Should You Optimize for Perplexity and ChatGPT Differently?

Should you optimize for Perplexity and ChatGPT differently?

Mostly no, but not entirely. The work that gets you cited is the same on both: clear answers, real expertise, pages a crawler can read. What differs is the kind of page each engine reaches for, and which bots you have to let in. Get the shared 90 percent right first.

We build websites for B2B companies, and this question now comes up in almost every kickoff call. Someone has read that ChatGPT and Perplexity pull from different places. They want to know if that means two content strategies, two teams, two budgets.

It does not. But the differences are real, and a few of them are settings on your server rather than words on your page. Those are the ones worth acting on this week.

Why do these two engines cite such different sources?

Because they fetch differently. Perplexity runs a web search on nearly every question and shows the sources it used. ChatGPT leans on what the model already knows, and reaches for the live web when the question needs it. Different retrieval habits produce different citation lists.

Profound published a study on 5 June 2025 covering 680 million citations tracked from August 2024 to June 2025. The split it found is stark. In ChatGPT, Wikipedia was the single most cited domain at 7.8 percent of all citations, and Reddit trailed at 1.8 percent. In Perplexity, Reddit led at 6.6 percent and YouTube followed at 2.0 percent. Google AI Overviews sat between them, with Reddit at 2.2 percent and YouTube at 1.9 percent.

Read that again, because it is the whole story in three numbers. Reddit is roughly three and a half times more prominent in Perplexity than in ChatGPT. One engine reaches for an encyclopedia. The other reaches for a forum thread.

How does ChatGPT actually pick the pages it cites?

ChatGPT cites a page when its search layer surfaces that page and the model decides the page answers the question. OpenAI runs a separate crawler for this, called OAI-SearchBot, and its job is only to surface sites in ChatGPT search results. Training is a different bot with a different name.

OpenAI documents four crawlers. OAI-SearchBot powers search. GPTBot collects content that may train foundation models. ChatGPT-User handles the case where a person asks ChatGPT to go look at a specific page. OAI-AdsBot checks the safety of pages submitted as ads. They are four separate user agents, and you can allow or block each one on its own.

That distinction matters more than most teams realise. A site can block GPTBot to keep its writing out of training data and still allow OAI-SearchBot, so it stays eligible to be cited in answers. Those are genuinely different decisions. We see plenty of sites that blocked everything with an OpenAI name in it and then wondered why they went quiet in ChatGPT.

How does Perplexity pick its sources?

Perplexity searches the live web for almost every query, so its index freshness matters more than a model's memory. It also runs two bots. PerplexityBot builds the search index. Perplexity-User fetches a page when a person's question sends it there. Only the first one is a crawler in the usual sense.

Perplexity's own documentation is direct about this. It states that PerplexityBot is designed to surface and link websites in search results, and that it is not used to crawl content for AI foundation models. It asks publishers to allow the bot in robots.txt and to permit its published IP ranges. Perplexity-User, by contrast, generally disregards robots.txt, because it acts on a person's explicit request rather than crawling on its own.

So Perplexity gives you a cleaner deal than most people assume. Allowing its index crawler does not hand your work to a training set, at least according to the company's own published guidance. Whether you find that acceptable is a business decision, not a technical one.

How much overlap is there between the two citation lists?

Less than most content plans assume. The domains that dominate one engine are often minor in the other, and the concentration sits with a handful of very large sites. For a B2B company, that means you are competing for the long tail of citations rather than the top of the list.

Profound's data also showed that .com domains took 80.41 percent of all citations, with .org sites at 11.29 percent. That is a useful sanity check. The web these engines cite is still overwhelmingly commercial, not academic. Your product pages and your blog are eligible. You do not need a research institute behind you.

The concentration at the top is the harder problem. When Wikipedia alone holds 7.8 percent of ChatGPT citations, every other site is fighting over what is left. This is the same shape as classic search, where a few giant domains soak up the head terms. If you have worked through how to get cited by AI search engines, the tactics carry over directly.

