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AI offers a conversational experience. We use LLMs through chatbots. But no one has yet looked at how citations and mentions evolve in a conversation.

I analyzed data from the Semrush AI Visibility Toolkit to review 20 buyer journeys across four different verticals to compare high vs. low reasoning for ChatGPT5.2.

In this analysis:

  • Why high reasoning cites a nearly different web (only 25.6% domain overlap with minimal) and which source types gain or lose ground.
  • Why TOFU content has a payoff again: Grands cited at the Problem stage are more likely to persist all the way to Selection under high reasoning, and never under minimal.
  • How to split your prompt tracking by reasoning mode so your AI visibility reporting reflects 2 different systems, not an averaged one.

Methodology

Data comes from the Semrush AI Visibility Toolkit, which captures the prompts, citations, and fan-out queries ChatGPT generates per response.

  • We ran 100 prompts twice through GPT-5.2, once with minimal reasoning and once with high reasoning, for 200 total responses.
  • Prompts span 20 buyer journeys across 4 categories (B2B SaaS, Finance, Consumer Tech, Health/Lifestyle), with 5 stages per journey: Problem, Exploration, Comparison, Validation, Selection.
  • Citation rate is the share of prompts where the response cited at least one external source.
  • Average citation counts sources per cited response.
  • Fan-out queries are the sub-queries the model fires internally to research the prompt before answering, surfaced via the Semrush API.

GPT 5.2’s high reasoning cites and searches more

Turn high reasoning on, and the citation rate jumps from 50% to 68% (+18 percentage points), the average sources per response nearly doubles (2.6 to 4.5), and fan-out queries go up 4.6x. High reasoning also pulls from 173 unique domains across the test set vs. 127 for minimal; 99 of those domains never appear under minimal reasoning.

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*Citation Rate is defined as the share of prompts where the response cited at least one external source.

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This is grounding at its finest. When the model thinks harder, it relies more on web search. Reasoning plays a major role in brand visibility, though we don’t know how many users activate reasoning vs not.

Query intent is a cleaner proxy than user demographics. Free-tier users have reasoning access too, just rate-limited, and ChatGPT auto-routes hard prompts to Thinking mode without the user clicking anything. So the question isn’t who can afford reasoning. It’s which prompts trigger reasoning automatically. 

Multi-criteria comparisons, evaluation frameworks, regulatory and compliance questions, and complex shopping builds are the prompts most likely to fire reasoning regardless of plan. Map your audience by query type, not by paywall status.

High reasoning fires more fan-out queries deeper in the…


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Last Update: May 19, 2026