as well, except in the two rows of Google AI Overviews, Gemini and Perplexity (4.5% and 2.3%).

Rounds one and two of this study had four engines. On the 41 questions of that row, all four cite the same page in 3 questions, and 686 of the 825 cited pages are cited by one engine only. The five-engine row holds those 41 questions without one, for which Claude (API) cites no source.

Perplexity's answer to one question (SVC-07) was read from the account's stored thread after an interruption, so the three-engine row is given with and without it. The rows that hold ChatGPT do not hold that question: ChatGPT cites no source for it.

Which pairs of engines share the most sources?

Pair by pair the lists share more. Over the ten pairs the mean page overlap runs from 2.9% to 14.7%.

Two measures stand side by side:

• Jaccard divides the shared sources by all sources of both engines.
• Containment is the share of one engine's own sources that the other also cites for the same question, pooled over questions.

Source: table A4.

Of the pages ChatGPT cites, 19% are also cited by Google AI Overviews and 25% by Perplexity (domains: 46% and 50%). Of Google's and Perplexity's pages, 5% and 4% are cited by ChatGPT.

By Jaccard, every other engine's highest pairwise value is with Google AI Overviews. List length moves both measures: Jaccard falls when one list is much longer than the other, and containment of a short list rises with the length of the other list. Perplexity has the longest lists, and by containment the highest value of ChatGPT and of Claude (API) is with Perplexity.

For ChatGPT the two values are about the same. On the 42 questions where ChatGPT, Google AI Overviews and Perplexity all cite a source, Perplexity also cites 35 of the 72 domains ChatGPT cites and Google AI Overviews 33 of the 72. For Claude (API), on its 46 such questions, the counts are 89 of 237 and 79 of 237.

The pair values carry no interval and each pair stands on its own questions, so these orders are descriptive and the study draws no ranking from them.

How much does one engine agree with itself?

An overlap between two engines needs a yardstick. The repeat run supplies one for three engines: the first ten questions of the asking order were asked a second time of Google AI Overviews, ChatGPT and Gemini, which shows how far one engine's source list agrees with itself. Perplexity and Claude (API) were asked each question once.

A row holds the questions where both answers cite at least one page. A mean or median over fewer than 10 questions is given as a whole percentage.

Source: table A5.

The comparison with two different engines is made like for like: for each pair, on the questions where both engines cite at least one page in both askings.

Source: table A5b.

On these questions the mean page overlap of two engines is 0% to 8%, against 24% to 50% for either engine asked twice.

The order holds for the means and not for every question. ChatGPT's two lists share no page in 5 of 9 questions, and the two engines share less than either shares with itself in 3 of 6, 5 of 7 and 3 of 9 questions.

How complete are the source lists?

An interface does not always show every source an answer names. A marker in the text can name a source that the source panel does not list.

Source: table A1.

• Six ChatGPT answers cite no source: their saved pages hold no "Sources" control and no outbound link, which indicates an answer without a search.
• In 32 of the other 44 ChatGPT answers a marker names more sources than the panel listed (52 mentions).
• 25 Gemini answers hold markers that name 45 sources without an address.
• ChatGPT's and Gemini's lists are both fully seen in 4 of the 40 questions.

On the 11 questions where ChatGPT's list was fully seen, the five engines share a page in 1 and a domain in 1.

Pages are counted by the key above. Perplexity's records hold 608 source entries; 8 of them repeat a page of the same answer under the key, which leaves 600 page citations. By the same key Google AI Overviews, ChatGPT and Gemini cite 468 distinct pages. The Evidence Index counts the sources of the same answers as 483 addresses, because it keeps each address as it was cited, with its query string.

What websites do AI engines cite most?

239 of the 249 answers of the five engines cite a source, on 543 distinct domains.

Source: table A6.

The six domains all five engines cite are bankrate.com, github.blog, kayak.com, samsung.com, tomshardware.com and tsa.gov.

Source: table A6.

23 answers cite YouTube: 21 AI Overviews and 2 Gemini answers. No YouTube address is in the source lists of ChatGPT, Perplexity or Claude (API).

Reddit is cited in 13 Perplexity answers, 11 AI Overviews, 4 Gemini answers and 1 ChatGPT answer, and in no answer of Claude (API).