Which crawlers do you actually need to allow?

At minimum, the two search crawlers: OAI-SearchBot for ChatGPT and PerplexityBot for Perplexity. Those are the ones that decide whether your pages are eligible to appear as sources. The training crawlers, GPTBot in particular, are a separate call that depends on how you feel about your content training models.

Check your robots.txt today. We find blocked search bots on maybe one in four sites we audit, almost always because someone pasted a blocklist from a blog post in 2024 and never revisited it. The file takes two minutes to read and it is the single highest leverage thing on this list.

Also check that your pages render without JavaScript. Some of these fetchers are patient and some are not. If the text only appears after a client side render, you are gambling. Our own site prerenders every article at build time for exactly this reason. Our guide to controlling AI crawlers with robots.txt walks through the file line by line.

Does one content strategy work for both engines?

Largely yes. Both engines reward pages that answer a specific question early, use the plain terms people search with, and carry evidence a model can quote. A page built that way is legible to ChatGPT, Perplexity, Google AI Overviews, Claude and Gemini at once. You are optimising for how machines read, not for one brand.

Where they split is format. Perplexity's tilt toward Reddit and YouTube says that community answers and video transcripts earn citations there. ChatGPT's tilt toward Wikipedia says that encyclopedic, neutral, well sourced writing earns them there. If you only have time for one adjustment, make your pages read more like reference material and less like a sales deck.

The honest framing is that this is one strategy with two emphases. It is not two strategies. We have never seen a case where splitting a content team by answer engine paid for itself.

What should you change on your site this quarter?

Four things, in order. Unblock the search crawlers. Make sure pages render server side. Rewrite your highest value pages so each section opens with a direct answer. Then add the specifics a model can cite, meaning numbers, dates and named sources rather than adjectives.

That last one is where most B2B sites lose. A page that says the platform is fast gives a model nothing to quote. A page that says median load time fell from 3.4 seconds to 0.8 seconds gives it a sentence. We saw exactly that on the ION Clean Energy build, where load time went from 3.4 seconds to 0.8 seconds and the site now scores 98 on PageSpeed. Specificity is citable. Vagueness is not.

Do not rewrite everything. Pick the ten pages that already earn search traffic and fix those. The rest can wait for a normal content cycle.

How do you measure whether any of this works?

Through referral traffic and manual prompt checks, because neither engine gives publishers a proper analytics view yet. You watch for visits from the AI domains in your analytics, and you periodically ask both engines the questions your buyers ask, then note whether you appear.

The traffic that does arrive tends to behave well. Semrush's roundup of AI SEO statistics reports that Adobe found retail sites receiving AI referrals saw a 27 percent lower bounce rate and 38 percent longer visit duration in 2025, and that Momentic measured 1.4 external links clicked per ChatGPT visit against 0.6 from Google. Semrush's own 2025 figure puts an AI search visitor at 4.4 times the value of a traditional organic one.

Set expectations on volume, though. The same roundup cites Pew Research finding that 8 percent of users clicked a traditional link when an AI summary was present, against 15 percent when it was not. Fewer clicks, better clicks. Plan for that shape. We go deeper on the tracking setup in our piece on measuring AI search traffic.

Where does this split go from here?

Our read is that the engines converge on retrieval and stay split on taste. Every serious assistant now searches the live web, so the mechanical gap closes. What will not close is the editorial preference each one has learned, because that comes from different training and different licensing deals.

Which is oddly good news. Converging retrieval means one well built, fast, honest page keeps qualifying everywhere. Divergent taste means you should keep a presence in the places your buyers already talk, whether that is a community thread or a recorded demo, without pretending you can game either list.

If you want a second pair of eyes on whether your site is even reachable by these crawlers, we are happy to walk through it. That audit is usually a short conversation and a robots.txt file, not a project. Find us at phoenix.studio.