ChatGPT Searches Like an Analyst. Perplexity Searches Like a Shopper.

TL;DR

• ChatGPT and Perplexity do not search the web the same way. When given identical scenarios, they produced 300 search queries with only 1 literal match.
• Perplexity behaves like a real-time, comparison-driven search engine.
• ChatGPT behaves like an analyst that investigates context before answering.

These differences mean that visibility in one AI system does not translate to visibility in another, a fundamental shift for SEO, content teams, and brand leaders.

LLMs are searching the web in real-time

Large Language Models don't simply produce responses, they analyze user's questions, search the web, reason and summarize. Those searches reveal something far more interesting than the final response: they expose how each model thinks, what it prioritizes, and how it decides what sources are worth consulting. In this analysis, we look beyond surface-level outputs and focus instead on the information-seeking behavior of two popular AI systems: ChatGPT and Perplexity. Using Genezio's AI Visibility platform, we ran the same banking-industry conversational scenarios against both systems and extracted the actual web search queries each model executed while forming its responses. By analyzing these queries side-by-side, we can observe:

• what kinds of information each model looks for,
• how they frame their searches,
• and where their priorities meaningfully diverge. The goal of this article is not to evaluate which system is "better," but to understand how they differ, and what those differences mean for brands, content teams, SEO and GEO strategies aiming to be visible in AI-generated answers. Let's dive in.

Headline finding: almost no overlap in search queries

Despite being tested on the same topics and the same conversational scenarios, ChatGPT and Perplexity produced almost entirely disjoint sets of web search queries.

• Exact overlap in the dataset: 0 queries
• Literal string overlap across both sets: 1 query ("UK banks low interest personal loans fast application")

Out of 300 total queries (150 per platform), only a single query appeared in both lists. At first glance, this might seem surprising. After all, both systems were asked to reason about the UK banking landscape.

But this result highlights a key insight: similar topics do not imply similar search behavior.

Both assistants are often trying to answer the same underlying questions—which bank is best, which is most trusted, which offers the best experience—yet they translate those intents into very different search strategies. ChatGPT tends to decompose questions into longer, more descriptive queries that explore context and trade-offs. Perplexity tends to compress intent into shorter, more direct, and often year-anchored queries optimized for fast comparison.

The lack of overlap doesn't mean the systems disagree. It means they approach discovery differently. For brands and content teams, this has an important implication: visibility in one AI system does not automatically translate to visibility in another.

Query style differences (quantified)

The following table breaks down the structural differences between the two models:

Perplexity: freshness-first, listicle-shaped search behavior

Perplexity tends to formulate short, high-signal queries that look like classic SEO list pages, strongly anchored to time relevance.

Example Perplexity-style queries:

• "best UK banks for customer service 2025"
• "top UK mortgage lenders 2024"
• "best digital banks UK 2025"
• "UK bank customer satisfaction rankings 2024"

This behavior signals that Perplexity is optimized to quickly surface up-to-date, comparative content that can be cited and summarized with minimal additional reasoning.

ChatGPT: context-first, investigative search behavior

ChatGPT, by contrast, issues longer and more descriptive queries that aim to understand why something is true, not just what ranks highest.

Example ChatGPT-style queries:

• "which UK banks have the best complaint handling and fastest response times"
• "strengths and weaknesses of major UK retail banks digital experience"
• "UK banks trust transparency ethics reputation comparison"
• "review of UK banks customer support quality and escalation process"

This pattern indicates that ChatGPT is searching in order to build an explanation, not just retrieve a list.

Why this distinction matters

Although both systems may answer similar user questions, they arrive there through very different paths:

1. Perplexity looks for fresh, structured answers it can quickly quote.
2. ChatGPT looks for contextual evidence it can reason over.

For content creators, this means visibility in one system does not automatically guarantee visibility in the other, even when the underlying topic is the same.

