50 Generative Engine Optimization Statistics That Matter in 2026

Struggling to make the most of your GEO strategy? We analyzed over 67,000 citations from AI-generated results across 8,500+ prompts. The dataset spans outputs from Google AI Overview, Gemini 2.0, OpenAI GPT-4o, and Perplexity Sonar, covering industries like software, healthcare, eLearning, cybersecurity, finance, and travel.

This post distills that analysis into 50 statistics that actually matter—not recycled SEO guesses or AI hype, but real insights grounded in how today’s engines cite and surface content.

We’ve organized the stats by theme so you can:

• See which content types and formats earn the most citations

• Understand how engines like GPT-4o and Gemini differ in citation behavior

• Identify which industries dominate visibility—and where gaps remain

• Learn what structural elements and ranking positions actually move the needle

• Benchmark your own content efforts against what LLMs are actually rewarding

Which AI models generate the most citations?

Not all generative engines behave the same way. Our analysis of 67,111 citations across 7,950 generative search results shows clear differences in how various AI models select, structure, and distribute citations. Understanding these patterns is crucial for anyone optimizing content for visibility in AI search.

Google AI Overview dominates with over half of all citations (51.4%)

Google AI Overview is by far the most influential engine in this dataset, responsible for 51.4% of all citations—more than double the share of the next most active model. This reflects the widespread integration of Google’s own summarization features across Search, SGE experiments, and Gemini-powered answer modules. Its dominance means that if your content isn't optimized to align with Google’s preferred citation formats, your visibility in generative outputs may be structurally limited—regardless of how well you perform in traditional organic SERPs.

Google Gemini 2.0 accounts for 25.3% of citations, favoring breadth and consistency

The second largest source of generative citations is Google Gemini 2.0, which produced 25.3% of the total. While Gemini’s per-output citation density is lower than GPT-4o’s, its consistency across a broad set of prompts makes it a critical engine to optimize for. Content favored by Gemini tends to be well-structured, topically specific, and aligned with middle-to-bottom-of-funnel intent. It’s also less selective than GPT-4o, meaning that medium-authority sites and up-to-date blogs have a stronger chance of appearing.

Perplexity Sonar represents 20.8% of citations, offering reach with less density

Perplexity Sonar, known for its broad web-crawling and real-time indexing approach, contributes 20.8% of all citations. What sets it apart is its preference for wide domain coverage. Perplexity draws from a larger variety of sources—frequently surfacing Reddit threads, user forums, niche blogs, and less mainstream publishers. However, individual outputs tend to include fewer citations per prompt compared to GPT-4o. For content teams, this means Perplexity rewards publishing across diverse topics more than relying on concentrated topical depth.

OpenAI GPT-4o accounts for just 2.6% of total citations—but rewards quality over quantity

Despite its popularity in chat interfaces and enterprise applications, OpenAI GPT-4o generated only 2.6% of the total citations in this dataset. This is not a reflection of irrelevance—but rather of selectivity. GPT-4o tends to generate outputs with fewer citations per result, but those citations are disproportionately weighted toward high-authority, highly structured pages. Its outputs often reference 2–5 sources maximum, usually ones with extremely clear formatting, specific brand references, and comparison-based layouts. In other words, while GPT-4o might cite your site less often, the quality and influence of those citations may be significantly higher.

What this means for content teams

Optimizing for generative search means recognizing the format and domain preferences of each engine:

• Google AI Overview prioritizes freshness, wide topic coverage, and citation structure that aligns with its SGE summarization patterns.

• Gemini 2.0 rewards mid-authority publishers with well-scaffolded content that can support structured recommendations.

• Perplexity offers a route to visibility through niche relevance and prompt diversity.

• GPT-4o is your quality filter. To be cited, your content must be clean, confident, and cite-worthy at the paragraph level.

Each engine acts as its own “referee” of online content. If your site isn't being structured for the right referee, it won’t make the field—regardless of how strong your ideas are.

