--- title: "Answer Engine Divergence Report | Brantial Research" description: "ChatGPT-Claude citation overlap is just 8% at domain level. A synthesis of cross-engine research on why each answer engine recommends different brands." source_url: "https://brantial.ai/reports/answer-engine-divergence-report/" lang: "en" --- REPORTS / ANSWER ENGINES · AUG 2026 # The Answer Engine Divergence Report: Do ChatGPT, Gemini, Claude and Perplexity Recommend the Same Brands? Marketers still speak of "SI visibility" as if it were one thing. The data says otherwise: at domain level, only 8% of the sources ChatGPT cites overlap with what Claude cites. Each answer engine runs its own retrieval universe: its own index, its own source preferences, its own content-format bias. This report synthesises the published cross-engine research and explains why a brand that wins one engine can be invisible on the next. [Measure your own category](https://brantial.ai/brandscore/)[Book a demo](https://brantial.ai/contact/) **8%**ChatGPT-Claude citation overlap at domain level **79.2%**Claude citations sourced from Brave top-10 results **95%**of ChatGPT answers use its own web index **17x**gap in community-content citation (ChatGPT 16% vs Claude 0.9%) **SCOPE** Cross-engine analysis · Global research, Türkiye implications **SAMPLE** 4 answer engines · 3 published studies · 2026 conference data “How visible is my brand in SI?” is becoming the wrong question. The right one is: _in which SI?_ This report brings together the cross-engine research published through 2026, most prominently the retrieval studies presented by Profound’s research team at Zero Click NY 2026, and reads it from one angle: whether the major answer engines are one market or four. The short answer: four. And that changes how visibility work must be planned, measured and budgeted. ## Methodology ### A synthesis, openly sourced This report is a research synthesis, not a Brantial panel measurement. Every figure below is drawn from published third-party research and conference presentations, cited inline. Where we add interpretation, it is labelled as such. The companion measurement on Brantial’s own Turkish prompt panel is planned as a follow-up study. ### Four engines, three lenses We compare ChatGPT, Gemini, Claude and Perplexity across three dimensions the published data covers: where each engine retrieves from (index architecture), which sources it prefers to cite (source ecosystem), and which content formats it lifts into answers (format bias). ## Finding 1: The engines barely cite the same web The most striking figure in the 2026 research: **at domain level, only 8% of the sources ChatGPT cites overlap with what Claude cites** (Josh Blyskal, Profound, Zero Click NY 2026). These are not two views of one index; they are two different webs. The architecture explains it. Claude leans on Brave’s search infrastructure: **79.2% of its citations come from Brave’s top-10 results**, while ChatGPT resolves **around 95% of retrieval through its own index**. Claude reaches for live web search in roughly one query out of three; ChatGPT almost always. Gemini and Google’s AI Mode sit on Google’s stack, which is why **Claude’s citation alignment with Google results (64%) is nearly double ChatGPT’s (37%)**, an irony worth reading twice: the Anthropic model tracks Google’s view of the web more closely than OpenAI’s does. **What this means for a brand:** a #1 organic ranking on Google is a meaningful signal for Gemini and partially for Claude, and a weak one for ChatGPT. Optimising “for SI” without naming the engine is optimising for nobody. ## Finding 2: Each engine trusts a different source ecosystem The divergence continues in _what kind_ of source gets cited. ChatGPT lifts community content (forums, Reddit threads, discussion boards) into **around 16% of its citations; for Claude the figure is 0.9%**, a seventeen-fold gap. Claude instead favours structured editorial: **roughly 36% of its citations are listicle-format pages** (Profound, Zero Click NY 2026). Layered on top: across engines, **fewer than 3% of citations go to tier-1 media**. The remaining 97% flow to niche publications, regional outlets and specialist sites (Blyskal; Tim Sanders/G2, same conference). The prestige placements PR teams fight for are not where answers are built. **What this means for a brand:** a Reddit presence moves ChatGPT and barely touches Claude. A well-structured “best X” listicle placement does the reverse. Source strategy has to be engine-weighted, one budget, split by retrieval behaviour. ## Finding 3: Behind one question, different sub-queries Divergence starts before retrieval. Each engine decomposes the user’s prompt into its own set of synthetic sub-queries (the fan-out layer) and the sets differ by engine and drift over time (Mike King, iPullRank, Zero Click NY 2026). Two engines answering the same question are often not even _asking_ the same questions underneath. This is why single-number “SI visibility scores” that average across engines can mislead: a brand can dominate the price-comparison branch on one engine while a competitor owns the reliability branch on another, and the average shows a tie. ## What this means in Türkiye The published research is largely US-centred. Three implications transfer directly to the Turkish market, and each is measurable: **Turkish source ecosystems are thinner, so divergence is sharper.** Where fewer authoritative Turkish sources exist per topic, each engine’s source preference weighs heavier, a single well-structured Turkish page can own an entire branch on one engine while being absent from another. **Community content is an open flank.** If the ChatGPT-cites-communities pattern holds for Turkish answers, the Turkish forum and review layer (Ekşi Sözlük, ŞikayetVar, sector forums) is a visibility channel most brands have never audited. **Consensus is the strongest signal available.** As our sector reports (coffee, women’s fashion, baby & maternity) consistently show, brands recommended by _all_ models measured carry the most durable visibility. In a divergent engine landscape, cross-engine consensus (not any single engine’s ranking) is the number worth managing. ## The takeaway There is no “SI search market”. There are engine-shaped markets: ChatGPT’s index-first, community-friendly web; Claude’s Brave-fed, listicle-leaning web; Gemini’s Google-aligned web; Perplexity’s citation-dense hybrid. A visibility strategy inherits this structure or fights it. The practical sequence: measure per engine, find where your citations actually come from on each, and work the source ecosystems separately. Averages hide the work; divergence _is_ the work. ## Sources - Josh Blyskal, Profound: retrieval architecture and citation-overlap research, Zero Click NY 2026 - Mike King, iPullRank: query fan-out and relevance engineering, Zero Click NY 2026 - Tim Sanders, G2: B2B buying behaviour and citation ecosystems, Zero Click NY 2026 - Brantial sector reports (2026) on Coffee, Women’s Fashion and Baby & Maternity: cross-model consensus methodology _Figures are as presented in the cited talks and publications; they describe primarily English-language, US-market behaviour. Brantial’s follow-up study will test each pattern on Turkish prompts across the engines in our panel._ ## Measure the same questions in your category Run this report’s questions for your own brand and competitors: your analysis credit takes about four minutes to set up. [Audit my brand](https://brantial.ai/brandscore/)[Talk to sales](https://brantial.ai/contact/) [← All reports](https://brantial.ai/reports/)