Two earlier studies on this site look at one engine at a time: the most-cited sources in Google's AI Overviews and retrieval and citation in the Claude API's web search.

What does this mean for a brand?

This section is a reading of the results above, not a result.

• On these questions most cited pages were cited by one engine: 733 of the 925. A citation by one engine did not, in most cases, come with a citation by another for the same question.
• The engines share a domain in more questions than a page. All five cite the same domain in 5 of the 40 questions and the same page in 2, and 46% and 50% of the domains ChatGPT cites are also cited by Google AI Overviews and Perplexity. A site can be cited by several engines through different pages.
• A source list is one draw. Asked the same question twice, Google AI Overviews, ChatGPT and Gemini cited again 63%, 29% and 59% of the pages of their first answers.

The manual way to check where a brand stands in each engine is in how to measure AI search visibility.

We run this same measurement for individual brands, with their own buyer questions and the brands they compete with: see AI visibility consulting.

What do these results cover?

The results describe these 50 questions, which ask for a price, a cost or another figure and were asked in English with New York as the location, on the collection dates, with one answer per question and engine, through the access routes named above. Source lists are counted as each interface, or the API, returned them.

The headline is the overlap of all five engines at once, which is lower by construction than a pairwise overlap. The pairwise table is the view nearest to studies that report pairs. A study that pools each engine's most-cited domains over all its questions and compares two engines at a time measures something else, and its values are not comparable with the ones counted question by question here. The nearest view in this study is the pairwise domain overlap per question, 7.9% to 22.8% over the ten pairs.

Earlier rounds of this study (June and August 2026) used other questions and four engines, and are not pooled with this one.

Data and code

• Method. The collection, the page key and the access routes are on the method page of The AI Answer Evidence Index.
• Preregistration. The answers are those of The AI Answer Evidence Index. Its preregistration, doi:10.5281/zenodo.23145556, names Google AI Overviews, ChatGPT and Gemini. This analysis of sources is outside that registration.
• Tables and files. Every table on one page, each with its own link, and the files behind them: the computed views, the method note, the deviation log, the questions and the scripts.
• Data and code. The data and code of this analysis are published with those of The AI Answer Evidence Index, under CC BY 4.0 with a Zenodo DOI: doi:10.5281/zenodo.23238429.

If a count here does not match what you get from the files, tell us. We will correct it and say what changed.

Rounds one and two: June and August 2026

The two earlier rounds of this study used other questions. Their results stand below as published and are not pooled with round three.

Rounds one and two in brief: ChatGPT gets its information from a few big publishers and Reddit; Perplexity casts the widest net; Gemini cites the fewest and most obscure. Asked the same buyer questions, all four engines shared just 1% of their sources, but 32% of the brands they recommended.

Most advice about "getting cited by AI" treats the AI as one thing. Optimize your page, earn some mentions, and you will show up in the answers. But "the answers" are not one place. ChatGPT, Perplexity, Gemini and Google's AI Overview each run their own retrieval, and we wanted to know a simple thing: where does each one actually get its information, and when you ask them the same question, do they reach for the same sources?

So we tested it. We took a set of ordinary B2B-SaaS buyer questions ("best CRM for startups," "HubSpot vs Salesforce," "how to reduce customer churn," "cheaper alternatives to HubSpot") and ran them through all four engines in June 2026, logging every source each one cited. (Full method at the bottom; we ran real prompts and recorded what actually appeared, nothing modeled.)

The short answer: no, they barely overlap. The same question produces four almost entirely different source lists. If you are planning your visibility around "AI," you are planning around four different games.

Then we re-ran the whole thing in August 2026 and recorded something we had missed the first time: not just the sources each engine read, but the brands each one recommended. That produced the more useful half of this study, and it points the opposite way: the four engines share almost no sources but agree on nearly a third of their recommendations.

What sources did each engine cite for the same question?

Here is the cleanest way to see it. This is the source list each engine cited for the exact same query, "best CRM for startups", on the same day, in round 1 (19 June 2026):

Look at how little they share. Only two sites appear in more than one list: techradar.com (in Perplexity, ChatGPT and the AI Overview) and reddit.com (in ChatGPT and the AI Overview). Neither reaches Gemini. In this June round, not a single source is cited by all four engines. (In the August round two were on a single question, zapier.com and project-management.com, and four across the twelve questions pooled; see the limitations below.) Gemini's four sources overlap with the other three engines exactly zero times. The same buyer, asking the same question, gets recommended pages from four non-overlapping corners of the web.

