Do ChatGPT, Claude, Gemini and Perplexity recommend the same brands?
No. In our measured data the same brand was named by Gemini in 73% of answers and by Claude in 11% — a seven-fold spread on identical questions — and engines disagree on the order of brands, not just the amount. There is no such thing as AI visibility in the singular.
Updated 22 July 2026
The assumption behind most AI-visibility advice is that "AI" is one audience: get visible, and the assistants will follow together. The measured reality is stranger and more useful.
The size of the disagreement
In our July 2026 measurement of UK cigar retail — the same 37 buyer questions, sampled repeatedly across four engines — the most-visible merchant in the field was named by:
- Gemini in 73% of answers
- ChatGPT in 70%
- Perplexity in 28%
- Claude in 11%
Identical questions, a nearly seven-fold spread. And the engines do not merely disagree on magnitude — they disagree on order. ChatGPT named that leader in 70% of answers but its closest rival in just 16%; Claude inverted the ranking, naming the rival more than twice as often. A buyer using ChatGPT and a buyer using Claude hear about different shops.
Why they diverge
Each engine is a different mix of two ingredients. Its parametric memory — what its model absorbed in training — differs because the models were trained by different labs, at different times, on different corpora. And its retrieval differs because each assistant searches differently: different indexes, different source preferences, different appetite for citing at all. An engine that leans on live search behaves like a fast-moving news reader; one that leans on memory behaves like an encyclopaedia a year out of date. The same brand can be strong in one mode and absent in the other.
What this means in practice
For measurement: a reading from one engine is not "your AI visibility" — it is your ChatGPT visibility, and extrapolating it to the others is guesswork the data does not support. This is why a serious score measures across engines and names which ones each reading ran on.
For strategy: diversify the way you would across sales channels. Retrieval-led engines reward being citable right now; memory-led engines reward the slow accumulation of being written about. The work overlaps but is not identical, and a brand strong on one engine has not finished the job.
For interpretation: if a tool shows you one number with no engine breakdown, ask which assistant it measured — and remember the others may be telling customers a different story.
Run a free scan to see the readable half of your standing, or see how measured brands compare across engines on the index.