State of GEO Q2 2026: the AI engine you optimize for matters most

We asked four AI engines — ChatGPT, Claude, Gemini, and Perplexity — the same ten questions about each of 100 brands, then recorded which engine cited each brand's own website. The clearest finding of the 4,000-prompt run, dated 2026-06-07, is not about any brand. It's about the engines: citation rate varies far more by which engine you ask than by which brand you are. Perplexity cited the target brand's domain on 42% of prompts; ChatGPT on 7%; Gemini grounds almost everything but returns opaque redirect URLs so you can't tell who it cited; and across all of them, the legacy news publishers whose journalism trained these models — NYT, the BBC, Bloomberg, Reuters — were cited 0% of the time.

The one-line takeaway: in mid-2026, "optimizing for AI search" mostly means optimizing for the engine, not in the abstract. The same brand can be a first-class citizen in Perplexity and invisible in ChatGPT.

Read this first: coverage and what broke

This is a v1, and an honest one. Of the 4,000 probes, 2,876 returned a usable answer and 1,124 errored — and errors are excluded from every rate below (an API failure means "not measured," never "not cited"). The errors were not random, so per-engine coverage is uneven and you should read each engine's number with its sample size:

Engine	Valid probes	Grounding (searched)	Citation rate	Note
Perplexity	1,000 / 1,000	100%	41.6%	Clean, full 100-brand coverage — the backbone of this report
Claude (Sonnet)	238 / 1,000	93%	32.8%	A 24-brand sample — our Anthropic credits ran out mid-run; treat as directional
ChatGPT	650 / 1,000	23%	6.9%	Rarely searches at all (so rarely cites); 350 calls hit OpenAI rate limits
Gemini	988 / 1,000	99%	0% (opaque)	Grounds heavily but returns vertexaisearch redirect wrappers — publisher hidden

Because Perplexity is the only engine with clean, uniform 100-brand coverage, the brand leaderboard below uses Perplexity's citation rate — a consistent ruler across all 100 brands. The other engines appear in the cross-engine section, where their job is comparison, not ranking. This mirrors what we found turning the same lens on our own site and on who LLMs cite for GEO: Perplexity and Claude do the real retrieving; ChatGPT mostly answers from memory; Gemini hides its sources.

The brand leaderboard (Perplexity citation rate)

Across 100 brands, Perplexity cited the brand's own domain on a mean of 41.6% of prompts (median 40%). The spread is wide: 43 brands were cited on at least half their prompts, while 11 brands were never cited once.

#	Brand	Sector	Perplexity citation rate
1	TechCrunch	Media / Publishers	100%
2	Brex	Finance / Fintech	90%
2	Databricks	SaaS / B2B	90%
2	Salesforce	SaaS / B2B	90%
2	Talkspace	Healthcare / Wellness	90%
6	Atlassian	SaaS / B2B	80%
6	MongoDB	SaaS / B2B	80%
6	Stratechery	Media / Publishers	80%
6	Wealthfront	Finance / Fintech	80%

The full ranking for all 100 brands is in the dataset.

The sector gradient

Grouping the Perplexity leaderboard by sector, the practitioner-heavy categories win and media loses:

Sector	Mean Perplexity citation rate
SaaS / B2B	49.6%
Finance / Fintech	46.0%
Healthcare / Wellness	42.0%
AI / ML companies	42.0%
Travel / Hospitality	41.0%
Consumer e-commerce / DTC	38.7%
Education / EdTech	38.0%
Media / Publishers	21.0%

SaaS and fintech brands — the ones with dense, structured documentation and a heavy developer/marketing content footprint — are cited roughly 2.4× more often than media publishers.

The media paradox

The single most counter-intuitive result sits inside that bottom row. The publishers whose archives plausibly trained these models are the ones the models won't cite:

• TechCrunch: 100%. Stratechery: 80%. The Information: 30%.
• NYT, Washington Post, BBC, Bloomberg, Reuters, Wired, The Verge: 0%.