This was not a fluke of one question. We saw the same pattern across every query we ran on all the engines.

Do ChatGPT and Perplexity cite the same sources?

For six questions we ran head-to-head through Perplexity, ChatGPT and Gemini (covering recommendation, comparison, how-to and alternative queries). Here is the overlap between the two engines that cite the most sources, Perplexity and ChatGPT, measured as shared domains on the identical query:

Two of six questions had zero shared sources. The rest had one or two. The average was under one shared domain per question, out of eight to ten cited by Perplexity and four to seven by ChatGPT.

Bring Gemini in and the overlap does not shrink. It disappears. Across all six questions in the June round, not one domain was cited by all three engines. Not on the recommendation questions, not on the comparisons, not on the how-tos. The closest any question came was "HubSpot vs Salesforce," where Perplexity shared two domains with Gemini and two different ones with ChatGPT, but nothing that all three reached for. On "how to reduce customer churn," Gemini cited no sources at all.

The takeaway is blunt: ranking in one AI engine tells you almost nothing about whether you will be cited in another. They are not reading from a shared shortlist.

Where each engine gets its information

The lists are not just different. They are different in character. After running the set, each engine had a recognizable citation style.

Google's AI Overview is the most mainstream. Its sources are essentially Google's own organic winners, condensed: the big review sites (TechRadar), the established vendors (Salesforce, Zoho, HubSpot, Monday), the well-known listicles (Podium), and Reddit. If a page already ranks on page one of Google, it has a real shot at the AI Overview. Classic SEO still does most of the work here. (We have two deeper studies on this engine alone, the most-cited sources in AI Overviews and who it cites by industry, and Reddit appeared in 13 of 21 AI Overviews in the first and 11 of 27 in the second.)

Perplexity casts the widest net. In round 1 it consistently cited the most sources per answer (often eight to ten; in round 2 the range widened to eight to nineteen) and the broadest mix: review sites, vendor pages, Reddit threads, even YouTube videos. For comparison questions like "Notion vs Asana," it pulled in YouTube three times, including a Japanese-language video. Perplexity is the most transparent engine, and the one with the most doors to get in through.

ChatGPT concentrates on a few big publishers, then a long tail of obscure blogs. When its web search is on, a handful of names recur across almost every answer (TechRadar, Forbes, Reddit) alongside a surprising volume of small, unfamiliar sites (think "StackFYI," "Prodify Flow," "RetentionCheck") that look like SEO-built content. Once it even cited an academic arXiv paper for a churn question. One important quirk: with web search off, ChatGPT often cited no third-party sources at all. It answered from memory and linked only to the vendors' own homepages. That is the default experience for a lot of free users, and it means the only "source" many people see is the brand's own site.

Gemini cites the least, and the most obscure. It returned the fewest sources of any engine (often just one or two, sometimes none) and they were almost entirely small, unrecognized sites you have likely never heard of (UpliftGTM, ZoFlowX, RichClicks). On the "how to reduce customer churn" question, it cited nothing at all and answered purely from its own training. Gemini is the hardest engine to influence and the hardest to predict.

You can line these up on a spectrum, from the most predictable engine to the least:

Does the type of question change which sources get cited?

There was a second pattern hiding in the data: the type of question changes which kind of source gets cited, and it changes the same way across engines even when the specific domains do not match.

• "Best [category]" questions pulled from review sites and listicles (TechRadar, PCMag, G2, Cloudwards). Reddit and YouTube were mostly absent.
• "X vs Y" comparisons leaned on YouTube and Reddit, plus each vendor's own comparison page. This is where brand-owned pages actually got cited.
• "How to" / strategy questions pulled from company blogs: for "reduce customer churn," Perplexity cited ten company blogs and vendor pages (Stripe, HubSpot, Qualtrics, Optimove, Contentsquare among them) and no Reddit or YouTube at all.
• "Alternatives" and "cheaper than" questions were the most community-driven, surfacing Reddit and even LinkedIn posts alongside competitor listicles.