Which content formats actually get cited?

The format of your content plays a pivotal role in determining whether it’s cited by generative AI models. It’s not just what you say—it’s how your page is structured, titled, and positioned in the broader information landscape. Our dataset of 67,111 citations reveals clear preferences among large language models for specific content types, and those preferences don’t always align with what traditional SEO teams optimize for.

Blog-style content leads with 43% of total citations—even when unstructured

Blog articles accounted for 43% of all citations, making them the most cited format by volume. This reflects the sheer scale of blog content available online, particularly from B2B and SaaS companies publishing thought leadership, how-to guides, and list-based articles. However, it’s important to note that many of these citations come from only moderately structured posts. While blogs are widely cited, they don’t necessarily dominate visibility at the top ranks unless they also incorporate comparison, brand specificity, or semantic structure. In short: blogs can get you on the board, but not always at the top.

News articles contribute 23% of citations, especially around timely or trending topics

News content represents 23% of total citations in the dataset. Generative engines lean on news sources for current information, product launches, industry events, and other time-sensitive updates. This category is especially influential for B2C brands, travel companies, and tech publishers covering AI, cybersecurity, or SaaS trends. However, news articles tend to age quickly and are cited less often for evergreen or commercial queries. Their inclusion is heavily prompt-dependent—useful in real-time contexts, but not a long-term GEO play unless paired with other formats.

Product blogs earn 7.5% of citations, proving vendor content can still compete

Despite skepticism around vendor bias, product blogs and brand-owned content accounted for 7.5% of all citations. This shows that generative engines will cite vendor sources—so long as the content is informative, structured, and specific. Citations in this category are often tied to features, comparisons, integrations, or pricing insights. The key difference is that LLMs appear to favor neutral tone and comparative framing, even within vendor content. Brands that can present their solutions in a broader context—without overt sales language—are more likely to be surfaced.

Comparison portals average 15.4 citations per row—the highest of any format

While they make up only 3.7% of total citation volume, comparison portals consistently outperform all other formats in per-row visibility. These include ranking-style content such as “Best CRM Tools in 2025,” side-by-side product breakdowns, and feature comparis on tables. Pages of this type average 15.4 citations per row, meaning they are referenced multiple times across prompts and engines. This is because their structure closely matches the answer format that generative models are designed to output: ranked options, key differences, brand mentions, and decision-support language. If your goal is to win visibility in zero-click summaries or voice AI responses, this is the most reliable format.

Wiki-style content earns just 2.1% of citations—and rarely appears in commercial queries

Despite Wikipedia’s high domain authority and massive footprint, wiki-style content makes up only 2.1% of total citations. These citations are almost entirely limited to informational queries—definitions, explanations, or technical overviews. In prompts with commercial or transactional intent, generative engines favor sources with narrative context, examples, and current data. While wikis are still useful for glossary or educational moments, they don’t support product discovery, decision-making, or recommendation tasks. For marketers, this means replicating a neutral tone alone is not enough—you need content that shows expertise and comparison, not just factual coverage.

What this means for content teams

If you want to be cited by LLMs, your content can’t just be relevant. It must be shaped in a way that reflects how models retrieve and summarize information. While blogs and news pieces provide volume, the most efficient citation formats—comparison portals, structured product content, and brand-aware writeups—punch above their weight. Optimizing for structure is no longer optional. It’s the difference between being scanned and being quoted.

Which rank gets you the most visibility in AI search?

Generative search engines do not distribute attention equally. In fact, the majority of citations come from a narrow band of top-ranked sources—making ranking position more consequential than in traditional search. Our data confirms that visibility in LLM outputs is overwhelmingly concentrated among the first three results retrieved.

Rank 1 content dominates with an average visibility score of 88.0

Pages ranked first in generative outputs carry an average visibility score of 88.0, making them by far the most influential in the dataset. These pages are not just present—they’re cited, quoted, and used as foundational material for the AI-generated summaries users actually read. This is the slot where structured content, strong brand signals, and domain-level authority converge to generate disproportionate returns.

