Home / Reports / State of AI Citations Q3 2026
Original research

The State of AI Citations, Q3 2026

0.195
mean pairwise overlap (conditional)
18.0%
run-0 share held by the top 10 domains
0.67
mean within-engine stability after 24 hours
384
engine-prompt observations
The short answer

In this dated dataset, four AI engines received the same 48 US-English commercial prompts twice, 24 hours apart, producing 384 engine-prompt observations. Among engine pairs that both returned sources, mean cited-domain overlap was 20%, and roughly four in five cited domains were unique to a single engine. In run 0, the ten most-cited hostnames accounted for 18.0% of publisher-domain occurrences (230 of 1,275). The residual other category accounted for 81.2%; this label does not establish publisher independence. Mean within-engine cited-domain stability across the two runs was 0.671, with substantial variation by engine. These estimates describe this prompt battery and these two run dates, not AI citation behavior in general.

Method and scope

The full prompt battery is listed below, and the exported domain observations are available as a downloadable CSV.

  • Battery: 48 prompts across 8 commercial categories (SaaS, Ecommerce, Health, Finance, Legal, Home Services, Education, Travel), spanning four buyer intents (category, comparison, problem, brand). No prompt mentions RankEcho or any site we operate.
  • Engines: Claude, Perplexity, Gemini, Google AI Overviews. OpenAI/ChatGPT was not included, so the findings do not describe that engine.
  • Sampling: every prompt x every engine, run twice, 24h apart (July 9, 2026 and July 10, 2026). 384 observations. Run 0 measures the cross-sectional questions; run 1 exists to measure stability.
  • Statistic: cited-domain sets are compared with Jaccard similarity (|A∩B| / |A∪B|). Cross-engine agreement is the mean pairwise Jaccard per prompt; stability is the mean Jaccard between the two runs of the same prompt and engine.
  • Analysis rules: engine-infrastructure redirects (e.g. unresolved grounding URLs) are excluded from every domain statistic and reported separately. Skipped observations are flagged and excluded from domain counts; the CSV does not disclose their reasons. Non-skipped observations with no exported domains are retained, but the CSV does not establish whether an answer was present.

Finding 1 — The top 10 domains accounted for 18.0% of run-0 citations.

The ten most-cited hostnames contributed 230 of 1,275 publisher-domain occurrences (18.0%) in run 0. Each hostname is counted once per prompt-and-engine observation. The denominator excludes 34 infrastructure occurrences, such as unresolved grounding redirects.

Reddit was the most-cited hostname, with 64 occurrences (5.0%). The residual other category contained 1,035 occurrences (81.2%). These are hostname-based classification labels: other includes hostnames that did not match another category, and does not verify ownership, independence, provenance or source quality.

Share of citations by source type (this study, run 0)
Other (residual)81.2%Reddit5.0%YouTube4.0%News3.2%Social3.2%Scholarly2.4%Review sites1.0%
View the source-category counts
Run-0 publisher-domain occurrences; denominator 1,275
Stored labelCountShare
Other (residual)1,03581.2%
Reddit645.0%
YouTube514.0%
News413.2%
Social413.2%
Scholarly302.4%
Review sites131.0%
Total1,275100.0%

The source counts were recalculated from the CSV retrieved on September 13, 2026. The study dates remain July 9–10, 2026; this section uses run 0. Several hostnames tie at the top-ten cutoff. Alphabetical tie-breaking selects ten names without changing their combined count.

Interpretation for this dataset: Run-0 citations span 670 distinct hostnames. The domain categories do not establish how many independent publishers or original sources produced the cited material. This distribution does not estimate citation concentration for other prompts, categories, locales, or dates.

Finding 2 — Conditional mean cited-domain overlap was 0.195.