If you only have budget to earn one kind of placement, this tells you where to spend it for the queries you care about.

The August round in numbers

Every figure below is recomputed from the published CSV and JSON. Nothing here is estimated.

Per engine, across the same twelve questions:

And by the kind of page cited:

Reddit and YouTube together made up 17.3% of Google's AI Overview citations, 18 of 104, far more than any other engine. ChatGPT was next at 7%.

Because the engines overlap so little, no single engine covers much of the set. Averaged across the twelve questions, this is how much of each question's cited set you would see by watching one engine, or two:

Sourcing cuts the other way too. If engines disagree this much about which sources to cite, it is worth asking whether the pages they cite can be checked at all, and we measured that in a companion study, reading 252 numeric claims one at a time.

Which sources show up in more than one AI engine?

Almost nothing is universal, but three things came closest, and they are the practical core of a cross-engine strategy.

1. Reddit. It was the single most common bridge between engines, showing up in comparison, alternative and "best free" answers across Perplexity, ChatGPT and the AI Overview. If there is one off-site place to be discussed, it is the relevant subreddit. (This matches what we found studying Google's AI Overview on its own, where Reddit was the most-cited site by a wide margin.)
2. Your own site, but mostly on comparison queries. For "HubSpot vs Salesforce" and "Mailchimp vs Klaviyo," the engines frequently cited the vendors' own comparison pages. A clear, honest "us vs them" page on your domain is one of the few brand-owned assets AI engines reliably reach for.
3. A small set of trusted publishers per engine. TechRadar showed up in three of the four engines in the June round (Perplexity, ChatGPT and the AI Overview); in August two sources reached all four engines on a single question (zapier.com and project-management.com) and that question was a recommendation query, not a comparison. On the comparison questions no source at all reached all four. Getting included in the big roundups still pays off in more than one place.

Do they at least recommend the same brands?

Everything above is about sources, the pages an engine reads. That left the more interesting question unanswered: if four engines read four different corners of the web, do they end up recommending four different shortlists of products? The brands each engine named, in order, are in the same August CSV.

So in August 2026 we ran the study again, and this time we logged the brand names in each answer alongside the sources. Seven of the questions were open-ended recommendation queries, the ones where a buyer is genuinely asking the engine to pick for them, and all four engines answered every one.

The result flips the headline on its head.

The four engines agreed on 32% of the brands they recommended and 1% of the sources they cited. Every one of the seven questions produced at least one brand all four engines named. Six of the seven produced no shared source at all.

That is the finding: the engines are not reading the same pages, but they are arriving at the same shortlist. Overlap on brands runs more than thirty times higher than overlap on sources (32% against 1%).

The practical reading (and we want to be careful here, because it is an inference, not a measurement) is that a recommendation does not appear to come from any single page an engine happened to read. If it did, four engines reading four disjoint source lists would produce four disjoint shortlists, and they do not. What the shortlist tracks is something closer to a brand's overall weight across the web: how consistently it is named, everywhere, by everyone.

That is worse news than it sounds for anyone hoping to find the one page to get onto. There is no such page. It is also why "we got mentioned in a roundup" so rarely moves the needle on its own.

One small site did get into three engines

There was one exception worth staring at. On "cheaper alternatives to HubSpot," a site called Searchlab was cited by ChatGPT, Perplexity and Gemini. Established sites reach three or four engines on the popular questions (HubSpot on "best CRM," Zapier and project-management.com on "best project management software"), but Searchlab is the only site with no established footprint to do it. A second small site, AI Topia, made it into two.

Neither is a major publisher. Both are small sites running detailed, honest, specific comparison lists on a narrow topic. The big names that dominate individual engines (TechRadar, Forbes, G2) did not manage the same trick on that question.

We are not going to build a rule out of one query. But it is the clearest counter-example we have to the assumption that cross-engine reach requires a big masthead, and it is the kind of thing we will be testing deliberately in the next round.

What this corrects

Three things in our own earlier reporting need adjusting, and we would rather say so than quietly restate them:

• We overstated a range, and the overstatement travelled. An earlier version of this page said the other three engines cited "between four and fifteen sources each" on the comparison questions. Checked against our own table, the true range is four to thirteen: the highest single count is Perplexity's 13 on "HubSpot vs Salesforce." The sentence has been replaced with the full count table below, because summarising counts as a range is what produced the error in the first place. The wrong figure also went out in a pitch email and a newsletter submission on 9 August, where we cannot correct it.

