Each engine runs its own searches, reads different pages and learned from different data, so the shortlist depends on which one your buyer opens. In Jarred Smith's September 2026 test, in the 299 questions all four engines answered (ChatGPT, Claude, Gemini and Perplexity), they named the same brand first only 26% of the time. Being named by one engine tells you little about the others.
How often do AI engines agree on which brand to recommend?
Not often. Four 2026 studies all found that engines agree on a minority of brands, though each one measured agreement a different way, so the numbers aren't directly comparable.
| Study | Engines | Size | What they measured | Result |
|---|---|---|---|---|
| Jarred Smith, AI Recommendation Index (Sep 29-30, 2026) | ChatGPT, Claude, Gemini, Perplexity (API, web search on) | 1,503 answers, 25 categories | Same brand named first by all four | 26% |
| organikpi (Sep 2026) | ChatGPT, Copilot, Gemini, Google AI Mode | 1,920 answers, 160 prompts | Share of brands two engines both named | 43.6% |
| BusySeed (Aug 2026) | ChatGPT, Gemini, Claude | 14,951 prompts, one run each | Company named by all three | 2.8% |
| BrightEdge AI Catalyst (Apr 2026) | ChatGPT, Perplexity, Gemini, AI Mode, AI Overviews | Ten industries | Overlap of each pair's top 100 named brands | 36% to 55% |
The pairs spread wider than the averages. In the Jarred Smith index, ChatGPT and Perplexity named the same brand first only 41% of the time, the lowest of any pair. In organikpi's data, half of all recommended brands (49.8%) came from a single engine, and only 20.9% showed up on all four.
All four studies come from companies or consultants that sell SEO or AI visibility work. So does Named. Treat them as field data with a seller behind it, and check the method notes on each page.
Why do ChatGPT, Claude, Gemini and Perplexity give different answers?
Because each one builds its answer from a different pipeline. The engine turns your buyer's question into its own searches, pulls different pages, and mixes them with what its model already learned.
The search step is the first split. Google says AI Overviews and AI Mode use "query fan-out," "a set of concurrent, related queries generated by the model" (Google Search Central). Two engines given the same question end up running different searches.
Then there's training. Each model learned from its own data up to its own cutoff, so a company that got a lot of coverage before one model's cutoff can be well known to that model and barely known to another.
None of the engines promise a fixed ranking, and answers vary run to run.
Do the engines read the same sources?
No, and the source gap is wider than the brand gap. In organikpi's study, two engines shared only 3% to 10% of the domains they cited, even though they shared about 44% of the brands.
Each engine leans on different pages.
- In the Jarred Smith index, 65% of ChatGPT's citations pointed to the website of a brand named in that same answer. For Gemini it was 12%, Perplexity 10% and Claude 6%. Those three leaned on reviews and coverage instead.
- In organikpi's data, Google AI Mode cited Reddit in 50.4% of answers and YouTube in 52.9%. ChatGPT cited neither in any answer.
So the same brand can reach ChatGPT through its own pricing page and reach Perplexity through a review site. If you only fix one of those, you'll look strong on one engine and missing on the next. It's the same split covered in mentioned vs cited, where an engine names you but cites someone else's page.
Does the same engine give the same answer twice?
Often, but not reliably. The Jarred Smith index found the same engine named the same brand first 70% of the time when asked the identical question again. organikpi found a rerun kept 63.6% of the brands.
Exact lists almost never repeat. SparkToro and Gumshoe had 600 volunteers run 12 prompts 2,961 times on ChatGPT, Claude and Google's AI. They put the chance of getting the same list twice at under 1 in 100, and the same list in the same order closer to 1 in 1,000 (SparkToro, Jan 28 2026).
That's why results vary run to run, and why one screenshot proves very little in either direction.
Read how often cited pages drop out and come back.
Does the way a buyer asks change the brands?
Yes, a lot. The Jarred Smith index found "What are the best X options?" produced about 10 brands per answer, a use-case question about 6, and a compare-the-top-two question about 4.
Who's asking matters too. When the same software questions came from six different buyer profiles, the brand lists for two buyers overlapped 27%, against 48% for the same buyer asking twice. Gusto showed up in 82% of payroll answers for a US founder and in none for buyers in London or Toronto, which makes sense since Gusto serves US employers.