Across identical prompts in this study, mean cited-domain overlap was 0.195 - about 20% - when both engines in a pair returned sources. When a comparison with an absent source answer was assigned zero overlap, the pooled mean was 0.138. Roughly four of five cited domains in the observed responses were unique to one engine. The run-specific conditional means were 0.195 on the first date and 0.201 on the second; their similarity is descriptive, not an independent replication.

Pairwise cited-domain overlap (conditional Jaccard, run 0)
ClaudeClaude—0.1950.1860.180PerplexityPerplexity0.195—0.1880.295GeminiGemini0.1860.188—0.129Google AI OverviewsGoogle AI Overviews0.1800.2950.129—
Pairwise Jaccard similarity of cited-domain sets. Darker = more agreement. Higher is more similar.

Interpretation for this dataset: A blended result would conceal the engine-level differences observed in this battery. Per-engine results are therefore more informative for these observations, but the study does not establish how strongly the engines differ for other prompts or dates.

Finding 3 — Among observed pairs, Gemini and Google AI Overview had the lowest overlap.

Among the available paired comparisons in this study, Gemini and Google's AI Overview had the lowest conditional mean cited-domain overlap at 0.129. Google AI Overview and Perplexity had the highest observed pairwise value (0.295). These rankings describe the available comparisons only; the study did not test why the systems differed.

Caveat, stated up front: the AI Overview appeared for a minority of queries, so this pair rests on 15 comparisons. Treat it as a narrow, high-uncertainty result rather than a settled cross-system pattern.

Finding 4 — Mean within-engine stability after 24 hours was 0.671.

Across matched prompt-engine observations on the two study dates, mean cited-domain-set stability was 0.671. The engine-specific values varied: Perplexity and Claude were near 0.77, while Gemini was 0.361. These values compare two runs 24 hours apart and do not measure longer-term volatility.

Within-engine cited-domain stability after 24 hours (two study runs)
Claude0.77Perplexity0.77Gemini0.36Google AI Overviews0.62

Interpretation for this dataset: A single run would omit the day-to-day variation observed in this battery. The two-run design cannot estimate longer-horizon variation or prescribe a monitoring cadence for other prompts. This full cited-domain-set Jaccard statistic is not manual exact-URL citation persistence across consecutive post-change windows.

Finding 5 — AI Overview appeared for 40% of this study's prompts.

Within this 48-prompt battery, Google's AI Overview appeared for 40% of prompts. No AI Overview was recorded for the remaining prompts. When one appeared, it cited a mean of 7.7 sources. The report therefore keeps appearance rate separate from citation count conditional on appearance. Neither estimate describes AI Overview coverage for commercial queries beyond this battery.

EngineAnswered with sourcesMean citations (when it did)
Claude100%9.19
Perplexity100%8.27
Google AI Overviews40%7.74
Gemini83%7.25

Limitations

Read these before citing anything above.

  • 48 prompts across 8 categories is a probe, not a census. US-English, consumer-commercial intent only.
  • Two runs, 24 hours apart, one week. Stability over longer horizons is unmeasured.
  • Four engines. OpenAI/ChatGPT is outside this dataset.
  • Finding 3 (Gemini vs AI Overview) rests on 15 paired observations. The observed ranking is descriptive, not definitive.
  • We measured which domains are cited, not whether a brand was named in prose without a citation. Answer-text brand mentions are outside this dataset.
  • AI engines change without notice. This dataset is a dated snapshot of two runs across one week; it does not measure longer-horizon patterns.

The data

The CSV contains prompt IDs, categories, intents, engines, run numbers, skipped flags, cited hostnames, source labels and exported positions. Positions are indices in the exported domain list, rather than ranks of original source URLs. Reuse the data under CC BY 4.0 with attribution; the prompt battery below supplies the study questions.

The September 13 data check retained 2,764 rows representing all 384 planned observations, including 16 skipped observations and 54 non-skipped observations without domains. The CSV omits answer text, skip reasons, model versions and original URLs. A no-domain row therefore cannot distinguish an absent answer from an answer without citations.