• "Not one domain was cited by all three engines" was true for June's six questions, and is not true in general. In this round, two domains (zapier.com and project-management.com) were cited by all four engines on the project-management question, and Searchlab reached three engines on another. The honest version is: shared sources are rare, not absent.

• We had no brand data before this round. When this study was cited elsewhere, the specific brands named in that citation were not ours. They could not have been, because we had not recorded any. Now we have, and the trio people tend to assume does not hold up. Across the seven questions, all four engines named HubSpot together twice and Zoho twice; they never once all named Salesforce. The brand all four agreed on more often than any other, three times out of seven, is Pipedrive, which is precisely the name these lists leave out.

How long does a citation last? We checked seven weeks later

Running the study twice gave us something the first round could not: the same twelve questions, the same engine, seven weeks apart, with both rounds published in full (June, August). So we asked what happens to a source list over time.

Perplexity is the only engine where the comparison is clean (in June it was the one engine we put all twelve questions to), so this is a Perplexity measurement, not a claim about all four.

Across those twelve questions, Perplexity cited 80 distinct sources in June. Seven weeks later, 42 of them were still being cited. The other 48% were gone. Overlap between the pooled June and August lists was 0.29 (Jaccard). Those figures pool all twelve questions into one set per round. Question by question the turnover is higher (58% of June's source slots, pooled across the twelve questions) because a source can persist somewhere on the site while dropping off the specific question it used to answer. Both are recomputable from the two published files: sources are counted as distinct domain strings exactly as the CSV records them, with no subdomain collapsing (blog.hubspot.com and hubspot.com count separately).

But the churn is not total, and the exception is the useful part: every one of the twelve questions kept at least one source. Not a single list turned over completely. The names that survived tend to be the same kind of thing (zapier.com, thedigitalprojectmanager.com, project-management.com, techradar.com, youtube.com): deep topic-specific resources and large reference sites.

The spread is worth noting too. The most volatile question was "best AI writing tools," where 90% of the June sources were replaced, a category that genuinely churns. The most stable was "HubSpot vs Salesforce" at 25%: a mature comparison whose sources have settled.

Two things follow, and they pull in opposite directions:

• Being cited once is a snapshot, not a status. Half the list turned over in two months. If you earned a citation in the spring and have not checked since, you may well have lost it without anything visibly changing on your end.
• There is a durable core, and it is reachable. Roughly half the sources persisted, and they are not all major publishers. Being the thorough, specific resource on a narrow question is what appears to keep a source in rotation.

One caveat we would rather state than bury: several of June's source panels were truncated with a "show more" we could not fully expand, so the 80 is the number of sources we recorded. The 48% is churn among recorded sources, a good estimate, not an exact count.

How to get cited when every engine reads something different

The headline finding has a direct, slightly uncomfortable implication: "get visible in AI" is not a single goal you can complete. You are running four campaigns, not one. Here is how we would prioritize them:

• Stop optimizing for "AI" in the abstract. Pick the engines your buyers actually use and treat them separately. Check where you stand in each. There is no shortcut, and the manual method is free; see how to measure AI search visibility. Once four engines a week stops fitting in an afternoon, the tools that log which page each engine cited are compared in best AI citation tracking tools.
• Keep doing real SEO for the AI Overview. It is the one engine that rewards classic ranking. If you are on Google's page one, you are most of the way to the AI Overview.
• Earn third-party validation, not just on-page work. In the August round Perplexity, Gemini and the AI Overview each sent more than half of their citations (54% to 57%) to sites no vendor owns: lists, review media, YouTube and Reddit. A page that only says good things about itself has no route into that half. The mechanics of earning those mentions are in how to get cited by ChatGPT and our broader guide to improving brand visibility in AI search.
• Be present where people argue and compare: Reddit threads, comparison roundups, and an honest comparison page on your own site. Those are the placements that travel across the most engines.
• Accept that Gemini is the hard one. It cites little and cites unpredictably. Do not build your plan around it; treat any Gemini citation as a bonus, not a baseline.

