Two numbers have followed GPT-5.6 around since August. Reddit's share of ChatGPT's citations fell 86%, and listicles lost half their share. Both are real measurements, and they don't mean the same thing. Reddit lost ground in absolute terms. Listicles mostly didn't, because ChatGPT started pulling far more sources per answer and their slice of a bigger pool shrank.
This page collects the ChatGPT SEO data from the GPT-5.6 switch in one place. Every number is labeled by what it measures, whether a share or a count and whether citations or retrieved pages, along with who measured it and when. Sources were checked on September 27, 2026.
The numbers at a glance
| Metric | Before | After | What it measures | Source |
|---|---|---|---|---|
| Reddit's share of ChatGPT Search citations | 3.83% | 0.52% | Share of citations | Promptwatch |
| Reddit pages retrieved (July = 100) | 100 | 31 | Count of retrieved pages | Peec AI |
| Listicles' share of retrieved pages | 15.47% | 8.19% | Share of retrieved pages | Peec AI |
| Listicles retrieved (July = 100) | 100 | 92 | Count of retrieved pages | Peec AI |
| All sources retrieved (July = 100) | 100 | 175 | Count of retrieved pages | Peec AI |
| Sources retrieved per prompt | About 12 | About 24 | Count | Peec AI |
| Product pages' share of retrieved pages | Behind listicles | 16.39%, the top type | Share of retrieved pages | Peec AI |
| Prompts answered with one fan-out search | 94.0% | 43.5% | Share of prompts | Peec AI |
| Fan-out searches using site: | 0.3% | 23% | Share of searches | Peec AI |
| Searches per response | 1.08 | 1.83 | Count | Promptwatch |
Peec AI's figures come from its own dataset, published by researcher David Konitzny on August 10, with the July-indexed counts added in a September 11 post on X. Promptwatch published its numbers on August 20. Both companies sell AI visibility tracking, as RankControl does, and their prompt sets differ, so compare directions across sources rather than exact decimals.
When GPT-5.6 arrived
OpenAI's ChatGPT release notes date the change to August 6, 2026:
The Free and Go part matters most for anyone tracking citations. Lily Ray pointed out that free users make up more than 90% of ChatGPT usage, so the new search behavior reached almost everyone within days.
| Date (2026) | What happened |
|---|---|
| August 6 | OpenAI announces GPT-5.6 Luna as the default for Free and Go users, and an updated GPT-5.6 Sol for Plus and Pro |
| August 8 | ChatGPT's use of the site: operator in its searches jumps, per Promptwatch |
| August 10 | Peec AI publishes its before-and-after retrieval data |
| August 14 to 17 | Reddit's share of ChatGPT Search citations averages 0.52% |
| August 17 | Lily Ray publishes a roundup of the fan-out research |
| September 3 | OpenAI introduces GPT-6 Astra to a limited set of organizations |
| September 11 | Peec AI's absolute counts separate the Reddit drop from the listicle dilution |
| September 25 | Peec AI reports YouTube's share of ChatGPT citations down 91% from August |
Reddit: an 86% drop that holds up in the counts
Promptwatch's tracking shows how fast it happened. Reddit's share of ChatGPT Search citations averaged 3.83% from July 18 to August 7, one of the largest shares of any single domain, and fell to 0.52% by August 14 to 17.
A share can fall for two reasons, so the count matters. Here it confirms the drop. Peec AI indexed weekly Reddit retrievals to their July average and found them down to 31, about two-thirds fewer Reddit pages, inside a pool of sources that had grown by three-quarters.
One AI visibility tracker measured the same story from the citation side, with ChatGPT's Reddit citations falling at least 73% in mid-August while ChatGPT's total citation volume rose 3.5%. That tracker saw Reddit citations across Google's AI features, Gemini and Perplexity move just 0.6% over the same days.
Google's own AI features did lose some Reddit share, only slowly. Promptwatch put the decline at about 11% for AI Overviews and about 31% for AI Mode over the summer, with no single break point.
Reddit also didn't vanish from ChatGPT's research. Lily Ray highlighted a finding from Suganthan Mohanadasan's network-traffic analysis in which Reddit made up 84 of 221 retrieved results in one test, and none of those Reddit URLs were cited. ChatGPT appeared to search Reddit after choosing brands, to check what people thought of them.
