When you ask a modern AI model a question that needs fresh facts, it doesn’t
answer from memory. It runs its own web search, reads what comes back, and
weaves some of those pages into its answer. That process is called grounding.

But “it searches the web” hides a lot. Each platform receives a different number
of pages, keeps a different fraction of them, hands back a different kind of
evidence, and cites at a wildly different rate. To show this concretely, we ran
the exact same query — “best ai seo agency 2026” — through all three, on
the same day, with no location set, and inspected the raw grounding data each one
returned.

The three runs:

Platform	Model	Time	Tokens in / out	Pages received	Pages cited
Google	gemini-3.5-flash	53 s	205 / 15,321	7	7
OpenAI	gpt-5.5	14 s	10,853 / 782	39	2
Anthropic	claude-opus-4-8	138 s	83,708 / 12,754	14	9

Three answers to one question, three completely different shapes underneath.

The one idea that explains all three: the grounding funnel

Every platform runs the same basic pipeline, just with different valves:

search query → pages RECEIVED → pages with READABLE content → pages CITED in the answer

• Received = every URL the search step handed back to the model.
• Readable content = the subset the model actually got text for (a snippet,
  a window, or an encrypted blob).
• Cited = the pages whose URL ends up footnoted in the final answer.

The gap between received and cited is where each platform reveals its
personality. Google cites almost everything it receives; OpenAI receives a
flood and cites a trickle; Anthropic sits in between. Let’s walk through each.

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Google (Gemini): “I only show you what I used”

Google’s model is the most economical. It sent only 205 input tokens —
basically just the question — then did the heavy lifting server-side and streamed
back a long, fully-written answer (15,321 output tokens).

It rewrote the question into two searches before answering:

• top ai seo agencies 2026
• best ai seo agency 2026

The funnel barely narrows: 7 received, 7 cited. This is the defining trait of
Google’s grounding — it doesn’t expose pages it didn’t use. Every source you see
is a grounding chunk that genuinely supported a sentence. Behind those 7
sources sit 24 support segments: individual answer sentences mapped back to
the page that justifies them. For example, this sentence —

“In 2026, the landscape of search engine optimization has shifted dramatically
from traditional ‘blue links’ to Generative Engine Optimization (GEO) and
Answer Engine Optimization (AEO)”

— is backed by both europeanbusinessreview.com and thriveagency.com.

The URL catch: everything arrives wrapped in a redirect. Google never hands
you the real link directly. Each source comes as a
vertexaisearch.cloud.google.com/grounding/... redirect that has to be resolved
to recover the true destination:

What Google sends	What it resolves to	Cites
vertexaisearch.cloud.google.com/grounding/...	https://thriveagency.com/news/best-ai-seo…	6
vertexaisearch.cloud.google.com/grounding/...	https://dejanmarketing.com/best-ai-seo…	6
vertexaisearch.cloud.google.com/grounding/...	https://wellows.com/blog/top-ai-seo…	5

It also gives you the retrieved page content per source — e.g. a Catalyst
review snippet stamped (2026-06-09).

Google’s philosophy: tight, citation-first, nothing wasted — but the real
URLs are hidden behind redirects you must unwrap.

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OpenAI (gpt-5.5): “Here’s a huge pile — I’ll cite two of them”

OpenAI was the fastest by far (14 seconds) and ran with reasoning effort set
to none (just 60 reasoning tokens), yet it cast the widest net.

It fanned the question into three searches:

• best AI SEO agencies 2026 AI search optimization agency
• top AI SEO agency generative engine optimization 2026
• best GEO agency AI search optimization 2026

The funnel is dramatic: 39 received → 37 with readable content → 2 cited.
That’s roughly a 20:1 drop. OpenAI is happy to show you 37 pages of evidence
but footnotes only the two it leaned on.

The snippets are a sliding window. This is OpenAI’s most distinctive feature.
Every page comes back as a short extractive window, explicitly tagged with a word
limit. In this run:

• 6 pages capped at a 100-word window (these are “news”-class results)
• 31 pages capped at a 200-word window (general web / forum results)

Each window is a ...-joined collage of the most relevant passages plus freshness
metadata. Here is the actual snippet OpenAI kept for the page it ended up citing,
marketingltb.com (search-class, 200-word window, published “2 weeks ago”):

10 Best AI SEO Agencies in 2026

… In 2026, Directive has extended this approach into AI search optimization,
building content architectures designed to appear in Google AI Overviews,
ChatGPT recommendations, and Perplexity answers …
… iPullRank, a New York-based enterprise SEO and content strategy agency
founded by Mike King … is one of the most technically distinctive AI SEO
agencies in the market.