The engines do not agree on who to trust. Until they do, your job is not to win "AI"; it is to be one of the few names that keeps showing up no matter which corner of the web a given engine happens to pull from.

We run this same measurement for individual brands, with their own buyer questions and the brands they compete with: see AI visibility consulting.

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Methodology

This section gives the method of rounds one and two of the Cross-Engine Citation Study, our own measurement of which sources ChatGPT, Perplexity, Gemini and Google's AI Overview cite when asked the same B2B-SaaS buyer questions. Every number in the sections on those two rounds comes from them, and both rounds are published in full under CC BY 4.0. The method of round three (five engines, 50 questions, October 2026) is under What was measured? above.

Download CSV Download JSON

We ran ordinary B2B-SaaS buyer questions through four AI engines and logged every source each cited in its answer. That was done twice, in June and August 2026, before round three.

Read this before quoting a number from this page. The two rounds have different coverage, and mixing them will produce a number we did not measure.

Round 1: 19 June 2026 (sources only)

The engines were not all asked the same number of questions, so the headline finding is scoped deliberately: six questions were put to ChatGPT, Perplexity and Gemini, and it is across those six that no domain was cited by all three. Google's AI Overview was sampled on three questions; where we say a source appeared in "all four" engines, we mean on those three questions specifically. Quoting this as "twelve questions across four engines" would overstate what we did.

• Questions: twelve buyer queries across four intents: recommendation ("best CRM for startups," "best email marketing platform"), comparison ("HubSpot vs Salesforce," "Mailchimp vs Klaviyo"), how-to ("how to reduce customer churn," "how to choose a CRM"), and alternatives ("Salesforce alternatives," "cheaper alternatives to HubSpot").
• Coverage: all twelve questions on Perplexity; a representative six (one to two per intent) on ChatGPT and Gemini; three (one per main intent) on Google's AI Overview. Single run per engine-question pair.
• Setup: Perplexity in default web-search mode; ChatGPT on the free tier with web search enabled (we note separately that with search off it cited no third-party sources); Gemini (Flash model); Google AI Overview as shown in standard search results.
• What we recorded: the domains/sources each engine displayed as citations or in its sources panel, in order.

Round 2: 6–7 August 2026 (sources and brands)

Round 1 recorded which pages each engine read but not which products it recommended, which left the more interesting question unanswered. Round 2 fixed that.

• Questions: the same twelve queries, same wording, same intents.
• Coverage: all twelve questions on all four engines. Single run per pair.
• What we recorded: the brand names in each answer, in the order they appeared, plus every source, each tagged by type (vendor's own site / review media / independent listicle / Reddit / forum / YouTube / reference).
• The brand-overlap figures come from seven of the twelve questions: the open-ended recommendation and alternatives queries. We deliberately excluded the "X vs Y" comparisons, because the question itself names the brands: all four engines "agree" on Mailchimp and Klaviyo when you ask about Mailchimp versus Klaviyo, which would push the average to a meaningless 100%. We also excluded "how to reduce customer churn," where all four engines recommended no brands at all. And we excluded "how to choose a CRM," where Perplexity named no brands while the other three did; a four-way overlap cannot be computed when one engine contributes an empty set. Twelve questions minus three comparisons, minus those two, is the seven.
• Overlap is measured as Jaccard similarity: brands (or sources) named by all four engines, divided by the total distinct set named by any of them. Light normalization was applied to brand names only ("HubSpot CRM" and "HubSpot" count as one brand; "Salesforce Starter," "Salesforce Essentials" and "Salesforce Sales Cloud" count as Salesforce).
• The seven-week churn figure compares June's Perplexity sources with August's, pooled across the twelve questions (48%) and question by question (58%), and is Perplexity-only because June's full twelve-question coverage exists for that engine alone. Several June panels were truncated, so it measures churn among visible sources.
• One behavioural note that affects the source figures: on "HubSpot vs Salesforce," "Notion vs Asana" and "Mailchimp vs Klaviyo," ChatGPT cited nothing (it answered from memory), while the other three engines did retrieve, with two exceptions: Gemini cited nothing on "Mailchimp vs Klaviyo" or on "how to choose a CRM"; on the latter it named nine brands without a single source. ChatGPT behaved the same way on both how-to questions. Rather than summarise that as a range, here is every count:

Counted from the August round; the same rows are in the CSV linked above.

