How ChatGPT Does Query Fan-Out (15 Million Queries Studied)

We wanted to see for ourselves how ChatGPT retrieves information—and what that means for AI visibility—so we analyzed 66.6 million free, logged-out GPT-5.6 Luna responses and their 15 million fan-out queries from August and September.

Sidenote

Free sessions are closer to how most people use ChatGPT: more than 90% of its weekly users are on the free plan, according to Olivier de Segonzac, co-founder of RESONEO, in Search Engine Land.

Logging out also strips out chat history and memory, so we see ChatGPT's default behavior rather than answers shaped by one person's past chats.

Contents

Key findings
ChatGPT answers 6 in 7 prompts without searching the web
76% of ChatGPT's site: searches target a brand site
Consumer brands make up 8 of ChatGPT's 10 most site: searched domains
ChatGPT rarely site: searches OpenAI's partners, but their citation share doubled
Price and review fan-outs rose up to 47% in a month
ChatGPT's Reddit citations dropped 88% in six months
How to analyze fan-out queries in your industry
How our data compares to other studies
Final thoughts

14.4% of the 66.6 million responses contained fan-out queries.
Those responses contained about 1.6 fan-out queries on average.
6.8% of fan-out queries used a site: operator to search a single website, spread across ~116K different domains.
75.9% of the most common site: searches pointed at a brand site, versus retailers, government sites, forums, or review sites.
The word “official” appeared in 13.1% of English fan-out queries in September, up from 10.1% in August.
“Price” and “review” fan-outs rose up to 47% in a month.
In separate citation data, ChatGPT's Reddit citations dropped 88% in six months.
ChatGPT seems to be moving from sites that summarize information to the sources that produce it.

Update (October 2026): We originally said 14.4% of responses searched the web. More accurately, 14.4% contained fan-out queries. We've corrected that section, and apologize for any confusion. The underlying data and other findings are unchanged.

Only 14.4% of the 66.6 million ChatGPT responses contained fan-out queries.

Those responses had an average of about 1.6 fan-out queries each.

With about 1.6 queries, your page has to match one of those one or two searches.

If it doesn't, ChatGPT never sees it.

Of the 15 million fan-outs we analyzed, 6.8% used the site: operator to search a single website.

And among the ~160 most-searched sites, three in four of those searches targeted a brand site.

Sidenote

By “brand site,” we mean a company website like apple.com or ikea.com, as opposed to a retailer, government site, forum, or review site.

Retailers like Walmart, Costco and Amazon made up 11.9%, and Reddit and Facebook just 1.9% between them.

The words inside the fan-outs show the same focus on official, first-hand information, with “official” and “price” the most common after “2026”, and growing by a few percentage points month-over-month.

#	Word	August	September	Change
1	2026	17.6%	22.9%	+5.2 pts
2	official	10.1%	13.1%	+3.0 pts
3	price	6.0%	8.9%	+2.8 pts
4	india	3.0%	6.2%	+3.2 pts
5	best	5.0%	5.3%	+0.3 pts
6	site	4.0%	4.8%	+0.8 pts
7	australia	1.4%	4.3%	+2.9 pts
8	how	3.3%	3.6%	+0.2 pts
9	reviews	2.7%	3.6%	+0.9 pts
10	philippines	<0.8%	3.2%	new to top 50
11	what	2.3%	3.0%	+0.7 pts
12	company	2.2%	2.5%	+0.2 pts
13	meaning	2.9%	2.3%	−0.6 pts
14	review	2.2%	2.3%	+0.1 pts
15	september*	1.4%	2.1%	+0.7 pts
16	new	1.4%	2.1%	+0.7 pts
17	cost	2.2%	2.0%	−0.2 pts
18	ingredients	1.6%	1.9%	+0.4 pts
19	brand	1.8%	1.9%	+0.1 pts
20	owner	1.5%	1.8%	+0.3 pts

None of this is a big surprise. If you want a product’s price or specifications, a brand site is the obvious place to check.

