Intro
The phrase “being cited by AI” hides a real problem: every engine cites differently.
ChatGPT, Perplexity, Gemini and Google AI Overviews do not select sources the same way. They do not weigh them the same way once selected. They do not refresh them at the same cadence. They do not behave the same way across languages. A page that wins in one engine can be invisible in the next.
This page sets out what is now well documented about engine-specific citation behavior, what it implies for brands, and how Capston Core measures it.
Test your brand across engines
The breadth vs depth divergence
Two recent research lines converge on the same point.
Chen, Wang, Chen and Koudas (2025) document that AI search services differ significantly from each other in domain diversity, freshness, cross-language stability and sensitivity to phrasing. The implication is direct: a single GEO strategy applied uniformly across engines is structurally suboptimal. Engine-specific and language-aware approaches are required.
Zhang, He and Yao (2026) add a second axis. Perplexity cites the most sources on average per prompt. Google also cites broadly. ChatGPT cites fewer sources, but the pages it does cite carry substantially higher average citation influence on the final answer. In other words: Perplexity selects widely, ChatGPT absorbs deeply, Google sits in between with broad but conservative behavior.
Breadth and depth are not interchangeable. A brand can appear inside a Perplexity citation list without meaningfully influencing the answer text. A brand can be one of three citations in a ChatGPT response and shape most of the wording. The two are measured differently and optimised differently.
ChatGPT: fewer sources, deeper influence
ChatGPT tends to cite a smaller set of sources per answer and lean heavily on the ones it picks. The pages that get pulled in tend to carry more weight in the wording, the framing, and the final recommendation.
What this implies for a brand:
- Source authority matters more than source count. Being one of three cited pages with strong semantic alignment beats being one of fifteen with weak alignment.
- On-page clarity gets absorbed. ChatGPT-style citation influence rewards pages that state facts cleanly, in short paragraphs, with stable entity language.
- Conflicts get inherited. If the cited page contradicts itself, the answer inherits the contradiction. Internal consistency is a citation quality factor, not just an editorial preference.
- Brand pages have a real chance. A well-structured brand page can become one of the few absorbed sources, provided the evidence layer supports it.
The optimisation target is depth of influence per citation, not raw mention count.
Perplexity: broad selection, less depth
Perplexity is the broadest citer of the four engines. It surfaces more sources per prompt, exposes them visibly, and lets the user click through. Its citation list functions as a reading list as much as an answer.
What this implies for a brand:
- Breadth of presence matters. Being in the citation list at all has value, even when the answer text borrows little from your page.
- Click-through behavior re-enters the picture. Unlike ChatGPT, Perplexity users read source titles and click. Title, URL slug and first lines need to read like a useful answer, not just rank like one.
- Domain diversity cuts both ways. Perplexity’s wider net means competitors, aggregators and OTAs are also pulled in. Visibility without context can route demand away from the brand.
- Freshness signals are picked up faster. Updated dates and recent on-page activity tend to be rewarded.
The optimisation target is share of citation list, with strong on-click experience.
Google AI Overviews and Gemini: broad and conservative
Google AI Overviews and Gemini cite broadly, closer to Perplexity than to ChatGPT on volume. The difference is editorial: the cited sources skew toward established domains, official entities and structured data sources. The selection is broad but conservative.
What this implies for a brand:
- Entity hygiene is decisive. Brand entity, location entity, person entity. Stable across the site, stable across Wikidata and structured markup, stable across languages.
- Conservative selection favours official surfaces. Brand-owned domains, verified profiles, and primary sources sit higher than secondary commentary.
- Cross-language stability is rewarded. Brands whose entity description holds across EN/FR/DE/IT/ES tend to surface more often than brands whose multilingual content drifts.
- Authority is durable here. Once a brand is in the citation pattern, it tends to stay until the entity layer breaks.
The optimisation target is being treated as a primary entity, not a candidate one.
How to test engine differences yourself
A practical, repeatable test sequence:
- Pick ten prompts. Mix discovery, comparison, trust and conversion intents.
- Run each prompt across the four engines. Same wording, same day, same market.
- Capture the cited URLs. Not the answer text only — the citation list.
- Count three things per engine. Number of unique sources cited, share of citations on brand-owned domains, share of citations on competitor or aggregator domains.
- Reread the answer. Mark which cited source most clearly shaped the wording. That is your proxy for citation influence.
- Compare across engines. Differences in breadth and depth will appear quickly.
This is the minimum viable version of what the Capston Core methodology does at portfolio scale, with locked prompt sets, dated captures and a structured evidence layer.
How this fits into Capston Core
Engine-specific citation behavior is one of the reasons AI visibility scoring treats each engine as a distinct measurement surface rather than a single aggregate score. The same brand can be strong on Perplexity, average on Google AI Overviews and weak on ChatGPT — and the corrective work is not the same in each case.
Capston Core captures, dates and stores answers per engine. The score reflects engine-specific reality. The recommendations are sequenced engine by engine.
→ Back to Capston Core
FAQ
Which engine matters most for premium brands?
It depends on the market and the buying journey. In hospitality, Perplexity and Google AI Overviews tend to weigh heavier on discovery; ChatGPT often shapes trust and comparison phases.
Is it worth optimising for ChatGPT if it cites fewer sources?
Yes. The cited sources carry higher citation influence per page, so a single inclusion can shape the wording of many subsequent answers.
Do Gemini and Google AI Overviews behave identically?
They share architecture but not selection logic. They overlap heavily on official sources and diverge on long-tail prompts.
Does language change citation behavior?
Yes. Cross-language stability is uneven across engines, which is why bilingual brands need parallel measurement, not a translated single-language score.
Final CTA block
See how each engine cites your brand.
Test your brand across engines
Read the methodology
References
- Chen, Y., Wang, R., Chen, L. & Koudas, N. (2025). Generative Engine Optimization: How to Dominate AI Search. arXiv:2509.08919v1.
- Zhang, K., He, X. & Yao, J. (2026). From Citation Selection to Citation Absorption: Measuring Source Influence Across AI Search Engines. arXiv:2604.25707v2.