The Answer Engine Divergence Report: Do ChatGPT, Gemini, Claude and Perplexity Recommend the Same Brands?

“How visible is my brand in AI?” is becoming the wrong question. The right one is: in which AI? 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 AI” 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 “AI 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 “AI 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): Coffee, Women’s Fashion, 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.