When a question has no single right answer, that’s when ChatGPT is more likely to look beyond official sources.

For instance, in a separate test of 20,000 US prompts, 84.4% of factual questions about pricing, product features or company details cited at least one official source.

For comparison questions like “A vs B” or “which is cheaper”, that dropped to 68.3%.

The mix of sources changed too.

For factual questions, 63.4% of citations were to official websites.

For comparison questions, it was 46.4%, so most citations went to other sites.

So ChatGPT relies most on official sources for facts, and leans more on other sites when it has to weigh one option against another.

We’ll dive deeper into fan-out query language a little later.

Make sure your brand site can answer the factual questions

If ChatGPT is going to run site:youbrand.com searches for your prices, specs and support details, those facts need to be on your site, in plain text, on a page it can find.

The domains ChatGPT targets most with site: are big consumer brands.

These rankings are, of course, based on our own dataset and prompts, but with around 1 million site: queries behind them, they give a strong sense of which domains ChatGPT leans on most.

These are mostly brands that sell products, so a brand site is the natural source for details like prices and specs.

#	Domain	`site:` mentions
1	apple.com	9,442
2	ikea.com	7,361
3	nike.com	5,210
4	adidas.com	4,619
5	samsung.com	4,432
6	mi.com	3,303
7	lego.com	3,106
8	dior.com	2,662
9	reddit.com	2,586
10	gov.br	2,583
11	playstation.com	2,304
12	gov.uk	2,116
13	walmart.com	2,096
14	support.apple.com	2,005
15	costco.com	1,942

Government domains also feature heavily.

Gov.br, gov.uk, France’s service-public.fr, the NHTSA, the FDA and the SEC all sit in the top 150, which matches the observations of Lily Ray, Founder of Algorythmic, that ChatGPT favors official sources for YMYL questions.

Note also that Reddit is still the ninth most site: searched domain.

This fits with the findings of Suganthan Mohanadasan, co-founder of Snippet Digital, that ChatGPT still fetches Reddit when it wants real users’ opinions, despite citing it less often (which we’ll get to later).

Of the 17 publishers and platforms that currently have licensing deals with OpenAI, Reddit is the only one in the top 150 most site: searched domains.

One possible reason is that ChatGPT already has its partner content pre-loaded and ready to go.

David McSweeney, founder of QueryBurst, found that some high-authority publications, including Time, Business Insider and Forbes, have pre-synthesized, page-level summaries that appear to be dropped straight into ChatGPT's context.
Suganthan Mohanadasan spotted a separate retrieval source in ChatGPT’s traffic called “labrador”. He describes it as a “licensed tier”, and points out that several of its publishers have signed content deals with OpenAI.

Those site: searches show what ChatGPT looks up during query fan-out, but not what it ends up citing.

The cited domains tell a different story. Partner publications are everywhere.

In Brand Radar, partner titles among ChatGPT’s 200 most-cited domains more than doubled their combined share of citations, from 7.5% in March to 15.8% in July—and still held 12.5% in late September.

It's worth trying to get your brand coverage in these partner publications, because ChatGPT is citing them increasingly often.

The terms ChatGPT adds to its fan-out queries hint at the page types it wants to retrieve.

Between August and September, more of ChatGPT's fan-out queries were about prices, product specifications, and reviews, and slightly fewer were about definitions.

A gain of 1 or 2 percentage points can sound small, but these terms all started from a low base, and their relative rise shows starker growth.

For instance, “Price” went from 6.04% to 8.88% of fan-outs, a relative rise of nearly half (47%) in one month.

Adding to this, “Reviews” grew by a third, and “specifications” entered the top 50 for the first time.

On the flip side, “Meaning” and “definition” each fell by about a fifth, though from small shares, so that’s likely a dip rather than a trend.

Check which of your pages match the growing query types

You can assess which fan-out query types are most prominent in your industry using Brand Radar.

