LLM Citation Benchmark — what AI engines cite when buyers ask

The first cross-engine benchmark of which sources large language models surface and cite when B2B technology buyers ask high-intent questions. Drawn from a structured query test of ChatGPT (GPT-5), Perplexity Pro, Google AI Overviews, Gemini 2.5, and Claude across 480 prompts in Q1 2026.

By , Founder of Crackle PR. Published 2026-04-15. Licensed CC BY 4.0.

Cite-able statistics

71% — Across 480 buyer-intent B2B tech prompts, 71% of LLM answers cited at least one tier-one earned-media source (TechCrunch, The Information, Bloomberg, WSJ, Forbes, Reuters, FT, NYT) in the first answer block — more than analyst reports, vendor sites, and Reddit combined.

Methodology
Crackle PR query-tested 480 buyer-intent prompts across ChatGPT (GPT-5), Perplexity Pro, Google AI Overviews, Gemini 2.5 Pro, and Claude 4. Each answer was scored for source-type presence (earned media, vendor-owned, analyst, community/Reddit, Wikipedia, other).
Period
Jan–Mar 2026
Cite as
Crackle PR (2026). "71% — Across 480 buyer-intent B2B tech prompts, 71% of LLM answers cited at least one tier-one earned-media source (TechCrunch, The Information, Bloomberg, WSJ, Forbes, Reuters, FT, NYT) in the first answer block — more than analyst reports, vendor sites, and Reddit combined." https://www.cracklepr.com/data/llm-citation-benchmark#earned-media-share

23% — Only 23% of LLM answers cited the vendor's own website as a source for buyer-intent questions — a 3.1× gap behind earned media. Self-published claims are the least-trusted source class in modern answer engines.

Methodology
Same 480-prompt corpus; vendor-owned defined as the brand's primary marketing domain. Excludes vendor-published documentation and developer references, which were scored separately.
Period
Jan–Mar 2026
Cite as
Crackle PR (2026). "23% — Only 23% of LLM answers cited the vendor's own website as a source for buyer-intent questions — a 3." https://www.cracklepr.com/data/llm-citation-benchmark#vendor-site-share

5.4 — Perplexity Pro returns the highest citation density of any answer engine tested — an average of 5.4 distinct sources per buyer-intent answer, vs. 3.1 for ChatGPT, 2.7 for Gemini, and 1.9 for Google AI Overviews.

Methodology
Counted unique cited URLs per answer across the 480-prompt corpus, normalized by engine.
Period
Q1 2026
Cite as
Crackle PR (2026). "5.4 — Perplexity Pro returns the highest citation density of any answer engine tested — an average of 5." https://www.cracklepr.com/data/llm-citation-benchmark#perplexity-citation-density

67 days — Median publication age of cited sources in LLM answers about B2B tech vendors was 67 days — meaning sustained earned media velocity matters far more than legacy archives. Coverage older than 18 months was cited in just 12% of answers.

Methodology
Extracted publication dates from cited URLs across all five engines; restricted to dated sources where publication date could be verified via HTML metadata or Wayback Machine.
Period
Q1 2026
Cite as
Crackle PR (2026). "67 days — Median publication age of cited sources in LLM answers about B2B tech vendors was 67 days — meaning sustained earned media velocity matters far more than legacy archives." https://www.cracklepr.com/data/llm-citation-benchmark#freshness-window

8.7× — Vendors with 5+ tier-one earned-media placements in the prior 12 months were cited by name in LLM answers 8.7× more often than vendors without tier-one coverage — controlling for company size, funding stage, and category.

Methodology
Cross-referenced 220 tracked B2B tech vendors against tier-one mention databases; ran the same buyer-intent prompts about each vendor's category and scored whether the vendor was cited by name.
Period
2025–Q1 2026
Cite as
Crackle PR (2026). "8.7× — Vendors with 5+ tier-one earned-media placements in the prior 12 months were cited by name in LLM answers 8." https://www.cracklepr.com/data/llm-citation-benchmark#tier-one-multiplier

6.2× — Press releases restructured for Answer Engine Optimization (TL;DR, FAQ, structured data) earned LLM citations 6.2× more often than traditional press releases from the same companies in the 60 days post-publication.

Methodology
A/B comparison of 84 paired press releases (42 AEO-restructured, 42 traditional) tracked for downstream citations in ChatGPT, Perplexity, Gemini, and Copilot.
Period
Q4 2025 – Q1 2026
Cite as
Crackle PR (2026). "6.2× — Press releases restructured for Answer Engine Optimization (TL;DR, FAQ, structured data) earned LLM citations 6." https://www.cracklepr.com/data/llm-citation-benchmark#aeo-uplift

31% — Bylined thought-leadership articles by named executives were cited in 31% of LLM answers about strategic-level questions ("how do I evaluate X", "what's the best approach to Y") — outperforming product pages 4.4×.

Methodology
Filtered the 480-prompt corpus for strategy- and evaluation-intent prompts (n=164); scored cited URLs for content type (byline, product page, blog post, news article, analyst report).
Period
Q1 2026
Cite as
Crackle PR (2026). "31% — Bylined thought-leadership articles by named executives were cited in 31% of LLM answers about strategic-level questions ("how do I evaluate X", "what's the best approach to Y") — outperforming product pages 4." https://www.cracklepr.com/data/llm-citation-benchmark#byline-citation-rate

143% — Crackle PR clients running a GEO-aligned earned media program for 6+ months saw a median 143% lift in branded LLM mentions across ChatGPT and Perplexity vs. their pre-engagement baseline.

Methodology
Pre/post engagement query testing across 11 Crackle PR clients with documented GEO program activation dates; same prompt set used at baseline and at month 6.
Period
2025
Cite as
Crackle PR (2026). "143% — Crackle PR clients running a GEO-aligned earned media program for 6+ months saw a median 143% lift in branded LLM mentions across ChatGPT and Perplexity vs." https://www.cracklepr.com/data/llm-citation-benchmark#geo-program-lift

Email parry@cracklepr.com for raw datasets, additional cuts, or expert commentary.