A few SEO/GEO data providers have shared this week that Reddit citations are collapsing in ChatGPT with the launch of 5.6, and after reading several articles and perspectives about this, I'd like to share my own version of what I think might be happening. First off, I highly
Lily Ray 😏@lilyraynycAug 21, 2026The May spike that came first
The July baseline hides an earlier swing. In the same September analysis, Konitzny traced a large Reddit peak in May to ChatGPT appending the word "reddit" to its fan-out queries between May 4 and May 25. That one word pushed Reddit's retrieval volume to 376 against the July baseline, nearly four times its normal level.
So Reddit's standing in ChatGPT has moved twice in one summer on changes to how queries are written, not on anything Reddit or its posters did. A Reddit program planned around May's numbers would have rested on a query pattern that lasted three weeks.
How people reacted
The news reached r/technology on August 22, where the thread passed 3,000 upvotes. The most upvoted comment welcomed it, complaining that search results had become full of Reddit links instead of real articles.
Reddit fades from ChatGPT citations
Our look at Reddit citations after ChatGPT 5.6 covers the mechanism in more detail, and our piece on what B2B marketers should do instead of chasing Reddit covers the response.
Listicles: half the share, about the same count
The listicle number needs more care than the Reddit one. In the data Konitzny published on August 10, listicles were one of the dominant page types before the switch, at 15.47% of all pages ChatGPT retrieved. After it, they were 8.19%, a relative drop of about 47%. Secondary write-ups rounded the drop to 50% and quote slightly different decimals, which may come from a later cut of the same data. We couldn't find those figures on Peec AI's own pages.
Then look at the counts. The same August data showed daily listicle retrievals down 9.8%. By September, indexed to July, listicles stood at 92 while total sources retrieved stood at 175.
We call this the pool effect: a source type's share falls because the pool around it grew, while the source itself is retrieved about as often as before. Konitzny put it plainly in his September post: listicles were diluted, while Reddit was dropped.
Other page types show the same pattern in reverse. Product pages became the most retrieved type, at 16.39% of retrieved pages, and their daily retrievals rose 119%, almost always from the brand's or manufacturer's own domain. How-to guides, comparison pages and homepages lost share too, yet their daily retrievals rose by 20% to 50%.
None of this makes listicles safe. A list that's retrieved as often as before now competes with twice as many other pages for the handful of spots in an answer. Our analysis of why AI search cites fewer listicles looks at what that does to citations, and our test of the content formats that still get cited shows which page types filled the gap.
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How ChatGPT searches now
The retrieval numbers make sense once you see how the searching changed. Before the switch, 94.0% of prompts in Peec AI's data were answered with a single fan-out search. After it, 43.5% were. Prompts that needed a second round of searching rose from about 5% to 33.5%, and the site: operator appeared in 23% of fan-out searches, up from 0.3%.
The API tells a similar story at a larger scale. Chris Long of Nectiv compared roughly 4,000 prompts on GPT-5.6 Sol with his own 2025 baseline and found fan-out queries per prompt rising from 2.17 to 7.61. The longest chain of searches went from 4 to 29, with site: in 64% of queries.
Why do the site: figures differ so much, 23% in one dataset, about 17% in Promptwatch's and 64% in Nectiv's? In her roundup of the fan-out research, Lily Ray pointed to collection method. Nectiv pulls fan-outs from OpenAI's API, which is clean and repeatable, while other tools extract them from the consumer app, which is closer to what real users get.
Her read of the pattern is that ChatGPT uses site: searches partly as a spam filter, sending factual questions to official brand domains, government sites and established review platforms. She also found a way to see some of it yourself: filter Search Console's queries for "site:" and look for repeated patterns. One site: query she found had about 197,000 impressions and exactly one click.
When site: guesses the wrong domain
The same roundup flagged a risk in this approach. Malte Landwehr found ChatGPT running site: searches against the wrong domain for lesser-known brands. In one test it kept searching census.com for a startup called Census whose real site is getcensus.com, while census.com was parked and for sale. A 2025 Netcraft study had already found that roughly a third of brand login links generated by AI tools pointed to domains the brands didn't own.