The two it actually cited, and the claim each one supports:

Cited page	Class	Window	Claim it grounds
marketingltb.com	search	200 w	“Several recent 2026 lists rank agencies like Directive, iPullRank, Searchbloom…”
techradar.com (Ahrefs)	news	100 w	“Recent SEO platforms are also adding AI visibility tracking across ChatGPT, Perplexity, Google AI Overviews…”

URLs are clean and direct — no redirect wrapper like Google — though cited
links carry a ?utm_source=openai tracking tag.

OpenAI’s philosophy: retrieve aggressively, expose readable windows for
everything, but cite conservatively. Great for seeing the evidence pool, weak
if you equate “cited” with “considered.”

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Anthropic (Claude): “I’ll read deeply, but the snippets are sealed”

Claude took the longest (138 seconds) and consumed by far the most input tokens (83,708) — because it grounds in two passes. First it narrates and
searches (“I’ll search for current information on the top AI SEO agencies in
2026.”), then it feeds the retrieved material back through itself to write the
final answer.

The funnel: 14 received → 9 cited, 5 unselected. Unlike Google, Claude does
keep pages it ultimately didn’t cite (the 5 “unselected” ones), so you can see
what it considered and passed over — e.g. it received frase.io, revvgrowth.com
and stridec.com but cited none of them.

The catch: the snippet content is encrypted. Each received page carries a
sealed blob — between roughly 3,200 and 4,800 bytes — that you cannot read
directly. It’s opaque by design.

You recover it as “boundary lines.” By passing the encrypted material back in
a second turn, the snippet can be reconstructed as a list of passages, long ones
collapsed to a start […] end form. One page alone yielded 44 of these
lines; across the 14 sources they range from 19 to 44 each. A sample from
spicymargarita.co:

• “Intero Digital is a large, full-service […] into AI-driven search environments.”
• “Good AI SEO is also good SEO.”
• “There is no separation.”
• “Unlike traditional SEO where you […] mentions, and references inside generative outputs.”

And Claude records the exact verbatim span it quoted into the answer (its
cited_text), e.g.:

“Good AI SEO is also good SEO. There is no separation. Thrive integrates
AI-powered SEO into every campaign…”

Anthropic’s philosophy: deep, two-pass reading with a generous citation rate
and full visibility into considered-but-rejected sources — but the raw snippets
are sealed and only recoverable indirectly, which is why it’s the slowest and
most token-hungry of the three.

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Side by side

Dimension	Google (Gemini)	OpenAI (gpt-5.5)	Anthropic (Claude)
Speed	53 s	14 s (fastest)	138 s (slowest)
Search queries fired	2	3	(narrated, single pass + read)
Pages received → cited	7 → 7 (~1:1)	39 → 2 (~20:1)	14 → 9 (~3:2)
Shows uncited pages?	No	Yes (37 readable)	Yes (5 unselected)
Snippet form	Retrieved page content	Sliding window (100/200 wds)	Encrypted blob
Read the snippet directly?	Yes	Yes	No — recover as boundary lines
URLs	Redirect-wrapped	Direct (+utm tag)	Direct
Input tokens	205 (lightest)	10,853	83,708 (heaviest)

What this means in practice

• If you want to know what an AI cited, the platform changes the answer.
  The same query yields 7, 2, and 9 cited sources. “AI mentioned us” is only
  meaningful relative to a specific platform’s citation behaviour.
• OpenAI’s wide-receive / narrow-cite split is a visibility trap. Being one
  of the 37 pages OpenAI read is very different from being one of the 2 it
  cited. Both are worth tracking, but they’re not the same win.
• Google’s redirect wrapping means any tool reading Gemini grounding must
  resolve URLs before it can match them to your domains.
• Anthropic gives the richest “considered set” (cited + rejected) but hides
  the snippet text, so reconstructing what it actually read costs a second pass —
  and real time and tokens.

Same question. Same day. Three very different machines deciding what counts as
evidence.