A marketing consultant recently told us she was thrilled because ChatGPT recommended her coaching practice for several key queries. She assumed that meant she was "visible on AI" and moved on. When she checked Perplexity a few weeks later, her brand was nowhere.
Same prompts. Same industry. Completely different recommendations.
This isn't an edge case. ChatGPT and Perplexity work differently under the hood, and those differences produce meaningfully different results. If you're only checking one platform, you're seeing half the picture.
How they actually work
ChatGPT primarily draws from its training data, supplemented by web browsing when it's enabled. Its recommendations lean heavily on what it "learned" during training: brand recognition, frequently cited tools, established players. It has a memory of the web from training, plus some real-time access.
Perplexity is fundamentally a search engine with AI synthesis. Every response is grounded in real-time web results. It crawls current pages, pulls in recent content, and cites its sources. The recommendations are only as current and accurate as the web pages it finds.
This creates a core tension. ChatGPT favors established, well-known brands because they're deeply embedded in its training data. Perplexity favors brands with strong, recent web presence because it's pulling live results.
The overlap is surprisingly low
When we ran the same set of relevant prompts through both platforms, the top recommended brand matched only about 42% of the time. Less than half.
For some categories, the overlap was even lower. Niche B2B tools, consulting specialties, and emerging product categories showed overlap rates around 25-30%. These are areas where there's no single dominant brand, so each platform's methodology leads to different picks.
Categories with strong market leaders showed higher overlap, around 60-65%. When there's an obvious answer (like Shopify for e-commerce platforms), both tend to agree.
But in the messy middle, where most businesses actually compete, the recommendations diverge significantly.
Where ChatGPT wins (for established brands)
If you've been around for a while, have strong brand recognition, and lots of content about your product across the web, ChatGPT tends to favor you. Its training data has absorbed years of reviews, blog posts, comparison articles, and forum discussions about established brands.
This is good news if you're a known player. Your history works in your favor, and ChatGPT's recommendations tend to be stable for recognizable brands.
The flip side? If you're a newer brand or a solopreneur who's been focused on doing great work rather than building web presence, ChatGPT might not know you exist yet. Its training data has a lag, and breaking through requires significant signal from third-party sources.
Where Perplexity wins (for newer brands)
Perplexity's real-time approach creates an opening for newer and smaller brands. If you've recently published strong content, gotten a review on a credible site, or been mentioned in a comparison article, Perplexity can pick that up almost immediately.
One example: a SaaS founder published a detailed comparison post on his blog, got it shared on a couple of industry forums, and within two weeks his tool was showing up in Perplexity recommendations for his niche. ChatGPT still wasn't mentioning him months later.
This makes Perplexity more dynamic but also more volatile. Your visibility can shift quickly based on what's currently ranking on the web. A competitor publishes a strong piece, and suddenly they're the recommended option instead of you.
The strategic implications
If your target audience uses both platforms, and most professional audiences do, you need visibility on both. The problem is that what works for one doesn't always work for the other.
For ChatGPT visibility: Focus on building long-term brand signals. Get mentioned on established review sites, comparison platforms, and industry publications. Build the kind of web presence that becomes part of ChatGPT's training data over time. This is a slower play, but the results tend to be more stable once you get there.
For Perplexity visibility: Focus on content that ranks well and is easy to find via web search. Perplexity essentially curates search results with AI synthesis, so if your content shows up in search, it's more likely to show up in Perplexity. Fresh, well-structured content with clear answers to specific questions performs well here.
For both: The overlap is where the fundamentals live. Clear positioning, genuine expertise, and third-party validation work across every platform. These aren't hacks for one system. They're the basics of being recommendable.
Don't forget the other three
ChatGPT and Perplexity get the most attention, but Claude, Gemini, and Grok each have their own patterns too. Claude tends to be more conservative in its recommendations, often providing caveats and noting that it can't verify current information. Gemini leverages Google's search infrastructure, which gives it a different data profile. Grok draws from X (Twitter) data in ways the others don't.
Each platform is a different lens on your brand. Being visible on one is good. Being visible across all five is what comprehensive AI visibility looks like.
This is exactly why multi-platform tracking matters. Tools like Mentionable track across all five major LLMs precisely because the single-platform view is incomplete. What you see on ChatGPT might not reflect what's happening on Perplexity, Claude, or the others.
What to do with this information
First, stop assuming that one platform represents your total AI visibility. Check multiple platforms, or use a tool that does it for you.
Second, identify where your gaps are. Maybe you're strong on ChatGPT but invisible on Perplexity, or vice versa. Each gap has a different fix. ChatGPT gaps usually mean you need more third-party brand signals. Perplexity gaps usually mean your content isn't ranking or isn't structured for easy extraction.
Third, track both over time. The platforms are evolving. ChatGPT is browsing the web more. Perplexity is building its own understanding of brands. The differences may narrow over time, or they may not. You need ongoing data to know.
The worst approach is checking one platform once and calling it done. The AI recommendation landscape is fragmented across platforms, volatile over time, and directly affecting how potential customers discover your business.
The brands that track across platforms will see the full picture. The ones that don't will be working with incomplete data and making decisions based on half the story.