If your company shares a name with something else, make the official domain unmistakable. Lily Ray suggested revisiting whether "Official" or "Official Site" belongs in your homepage title tag or meta description, which helps the model match the brand to the right site.
Retrieved is not cited
Every retrieval number above counts pages ChatGPT read, not pages it cited. The gap between the two is large.
Mohanadasan's July study, from a single account and 57 conversations, logged 3,554 retrieved pages and found only 110 cited, about 3.1%. Brands named in ChatGPT's first fan-out query were cited 68.9% of the time, against 2.1% for pages that were only fetched. In 21 of 27 initial queries, ChatGPT had already added brand names the user never mentioned.
That's a small sample, which he states himself, but the direction matches larger data. The Resoneo team found unique domains cited per response fell from 19 to 15 after an earlier model update, even as retrieval grew.
The gap kept moving after August. On September 25, Konitzny reported that YouTube had lost 91% of its share of ChatGPT citations compared with August, and that its absolute citation count had fallen too, so it wasn't only dilution. Facebook, Wikipedia and Forbes lost between about 72% and 88% of their citation share over the same period.
Our guide to how rankings, citations and brand mentions work together covers why being used and being named are separate outcomes.
What this data can't tell you
A few limits apply to every number on this page:
- The main sources are AI visibility vendors with their own prompt panels. Their prompts, markets and categories differ, so treat agreement in direction as the signal.
- Most headline figures are shares. A share can fall while the count holds, as the listicle data shows, so ask for the count before acting.
- Retrieval data and citation data answer different questions. A page can be read on every search and cited on none.
- API studies and consumer-app studies measure different products. The app adds tiers, personalization and a model picker the API doesn't have.
- The window is short. GPT-6 Astra arrived for a limited set of organizations on September 3, and the September YouTube drop shows citation patterns are still moving.
We made a similar distinction for ChatGPT's traffic numbers in our piece on why ChatGPT's referral share fell in 2026, where a reversible product change looked like a lasting decline.
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How to read each kind of number in your own reports
Most AI visibility reports mix these measurements on one dashboard. Before reacting to a change, check which kind you're looking at:
| If your report shows | It tells you | Check next |
|---|---|---|
| Share of citations | How much of the cited space you hold | The total number of citations in the same period |
| Count of citations | How often you're linked as a source | Whether the questions tracked stayed the same |
| Share of retrieved pages | How much of what the engine reads is yours | Whether the engine now reads more pages per answer |
| Count of retrieved pages | How often the engine reads you | Whether reading turned into citations |
| Mentions | Whether answers name you | Whether the mention is accurate and near the top |
The listicle story is the reason for the third row. A report showing your listicles' share halving in August would have been accurate and still misleading, because they were read about as often as before.
What to do with these numbers
The data points to a few practical changes, none of them drastic:
- Put the facts buyers ask about on your own site in plain HTML. Pricing, features and specifications are what ChatGPT's site: searches look for on brand domains.
- Don't delete listicles because of a share chart. Check whether yours are still cited for the questions you care about, then stop making new self-ranked lists the center of your plan.
- If Reddit threads carried your ChatGPT visibility, re-run those questions now. Reddit still carries weight on Perplexity and Google's AI features, as our look at which Perplexity SEO advice from Reddit holds up shows.
- Track counts and shares separately in your own reports, and note the date of every model change next to them.
- Look for site: queries in Search Console and Bing Webmaster Tools, which is the closest thing to a ChatGPT query report you can get today.
The one-time audit is manageable. The ongoing part is catching the next change, since August's arrived without notice and September's YouTube drop did too.
Doing it yourself or handing it off
Done by hand, checking 50 questions on six engines takes about five hours a week, and publishing eight solid articles a month takes 16 to 24 hours more. Those are rough estimates.
RankControl's AI visibility tracking runs your 50 questions every week on six engines, marks each one as mentioned, cited or both, and records which competitors and sources each engine used. The content engine writes each article against the pages already ranking for its keyword and publishes it on your own domain.
You can follow these numbers yourself through vendor reports and a weekly spreadsheet of your own questions. Or RankControl's agents can check every engine each week and publish the product, pricing and comparison pages ChatGPT now searches for, while your team decides what to do about the next model change.
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