The short version
- The same brand can be everywhere on one engine and nearly invisible on another. Citation rates diverge enormously by engine.
- The sources behind answers barely overlap: 2026 analyses found only a small share of domains cited by both ChatGPT and Perplexity.
- So a single blended AI-visibility score misleads. You can win one engine and lose another and never see it in the average.
- Track and optimize per engine, because the playbook that moves one often does little on the next.
Ask a marketing dashboard "how visible are we in AI" and it will usually hand back one number. That number is close to useless, because AI visibility is not one thing. It is a different thing on every engine, and in 2026 the gaps between them stopped being a footnote and became the whole story.
The figures are striking. One analysis of tens of thousands of AI responses found brand citation rates differing by roughly 46 times between engines, with one engine citing brands well under a percent of the time and another well into double digits. A separate cross-platform study reported citation-volume differences of a few hundred times for the same brand, a company that dominated one engine's citations was almost absent from another. And when researchers looked at the source domains behind answers across hundreds of millions of citations, only around a tenth were cited by both ChatGPT and Perplexity, with most sources appearing on a single engine. One study of a handful of B2B brands put it bluntly: the per-engine profiles were so different they looked like different brands.
The same brand, seen through different engines. Note which engines cite each source: the sets barely overlap. A blended score would flatten all of this into one misleading number. Illustrative data.
Why the engines diverge
The divergence is not noise, it is architecture. Each engine has its own citation logic and its own preferred sources. Perplexity is built to answer from real-time web retrieval, so it cites heavily and its citations move with what is freshly on the web. ChatGPT constructs more of its answer from knowledge baked into training, so it cites the live web less and leans on what it already "knows." Google AI surfaces lean on what Google already trusts. Same question, three different machines deciding who to name and where to source it.
Because the engines source answers differently, the playbook that moves Perplexity can do almost nothing on ChatGPT.
There is one steadying nuance worth knowing: the brands an engine names tend to be more consistent across engines than the sources it cites to get there. Recommendation is steadier than the source mix. But the source mix is exactly where you earn your way in, and that is what diverges wildly. So the divergence matters most for the work of getting cited, which is where GEO effort actually goes. For the citation-versus-recommendation distinction, see being cited is not being recommended.
Why a blended score is dangerous
A single AI-visibility number does three harmful things. It hides where you are losing, because a strong showing on one engine can mask near-invisibility on another. It misdirects effort, because you cannot tell which engine to work on. And it mismeasures your wins, because an optimization that moved one engine gets diluted into an average that barely twitches, so it looks like nothing worked when in fact one engine improved a lot.
The buyer does not experience a blended average. They experience whichever engine they happen to use. If your category skews to Perplexity and you are strong there, a mediocre blended score understates you. If your buyers use ChatGPT and you are weak there, a healthy blended score is lying to you.
What to measure instead
- A separate number per engine. Citation rate and share of voice for ChatGPT, Claude, Gemini, Perplexity and Google AI, each with its own sample and confidence interval, not averaged together.
- The source set per engine. Which pages each engine cites for you, since they barely overlap. The sources to earn on Perplexity are not the ones that matter on ChatGPT.
- Where your buyers actually are. Weight your attention by the engines your market uses, not by a democratic average across engines.
- Change per engine, over a window. So a win on one engine shows up as a win, instead of vanishing into a blended line.
How llemmy helps
llemmy never collapses AI visibility into one number. It reports ChatGPT, Claude, Gemini, Perplexity and Google AI separately, each with its own citation rate, share of voice, average position and the specific sources that engine cites about you, every figure carrying a sample size and a 95% confidence interval and refreshed daily. So you can see at a glance that you own one engine and are invisible on another, target the source set that matters for each, and watch a per-engine win register as a win instead of drowning in an average. The blended headline is there if you want a rough summary, but the decisions come from the per-engine view, which is the only one that matches what your buyers actually see. Run a free GEO audit or start tracking free to see your visibility on each engine, separately.
FAQ
Do AI engines cite the same sources?
Mostly not. Large 2026 analyses of AI citations found only a small share of source domains are cited by both ChatGPT and Perplexity, and the majority of sources appear on just one engine. Each engine has different citation logic and source preferences, so the set of pages behind an answer is very different from one engine to the next.
Why is my brand cited on Perplexity but not ChatGPT?
Because the engines work differently. Perplexity leans heavily on real-time web citations, while ChatGPT constructs more of its answer from knowledge baked into training. Reporting in 2026 found brand citation rates differing by an order of magnitude or more between engines. A brand that dominates Perplexity's citations can be nearly absent from ChatGPT for the same query.
Should I track AI visibility as one number?
No. A single blended AI-visibility score hides the thing you need to act on, because your position on each engine can be completely different. Track each engine separately, with its own rate, sample size and confidence interval, and read the blended figure only as a rough headline, never as the basis for a decision.
Does optimizing for one AI engine help on the others?
Only partly. Because the engines cite different sources and weigh signals differently, the playbook that moves a citation-heavy engine like Perplexity often produces little movement on ChatGPT. Some foundations help everywhere, like being clearly readable and corroborated across the category, but you still need to measure each engine to know where you actually stand.
By the llemmy team, July 2026. Grounded in 2026 cross-platform research on how AI citation rates and source sets differ by engine, whose figures vary by study, category and method and should be read as directional. Related reading: What AI engines actually cite, Cited is not recommended, and How to track your brand across AI engines.