For a small business, the narrow questions are the opening. The list is short there, and the big names don't win by default. More on that in can a small business get recommended over big brands.
When do the engines agree?
When a category has a few leaders everyone writes about. In the Jarred Smith index, the leading brand appeared in at least 90% of answers in 19 of 25 categories, and all four engines shared the same most-mentioned brand in 14 of 25.
BrightEdge goes further and argues the engines agree more on brands than on sources, with brand overlap in a 36% to 55% band against 16% to 59% for sources. I think that's fair, and it fits the other data. The engines read different pages and often still name the same two or three leaders. They split on everyone after the leaders, and that's where most small businesses compete.
What should you track if every engine says something different?
Track each engine on its own, on a fixed list of buyer questions, asked more than once. A single blended "AI visibility score" averages away the one thing you need to know, which is where you're missing.
| Approach | What it shows | Where it falls short |
|---|---|---|
| One question, one engine, one screenshot | A single answer on one day | Can't tell a real change from a reshuffle |
| One blended score across engines | A trend line that's easy to read | Hides the engine that skips you |
| Fixed question list, each engine counted apart, repeated | How often each engine names you, and who it names instead | Takes more runs, and small samples still wobble |
I don't trust a report that hands you one number for "AI." Ask for the per-engine counts behind it. If they don't exist, the number isn't telling you much.
Named keeps one fixed prompt set and repeats it to check changes, on every AI engine you care about, starting with ChatGPT, Claude, Gemini, Perplexity, Grok and Muse. Each engine gets its own count, and results vary run to run, so we repeat each question before calling a change. The Namies draft fixes, and a person checks every change. The full rules are on our facts page, and buyer answers covers what a monthly report should include.
None of the studies above covered Grok or Muse, so there's no public data yet on whether they follow the same pattern.
FAQ
Which AI engine should I check first?
The one your buyers use. If you don't know, ask new leads how they found you. Keep in mind ChatGPT leaned on brands' own sites in the Jarred Smith index, while Claude, Gemini and Perplexity leaned on reviews and coverage, so the fix differs by engine.
Is being recommended by ChatGPT a sign Perplexity will recommend me too?
No. ChatGPT and Perplexity had the lowest first-brand agreement of any pair in the Jarred Smith index, at 41%. Check each one.
How many times should I ask the same question?
More than once per engine, and on a fixed list so you can compare over time. One run is a sample. SparkToro found exact lists almost never repeat, while how often a brand shows up across many runs held steadier.
Can anyone guarantee every engine will recommend my business?
No. No one controls what an AI engine says, and results vary run to run. Be wary of anyone who promises a spot.
Sources
- Jarred Smith, The AI Recommendation Index, Q4 2026: https://www.jarredsmith.com/research/ai-recommendation-index (data Sep 29-30, 2026; published Oct 2026)
- Daniel Shashko, organikpi, AI Engines Recommend Different Brands: 1,920 Answers Compared: https://organikpi.com/blog/geo-ai-search/ai-engines-different-recommendations/ (published Jul 13, 2026; updated Oct 2, 2026)
- BusySeed Research, Do ChatGPT, Gemini, and Claude agree on which companies to recommend?: https://busyseed.com/research/state-of-ai-search-2026-08/findings/do-ai-engines-agree-on-recommendations (data Aug 4-31, 2026)
- BrightEdge, Why AI Engines Cite Different Sources but Recommend the Same Brands: https://www.brightedge.com/resources/weekly-ai-search-insights/ai-search-same-brands-different-sources (Apr 24, 2026)
- SparkToro, AIs are highly inconsistent when recommending brands or products: https://sparktoro.com/blog/new-research-ais-are-highly-inconsistent-when-recommending-brands-or-products-marketers-should-take-care-when-tracking-ai-visibility/ (Jan 28, 2026)
- Google Search Central, AI optimization guide: https://developers.google.com/search/docs/fundamentals/ai-optimization-guide (last updated Jul 10, 2026)
Named works to get small businesses recommended by every AI engine you care about, starting with ChatGPT, Claude, Gemini, Perplexity, Grok and Muse. We keep one fixed prompt set and repeat it to check changes. The Namies draft fixes, and a person checks every change. If you want to see where you stand, start with a free AI audit.