Every legacy or paywalled publisher in the set scored zero Perplexity citations; only the tech-native, openly-readable outlets surfaced. The likeliest explanation is structural, not editorial: paywalls and aggressive robots/bot policies keep the retrieval layer out, so the engine reaches for whatever it can fetch — which is the open tech press. Whatever the cause, the lesson for a publisher is blunt: training on your content is not citation, and if the live crawler can't reach the page, you don't exist in the answer.

How much the engine matters: divergence

For brands where we have more than one engine's data, the gap between engines dwarfs the gap between brands. Two examples from the dataset:

• Brex — Perplexity 90%, ChatGPT 10%.
• Wealthfront — Perplexity 80%, ChatGPT 10%.

A brand can be nearly always cited by one engine and nearly never by another. That is the practical core of GEO in 2026: there is no single "AI visibility" number. ChatGPT's low rate is mostly because it searches on only ~23% of prompts — when it answers from training memory, no one gets cited. Gemini's zero is an artifact of its API hiding the publisher behind a redirect, not evidence that it cites no one. Perplexity, which searches every time and returns real URLs, is where citation is both highest and measurable.

A note on sentiment

We also ran a crude keyword-based sentiment proxy on the "is X any good / what are its weaknesses" prompts — disclosed as rough (a v2 would use a proper classifier). The only pattern worth stating at this confidence: telehealth brands skewed negative (Hers, Ro), consistent with the hedging LLMs apply to health topics, while education and open-model brands skewed positive (Codecademy, Mistral). Treat these as directional, not definitive.

How we tested

The method reuses our citation harness: for each of 100 brands (8 sectors, see the dataset), ten prompts — direct ("tell me about X"), category ("best $category in 2026"), comparison ("X vs competitor"), and sentiment — were sent to ChatGPT, Claude, Gemini, and Perplexity with web grounding enabled, on 2026-06-07. Citation = the brand's own registrable domain appears in the engine's returned sources (an unambiguous signal, and the same one we use for our own citation rate). Every figure here is produced by an open analysis script over the captured probes; nothing is estimated. Probes ran from a single EU location.

Limitations

This is the first quarterly State of GEO, and it is bounded:

• Uneven engine coverage. Perplexity is complete (100 brands); Claude is a 24-brand sample (Anthropic credits ran out mid-run, on the cheaper Sonnet model); ChatGPT lost 350 probes to rate limits; Gemini's sources are opaque. The leaderboard is therefore Perplexity-anchored, and cross-engine numbers carry their sample sizes. A v2 re-runs all four to full coverage.
• Single run, 10 prompts, one day, one region. A snapshot, not a trend; the value compounds when Q3 gives the first delta.
• Citation, not ranking quality. We measure whether the brand's domain was cited, not whether the answer was good or the brand was described accurately.
• Crude sentiment proxy (keyword-based) — directional only.
• Prompt-set and brand-set bias. A different 100 brands or 10 prompts would move the numbers.

The full per-brand, per-engine data — including every brand's coverage so you can see exactly what's measured — is the public dataset. This sits in the measurement pillar alongside our other quantitative work; if you're new to the metric, start with what GEO is and the citation rate definition.

Frequently asked questions

Why is the leaderboard only Perplexity? Because it's the only engine that searched every prompt and returned real URLs, giving uniform, clean coverage across all 100 brands. Ranking brands on an engine with partial coverage would compare them on different sample sizes. The other engines are reported in aggregate, with their coverage stated.

Does Gemini really cite nobody? No — Gemini grounds on ~99% of prompts. But its API returns vertexaisearch.cloud.google.com redirect wrappers instead of the publisher URL, so a brand-domain citation can't be observed. We report that as opacity, not as zero citations. (We hit the same wall measuring who LLMs cite for GEO.)

Can I reproduce this? Yes. The dataset has every brand's per-engine rate and coverage; the brand list, prompt set, and analysis code are in our repo. Re-run it and you should reproduce these numbers from the captured probes.

GeoSalience (2026, June 7). State of GEO Q2 2026: the AI engine you optimize for matters most. GeoSalience. https://geosalience.com/measurement/state-of-geo-q2-2026