ChatGPT Search vs Google AI Overviews: Where Brands Differ

Four engines, four different retrieval mechanics

OpenAI web_search (gpt-4.1-mini). The model has a search tool available and calls it when it judges the question needs current information. When it does, the answer comes back with inline citations you can inspect — but the model's decision to search at all is not guaranteed on every prompt.

Perplexity Sonar. Sonar is grounded by default — every query triggers live retrieval, with no model judgment call involved. That makes Perplexity the most consistently "search-triggered" of the four, which is one reason it behaves differently from the others in raw appearance rates.

Gemini grounded search. Gemini can ground its answer in Google Search results, but — like OpenAI's tool call — grounding is a per-response decision, not a guarantee. Two runs of the same prompt can come back with different grounding behavior.

Google AI Overviews (via SerpAPI). This is the odd one out: it is not a chat answer at all, it's a block embedded directly in the Google search results page. It is deterministic per search in the sense that a given query, at a given moment, returns one specific AIO (or none) — but whether an AIO renders is entirely Google's call, and for a large share of queries it simply doesn't show up. That absence is what we log as no_surface, and it is a different fact than "the brand wasn't mentioned." A brand can be completely absent from an AIO that never appeared, or absent from an AIO that appeared and mentioned three competitors instead — those are two different problems with two different fixes.

Why "AI search" is not one channel

Bundling these four into a single "AI visibility" number hides exactly the information a marketing or SEO team needs. A brand might have a 40% mention rate on Perplexity, near-zero on Gemini for the same set of prompts, and no AI Overview appearing for most of the local or pricing-intent queries where a competitor is cited. Three different situations, three different actions — none of which are visible in a blended score. Measuring per-surface also matches how traffic actually arrives: a citation in ChatGPT's answer, a link surfaced in Perplexity's sidebar, and a brand name inside an AI Overview snippet are three different exposure events with different click behavior.

Non-determinism is the reason single checks are unreliable

None of these four engines return the same answer every time for the same prompt. A single query to OpenAI web_search or Gemini today tells you almost nothing about tomorrow's answer, or about how the same intent is handled by ten reworded variants of that question. That's why TAMGIO's core metrics — Mention Rate, Share of Voice, Citation Rate, and Prompt Coverage — are all N-sample proxy measures: they are computed across a set of prompts and repeated samples, not a single lucky or unlucky call. No tool can honestly promise a specific percentage move, going from 60% to 90%, because the underlying answers are not deterministic; any tool that promises otherwise is smoothing over the noise for you, not measuring it.

How TAMGIO keeps the four surfaces honest

Every metric in TAMGIO carries a dashed underline — open it and you see the exact API or SERP response, the citations returned, and the timestamp behind that number, for whichever engine produced it. Errors and no_surface results are shown openly rather than dropped from the count, because silently excluding them would inflate every score. Prompts are written or AI-generated in sets of 30 to 40, spread across best-of, comparison, how-to, local, and pricing intent — the same prompt set run across all three ongoing engines (OpenAI web_search, Perplexity Sonar, Gemini grounded search) so the per-surface differences are comparable. For a faster first look, Audit Mode runs a one-time batch of roughly 20 to 30 auto-generated prompts across all four engines in a single scan and produces an audit report and PDF, without setting up ongoing tracking.