If an LLM's search queries are short + year-based + "best/top", it will preferentially surface fresh, explicit, list-like content that is updated frequently. If queries are long + investigative + "why/which/strengths", it will preferentially surface deep explanatory pages, reports, and "how/why" content that answers nuanced sub-questions.

This is why "ranking pages updated for 2025" can crush on Perplexity, while "deep-dive: complaint handling practices in UK banking" can be disproportionately discoverable through ChatGPT's browsing behavior.

What this means for content teams

How to write content for ChatGPT.com

Based on the query patterns, optimize for investigative, context-rich retrieval:

• Write "why + how + tradeoffs" pages: ChatGPT asks "why is X strong", "strengths", "review", and "complaint handling". Create pages like "How UK banks handle complaints (process, timelines, escalation paths)" or "Digital onboarding in UK banking: security vs friction tradeoffs".
• Answer composite questions on one page: ChatGPT queries often bundle multiple facets (branch + digital, SME tools + advisory). Use clear H2s that map to those facets so the page can satisfy multi-intent retrieval.
• Include decision frameworks: Since ChatGPT uses "which" more, it benefits from content that supports selection, such as comparison matrices, "if you're X, choose Y" rules, and pros/cons lists.
• Be explicit about "strengths" and "limitations": Queries literally include "strengths", "review", "report". Add sections like "Where this bank is strong" / "Where it's weaker" with supporting evidence.
• Evergreen > year-stamped: ChatGPT uses years far less than Perplexity. Don't rely only on "2025" SEO. Make content that remains relevant even if the year is removed from the query.

How to write content for Perplexity

Perplexity's query style screams: freshness, rankings, and citations.

• Maintain year-specific landing pages: ~79% of Perplexity queries include a year. Create and actually update pages like "Best UK banks for customer service (2025)" or "UK bank customer satisfaction rankings (2025/2026)".
• Make list content extremely scannable: Short queries imply that the engine wants quick extraction. Use tight intros, bullet lists, tables, and "Top picks" boxes.
• Cite primary sources and name them: Perplexity includes "survey", "index", and "rankings" language more often. Put the source names directly on-page (e.g., "Based on [Survey/Index X], updated on October 2025…"). Even better, include a "Methodology" section so it can be quoted.
• Optimize for "best/top" wording: Perplexity starts with "best" ~27% of the time. Make sure your headings mirror that language (e.g., "Best for cashback", "Best for SMEs").
• Refresh cadence matters: If Perplexity keeps asking for 2025/2026, stale pages lose. Add "Last updated" timestamps, updated tables, and change logs.

Conclusion: Two models. Two search worlds.

AI search is fragmenting. The same topic triggers different searches, retrieves different sources, and shapes different narratives across systems. For brands, this means AI Visibility is no longer a "one-channel" problem. It's a cross-LLM narrative problem.

ChatGPT searches like an analyst, while Perplexity searches like a shopper. To win in AI discovery, content teams must design for both—depth and freshness.

If you want to understand how AI systems see, search, and talk about your brand, and how to improve your AI Visibility Score, you can test it with Genezio.

Try Genezio for free to understand your AI visibility.

Methodology

To ensure a fair and controlled comparison, we followed the same process for both ChatGPT and Perplexity.

1. Topic and scenario definition: We defined high-level topics related to the UK banking industry (retail, digital, customer experience, trust, loans, SME) and generated conversational scenarios to reflect realistic user decision-making journeys.
2. Scenario execution: Each scenario was executed independently against ChatGPT.com and Perplexity. Both systems were allowed to perform web searches as part of their normal response generation process.
3. Search query extraction: Using the Genezio AI Visibility platform, we extracted all web search queries launched by each LLM. These are the raw search queries, not paraphrases.
4. Dataset construction: We selected the top 150 queries per platform based on frequency and relevance.
5. Analysis approach: We analyzed query length, structure, lexical patterns, thematic intent, and semantic similarity. Importantly, this analysis focused on how models search, not on the quality of their final answers.