Identify the CSV used for the source-count update

The downloaded snapshot was 181,445 bytes. Its SHA-256 checksum is c4235cbd3ef858dd9a09fa8e201c74aaf5f724ab36a866d2091a2e2162726fd6. The download below serves current study rows and may change; a matching checksum identifies the exact bytes used for this update.

Download the dataset (CSV)

The full 48-prompt battery
  1. best project management software for small teams
  2. best customer support helpdesk software
  3. Notion vs Asana for product teams
  4. Stripe vs PayPal for subscription billing
  5. how do I stop my SaaS trial users from churning
  6. is Linear worth it for issue tracking
  7. best ecommerce platform for a small business
  8. best running shoes for flat feet
  9. Shopify vs WooCommerce for a first store
  10. Dyson vs Shark vacuum which is better
  11. how do I reduce cart abandonment on my online store
  12. is Allbirds actually sustainable
  13. best online therapy platforms
  14. best free meditation apps
  15. BetterHelp vs Talkspace which is better
  16. creatine vs protein powder for beginners
  17. how do I fix my sleep schedule after night shifts
  18. is Noom effective for weight loss
  19. best high yield savings accounts
  20. best budgeting apps
  21. Vanguard vs Fidelity for index funds
  22. Roth IRA vs traditional IRA which should I choose
  23. how do I pay off credit card debt fastest
  24. is Wealthfront a good robo advisor
  25. best online will making services
  26. best LLC formation services
  27. LegalZoom vs Rocket Lawyer
  28. LLC vs S corp for a small business
  29. how do I trademark a business name myself
  30. is LegalZoom trustworthy for incorporation
  31. best home security systems without a contract
  32. best pest control companies
  33. SimpliSafe vs Ring alarm
  34. heat pump vs furnace for a cold climate
  35. how do I find a plumber I can actually trust
  36. is Angi worth using to find contractors
  37. best online coding bootcamps
  38. best language learning apps
  39. Coursera vs Udemy for career change
  40. Duolingo vs Babbel for Spanish
  41. how do I learn data analysis without a degree
  42. is Codecademy Pro worth the money
  43. best travel credit cards for beginners
  44. best travel insurance for international trips
  45. Airbnb vs hotels for a family trip
  46. Booking.com vs Expedia which is cheaper
  47. how do I find cheap flights last minute
  48. is Going (formerly Scott's Cheap Flights) worth it

Method note: this report puts its bounded answer before the detailed method and uses Dataset and FAQ structured data. Those presentation choices do not affect the calculations.

Frequently asked questions

How was cross-engine agreement calculated?

For each prompt, we took the set of domains each engine cited and computed the Jaccard similarity between every pair of engines, then averaged. The conditional figure (0.195) counts only pairs where both engines cited at least one source; the pooled figure (0.138) assigns zero overlap when one engine returned no sources.

Why only four engines and not ChatGPT?

The dataset covers Anthropic (Claude), Perplexity, Gemini, and Google's AI Overview. OpenAI/ChatGPT was not included, so the findings do not describe that engine.

What does 'citation stability' mean?

We ran the identical battery twice, 24 hours apart, and measured how much each engine's cited-domain set for a given prompt overlapped between the two runs. Overall stability of 0.671 is the mean Jaccard overlap across those matched observations; it does not translate directly into a percentage of sources changed.

Is this enough data to draw firm conclusions?

It is a probe, not a census: 48 prompts, 8 categories, US-English, and two runs 24 hours apart. Similar run-level agreement values on two adjacent dates do not constitute independent replication, and the smaller cuts - especially Gemini versus AI Overview at n=15 - remain high-uncertainty descriptions of this dataset.

Can I reuse the data?

Yes, under CC BY 4.0 - reuse freely with attribution. The full per-citation dataset and complete prompt battery support inspection of these calculations and independent reruns; model, configuration, and date changes may produce different observations.

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