New Data Finds Difference Between Google Rankings And LLM Citations

New data finds difference between Google rankings and LLM citations, highlighting major shifts in online visibility and content trust.

A recent comprehensive analysis by Search Atlas, an SEO software company, compared how large language models (LLMs) cite web sources relative to Google’s traditional search rankings.

Examining 18,377 semantically matched queries, the study reveals notable discrepancies in domain and URL overlaps between Google’s top results and responses provided by OpenAI’s GPT, Google’s Gemini, and Perplexity.​

Perplexity Closely Mirrors Google Search Results

Among the three LLMs studied, Perplexity showed the highest alignment with Google search outcomes due to its live web retrieval capability.

The research found Perplexity’s median domain overlap with Google was around 25-30%, with URL-level overlap close to 20%.

Overall, about 43% of the domains cited by Perplexity also appeared in Google’s results, indicating a strong correlation with traditional search visibility.​

ChatGPT and Gemini Are More Selective and Divergent

In contrast, ChatGPT displayed a median domain overlap of only 10-15% with Google results, citing fewer shared domains (around 21% of its total citations).

URL matches with Google were typically below 10%. Gemini showed even less consistency, sharing just about 4% of domains with Google’s search results despite those domains comprising 28% of Gemini’s cited sources.

This suggests ChatGPT and Gemini rely more heavily on pre-trained knowledge and selective source retrieval than real-time search alignment.​

Why These Differences Matter for Search Visibility

The findings demonstrate that ranking well in Google does not guarantee citation by LLMs. Perplexity’s architecture, which actively retrieves from the web, leads to citation patterns closely tracking Google’s rankings.

By contrast, ChatGPT and Gemini tend to cite a narrower, more selective subset of sources, often independent of current SERP rankings.

This highlights distinct visibility mechanisms between traditional search engines and AI-powered answer generation.​

Limitations and Context of the Study

The dataset largely favored Perplexity, accounting for 89% of matched queries, with GPT at 8% and Gemini at 3%.

Queries were paired using semantic similarity scoring with an 82% threshold, representing close but not identical information needs.

The two-month study window offers a recent snapshot, and longer-term studies are necessary to confirm whether these overlap patterns persist over time.​

SEO Implications and Future Outlook

For retrieval-based LLMs like Perplexity, traditional SEO signals such as domain authority and backlink profiles are more influential for visibility.

For reasoning-focused models like ChatGPT and Gemini, these signals matter less, suggesting SEO strategies should adapt to optimize for citation relevance beyond classical search rankings.

Developers and marketers should monitor how brand visibility differs across conventional and AI-driven platforms to fully leverage emerging search paradigms.​

Summary

• LLM citations differ significantly from Google search rankings, with Perplexity showing the highest overlap due to live web retrieval.
• ChatGPT and Gemini cite fewer shared domains and URLs with Google, favoring pre-trained and selective knowledge sources.
• Ranking well on Google does not guarantee LLM citation, underscoring different underlying mechanisms for AI visibility.
• The study’s scope favors Perplexity data and represents a limited timeframe; ongoing research is needed.
• SEO strategies should evolve to address both traditional search signals and AI citation criteria for comprehensive visibility.

Bottom Line

This evolving landscape shows the importance of understanding both classical SEO and AI-driven citation dynamics to maintain competitive advantages in search visibility today and beyond.