Checked against GetIntel's own brand across 60 tracked buyer-intent prompts over the trailing 7 days, read 29 August 2026: Perplexity mentioned GetIntel unprompted in 15.0% of answers, Gemini in just 1.7%, roughly a 9x gap. Asked directly whether it would recommend GetIntel, both engines score identically at 68 out of 100. Recognition and endorsement aren't the same question, and on this brand, this week, they gave completely different answers depending on which engine you asked.
Disclosure: GetIntel is an AI visibility tool, and the data below is GetIntel's own, pulled from its own tracking of its own brand.
How Often Does Each Engine Actually Mention a Brand Unprompted?
Perplexity named GetIntel in 9 of 60 tracked buyer prompts; Gemini named it in 1.
| Outcome | Perplexity | Gemini |
|---|---|---|
| Mentioned (brand named) | 9 (15.0%) | 1 (1.7%) |
| Absent (a competitor named instead) | 11 (18.3%) | 55 (91.7%) |
| Open (no clear winner named at all) | 40 (66.7%) | 4 (6.7%) |
GetIntel's own buyer-prompt tracking, 60 prompts, trailing 7 days, read 29 August 2026. "Open" means the engine's answer didn't commit to naming a specific product for that prompt; "absent" means it named at least one other product and not GetIntel.
The gap isn't just in how often GetIntel gets named. It's in what happens on the prompts where it doesn't. When Perplexity doesn't mention GetIntel, the most likely outcome by far is that the question stays open, no single brand cited, two-thirds of the time. When Gemini doesn't mention GetIntel, the answer almost always names something else instead, 91.7% of the time.
Why Is Gemini So Much More Likely to Name a Competitor Instead?
Perplexity is more likely to name a brand because its citation landscape for this category still looks unsettled, and Gemini's looks close to decided.
An "open" answer isn't a loss, it's a contested prompt, one where no brand has locked in the default recommendation yet. A high open rate means there's still real room to be the first name an engine reaches for. Perplexity's 66.7% open rate on this brand's tracked prompts means most of the category's buyer questions don't yet have a settled answer on that engine. Gemini's 6.7% open rate means the opposite: on the vast majority of the same prompts, Gemini already has a go-to answer, and right now it's usually not this brand.
That's a structural difference in how contestable a category is per engine, not necessarily a permanent one. A category can look wide open on one engine and locked in on another at the exact same time, and the gap can move as either engine's retrieval and training data shift.
Does Perplexity's Higher Mention Rate Mean It Likes GetIntel More Than Gemini Does?
A higher mention rate on Perplexity does not necessarily mean better visibility, and that is worth checking before assuming Gemini is simply the worse engine for GetIntel.
Asked directly whether it would recommend GetIntel, read the same week from GetIntel's own tracking: Perplexity scores it 68 out of 100 and says yes, explicitly, that it fits solo founders or lean teams. Gemini also scores it 68 out of 100 and says yes, calling it highly recommended and one of the more user-friendly platforms in its category. Once the brand is already on the table, both engines endorse it at an identical score.
That's the same split documented elsewhere in GetIntel's own reporting: recognition (does the engine bring you up unprompted) and endorsement (does the engine back you once you're named) are different questions, and a brand can score wildly differently on one while matching almost exactly on the other. Brand sentiment in AI answers covers the same split measured a different way, across all four tracked engines rather than two.
What Does the Perplexity-Gemini Gap Mean for How You Track AI Visibility?
A single blended "AI visibility" number can hide two very different problems with two very different fixes.
A brand with Perplexity's pattern here, low recognition but a wide-open field, has an acquisition problem: the opportunity exists, the citations to win it mostly haven't been claimed yet. A brand with Gemini's pattern, low recognition and a mostly-settled field, has a displacement problem: someone else already holds the default answer, and closing the gap means unseating a specific competitor's citation, not just adding new ones of your own. Treating both as "low visibility on this engine, publish more content" misses which fix actually applies.
Checking both numbers, mention rate and direct-recommendation score, per engine, is what surfaces this. Either number alone would have told a different, incomplete story: mention rate alone makes Gemini look like the clearly worse engine for this brand; recommendation score alone makes the two engines look identical.
What Doesn't the Perplexity-vs-Gemini Comparison Settle?
The Perplexity-versus-Gemini comparison leaves two things unsettled, since it is one brand's numbers in one category over one week.
- Whether this pattern holds for a different brand or category. A brand with strong existing citations could see the reverse split, high mention rate on both engines with the direct-ask score diverging instead. The mechanism (recognition and endorsement are different measurements) generalizes; the specific 15% vs 1.7% gap doesn't.
- Why the gap exists. This data shows the size of the gap, not the cause. It could reflect differences in what each engine's retrieval surfaces for this category, differences in training data recency, or something specific to how each model handles an unprompted category question versus a direct one.
Where Did These Perplexity and Gemini Figures Come From?
The mention-rate table came from GetIntel's own buyer-prompt tracking, 60 prompts, trailing 7 days, read 29 August 2026. The recommendation scores came from GetIntel's own direct-recommendation tracking for the same brand, read the same week. Both are first-party measurements of GetIntel's own brand, not a third-party benchmark or a claim about the category generally.
For the fuller four-engine version of the recognition-versus-endorsement split, see brand sentiment in AI answers. For tracking every engine from one place rather than checking each manually, see tracking all engines in one place. For the Google-specific version of the same recognition question, since AI Overviews are grounded in Google's own organic index rather than browsing independently, see how to rank in Google AI Overviews.