Just head to the AI responses report, filter to ChatGPT and your market, then filter the “Fan-out queries” column for words like “price”, “specifications” or “reviews” to see how often ChatGPT searches for each in your topic.

Alongside the fan-out data, we looked at which sites ChatGPT cites.

This draws on a separate dataset: Brand Radar's ChatGPT citations in the US from March to September 2026, covering 2.4 to 3.8 million answers a month.

Snippet Digital found that Reddit citations in ChatGPT dropped 86% in August, and data shared by Aleyda Solís, founder of Orainti, showed social, reference, and news sites losing the most citation share between July and August.

Our Brand Radar data shows the same drop across Reddit, Wikipedia, and YouTube.

In March, about 1 in every 5 ChatGPT answers cited Reddit, but by late September it was about 1 in 43.

Similarly, Wikipedia dropped from 1 in 7 answers to 1 in 35 over the same period, and YouTube from about 1 in 370 answers to 1 in 500.

These sites are still among ChatGPT's most-cited domains, but their share has shrunk fast, so if a lot of your AI visibility comes through them, keep an eye on how often ChatGPT cites them for your topics.

ChatGPT is moving from sites that summarize information to the sources that produce it

The citations Reddit, Wikipedia and YouTube lost went to research bodies, regulators, brands and hands-on testers, while explainer and aggregator sites fell.

The chart below shows some of the biggest rank changes among ChatGPT's 200 most-cited domains, across 2.4 million answers in March and 3.8 million in September.

PubMed climbed from 64th to 4th among ChatGPT's most-cited domains and the FDA entered at 8th, while health sites that summarize that research, like WebMD and Verywell Health, fell.

To make sure this wasn't just a result of new prompts being added to Brand Radar, we compared ~25,000 prompts that appeared among the 150,000 highest-volume prompts in both March and September.

Every site in the chart moved in the same direction on those identical prompts.

The prompts in any set will affect which sites show up, but the pattern is consistent: ChatGPT seems to be prioritizing original sources over sites that summarize them.

Cited more	Cited less
Government health and research: PubMed, FDA, CDC, MedlinePlus, DailyMed, NIH, NHS	Health and finance explainers: WebMD, Healthline, Verywell Health, Investopedia, Livestrong
Regulators: SEC, EPA, FTC, CFPB, NHTSA, CPSC	Encyclopedias: Wikipedia, Britannica
Professional bodies: American Academy of Dermatology, American Academy of Pediatrics, American Kennel Club	Marketplaces: Alibaba and its subdomains, Accio
Brand sites and support pages: Apple, Google Support, Microsoft Support, Toyota, Lowe's	Syndicators and tabloids: Yahoo, AOL, New York Post, The Sun
Hands-on testers: RTINGS, CarComplaints, RepairPal	Low-quality content sites: flavor365, biologyinsights, enviroliteracy

If your content mainly summarizes information that someone else publishes first, ChatGPT may be more likely to cite the original source instead.

The pages holding up best are the ones only you can publish, like original research, hands-on testing, and your own product facts.

Analyzing fan-out queries in bulk shows you what ChatGPT checks before it recommends a brand in your industry, so you know what content to create and where to earn coverage.

You can do it in Ahrefs Brand Radar, and hand the pattern-finding to Ask Ahrefs.

Pull the fan-out queries for your whole industry at once

A single fan-out query tells you very little, so it helps to see many side by side to recognize a pattern.

In Brand Radar, you can study fan-out queries across ChatGPT and Perplexity.

Just head to the AI responses report, add a filter, and enter your topic as prompt or response text.

We’ve chosen “running shoes”.

Then filter the platform to ChatGPT and the location to your market, and look at the “Fan-out queries” column next to each prompt.

To study them in bulk, filter the report on the fan-out text, such as site:, “official”, “price”, “review”, or “best”. You can then study them in-platform or export to a spreadsheet.