Counted from the August round; the rows are in the CSV linked above.

Perplexity and the AI Overview cite consistently on every one of these. ChatGPT cites on none of them. Gemini sits between the two and is the least predictable of the four, which is the same character it showed in the June round.

* Gemini's "Mailchimp vs Klaviyo" answer is truncated at source. It stops mid-sentence and never reaches the point of citing. We re-opened the stored conversation rather than assuming our own load error, and it is genuinely cut off. Read that zero as "no sources in an incomplete answer," not as a clean measurement.

Limits, stated plainly

AI answers vary between runs, and every figure here comes from a single run per engine-question pair. We report only what we observed (nothing is estimated or modeled), but a single run cannot separate a stable pattern from a lucky draw. The 32%-versus-1% gap is large enough that we doubt it is noise; the individual per-question percentages are not, and should not be treated as precise. Both rounds are one market (US/English), one sector (B2B SaaS) and one moment in time.

This is a deliberately small, hand-run study; the value is in the side-by-side comparison, not the sample size. The next round will run each question multiple times so we can say something about stability rather than just position. A separate study has since done this for Claude: 50 buyer questions run three times each, where two runs of the same query overlapped 0.555 on cited domains.

Which rows you can re-open yourself. A study that asks you to verify it should say where verification runs out. Of the twelve runs per engine in round two: Gemini, all twelve have a saved conversation that re-opens, so every Gemini row can be checked against the original answer. Perplexity, two of twelve; the rest were not saved at the time. ChatGPT, none: every question ran in a temporary chat, which the method requires so that no memory carries between questions, and temporary chats leave no transcript. That is the cost of a clean session rather than an oversight, but the effect is the same: the ChatGPT rows rest on our logging alone. Google's AI Overview, none, because AI Overviews have no permanent URL and can differ between runs.

Source counts are a floor, not a total. ChatGPT and the AI Overview both hide some sources behind a "+N" badge, so what the file records is what was visible.

From the next round on, every run gets a screenshot saved at collection time. We would rather publish the gap than quietly leave it out.

Frequently asked questions

Where does ChatGPT get its information?

When its web search is on, ChatGPT cites pages from the live web. In the earlier rounds a few big publishers recurred across its answers (TechRadar, Forbes, Reddit) alongside many small sites. With web search off, ChatGPT often cited no third-party sources at all and answered from memory.

Do ChatGPT, Perplexity, Gemini, Claude and Google cite the same sources?

Rarely the same page. On the 40 questions where all five engines cite a source, all five cite the same page in 2, and at least two of them share a page in all 40.

Does asking the same question twice give the same sources?

Not fully. Asked ten of the questions a second time, Google AI Overviews, ChatGPT and Gemini cited again 63%, 29% and 59% of the pages of their first answers.

Cite this study

This is original, hand-run research. You are welcome to cite or share it (CC BY 4.0). Suggested attribution:

Minel Gunesoglu, Is My Brand in AI (2026). Cross-engine AI citation study: where ChatGPT, Perplexity, Gemini and Google get their information. https://ismybrandinai.com/do-ai-engines-cite-the-same-sources

Twelve B2B-SaaS buyer questions, four AI engines, run 19 June 2026 (sources) and again 6–7 August 2026 (sources and recommended brands). See the method above or our full methodology page.

The publishers that cited this study between June and September 2026, and the figures they took from it, are listed in the Global Citation Case Study.

The raw runs are published, not available on request. Every answer we logged, with the brands named in order and every source with its type code, is in these two files under CC BY 4.0:

• cross-engine-brands-sources-2026-08.csv: 48 rows, one per engine-question pair
• cross-engine-brands-sources-2026-08.json: same data with the study metadata and the known limits
• cross-engine-brands-sources-2026-06.csv: the June round, the one the seven-week churn figure is measured against
• cross-engine-brands-sources-2026-06.json: same, with that round's metadata and limits

The June and August files, with the method notes, are in the study's repository and archived at CERN's Zenodo under doi.org/10.5281/zenodo.23427370, which is citable and cannot be quietly edited.

Every percentage on this page can be recalculated from those files. If a count here does not match what you get, tell us and we will publish a correction saying what changed.