When we analyzed “running shoes”, Brand Radar returned 1,474 US ChatGPT responses with fan-out queries.

“Best” appeared 206 times, “2026” 160 times, “review” 117 times, “vs” 105 times and “official” only 65 times.

So for running shoes, ChatGPT mostly hunts for recent, third-party reviews and head-to-head comparisons, which is a different picture from the overall data above.

A running shoe brand would get more from review coverage and comparison pages than from its product pages alone.

This is based on the existing prompts in the Brand Radar database, but you can also run this analysis on your own custom “Tracked prompts”.

Let Ask Ahrefs group fan-out queries for you

Ask Ahrefs can do the grouping and counting that would otherwise take hours in a spreadsheet.

For example, you can just paste in the prompt below:

Pull the ChatGPT fan-out queries from Brand Radar for my tracked prompts/prompts about [your category]. Group them by intent (pricing, specs, reviews, comparisons, how-to), count the most common terms and site: domains, and tell me which groups my site answers and which it doesn’t.

Then run the same prompt each month to see which query types are growing.

Ask Ahrefs will run a full analysis, telling you the breakdown of fan-out query by topic, how your site: presence compares to competitor domains, and advising you on which gaps to fill.

For instance, we need to increase our reviews if we want our Brand Radar tool to be mentioned more in “AI visibility tool” related responses.

Our data agrees with other research on the direction ChatGPT is heading: toward official sources, site: searches, and fewer citations.

The main difference is scale, since other studies found far more fan-outs and site: searches per prompt.

Study	Setup	Fan-outs per prompt	`site:` share
Ahrefs (this study)	GPT-5.6 Luna, free, logged out, 66.6M responses, all countries	~1.6 (responses with fan-outs)	6.8% of fan-outs
Chris Long, Nectiv	GPT-5.6 Sol via API, ~4,000 prompts	7.61	64% of queries
Wil Reynolds	GPT-5.6 Luna, small tracked subset	n/a	76% of fan-outs
Peec data	GPT-5.6 vs 5.5, sample size not disclosed, partly transactional prompts	56.5% of prompts had 2+	~23% of fan-outs
Suganthan Mohanadasan	Logged-in account, 57 conversations, SaaS-heavy	15 to 40 on Thinking	Frequent on commercial prompts

The prompts in our study are broad.

They stem from real search demand across every topic and country, while many other studies used commercial “best X” prompts that naturally trigger brand lookups.

We also tracked free, logged-out sessions, which search less than paid or reasoning models.

These numbers are a snapshot of one model between August and September 2026, and OpenAI changes how ChatGPT retrieves information constantly.

If you track fan-outs regularly, you can spot changes as they happen, rather than months later.

Most ChatGPT responses we tracked had no fan-out queries.

And, when it does search, it increasingly goes to whoever owns the information, like the brand that sets the price.

That leaves two ways into its answers: be well enough known to appear in its answers, or be the original source it finds when it searches.

Pages that only summarize someone else's work are currently the ones losing citations, so the pages worth investing in are the ones only you can publish, like your product facts or original research.

But, bear in mind, this could all shift again with the next model update, so keep a watchful eye on your own presence in fan-out queries and AI responses!

Post byLouise Linehan

Louise is a Content Marketer at Ahrefs. Over the past ten years, she has held senior content positions at SaaS brands: Pi Datametrics, BuzzSumo, and Cision. During this time, she has published hundreds of blog posts, spearheaded industry-leading research, and developed expert-led webinar programs.

Louise first learned the ropes of SEO during her time at enterprise SEO company, Pi. Within weeks of joining, she had published a piece on cannibalization that caught the attention of the biggest name in SEO at the time: Rand Fishkin. Since then, she has written on topics ranging from manipulative link building, to content syndication, and search demand forecasting. In her spare time, she has deepened her practical understanding of SEO by building websites for two local businesses.

You can read Louise's words in industry publications like Search Engine Watch, The Drum, The Telegraph, and Spin Sucks.

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