Rank 2 and 3 still perform—but drop significantly in visibility

Second-ranked content drops to an average score of 79.1, while Rank 3 declines further to 70.8. These positions still receive substantial citation activity, but the falloff is sharp. The drop between Rank 1 and Rank 3 is nearly 20%, illustrating how heavily LLMs weight the very top of their retrieval sets. Once a page slips out of the top three, its influence diminishes rapidly—regardless of how relevant or well-written the content may be.

By Rank 5, visibility plummets to an average score of 53.6

At Rank 5, the average visibility score is just 53.6, a full 40% lower than Rank 1. While still technically retrievable, content at this tier is rarely cited or directly quoted. Large language models prioritize brevity and clarity in their outputs, often selecting just 2–3 sources per answer. Any content that falls outside that short list is unlikely to surface, especially in zero-click formats where users don’t explore beyond the AI’s summary.

Brand-tagged citations show a similar pattern—from 65 down to 46

We also observed this trend among brand-specific placements (labeled Brand1 through Brand5 in the dataset). Visibility scores for these followed a nearly identical decay curve—from 65.4 at Brand1 down to 46.0 by Brand5. This suggests that LLMs don’t just consider rank position—they also weigh brand prominence and name clarity when selecting what to include. If your brand name is mentioned but buried or inconsistently presented, it’s unlikely to sustain citation performance.

What this means for content teams

Unlike traditional search, where lower-ranked content might still earn clicks or impressions through organic discovery, generative outputs are extractive and compressed. If your content does not appear in the top three retrieval slots, it is almost never cited. Visibility is not distributed across a long tail—it is stacked at the head.

To earn placement in AI summaries, content must be optimized for depth, clarity, and structure—and it must outperform whatever currently holds the top three positions.

This requires more than publishing good content. It demands that your content be:

• Faster to load

• Cleaner to parse

• More structured for summarization

• More confidently branded

• More comprehensive in scope

What structural features correlate with visibility?

When it comes to generative engine optimization, the structure of your page is just as important as its content. Large language models don’t “read” like humans. They scan, segment, and extract. They rely on pattern recognition, semantic markup, and positional consistency to determine what content is useful, quotable, and safe to summarize. Our citation dataset shows a clear and measurable preference for pages that are structurally optimized—down to the level of headings, bullet formatting, and HTML markup.

Structured formats—tables, lists, and rankings—dominate high-performing pages

Pages that include comparison tables, product rankings, or side-by-side feature breakdowns consistently appear at the top of AI-generated citations. This is no accident. These structures mirror the answer formats LLMs are trained to generate—ranked lists, pros and cons, summaries by feature or pricing tier. When a model sees a cleanly formatted list of “Top 10 X for Y,” each with consistent descriptors and brand mentions, it has a high-confidence path for summarization. That makes your content more likely to be cited.

In our dataset, these pages had the highest average citation density—frequently surfacing in Rank 1 or 2 slots, particularly in commercial or decision-stage prompts.

Pages using semantic HTML—especially H2s, H3s, and bullet points—perform better

Semantic clarity matters. Pages that use proper heading hierarchy (H1, H2, H3), bullet lists (<ul>), and tables (<table>) are cited more often than those that rely on stylized divs or text formatting alone. This is because LLMs are trained on HTML-rich web corpora. They look for predictable markup to infer structure and meaning. When you provide that structure through semantic HTML, you reduce ambiguity—and increase the chance that your content will be selected and reused.

This isn’t just about accessibility. It’s about AI-readability. Clean structure means faster parsing, more accurate chunking, and easier retrieval.

Repeated, specific brand mentions improve citation stability

Pages that name-drop their own brand—or clearly identify the brands they’re referencing—are cited more consistently than those that rely on vague phrasing like “this tool” or “a popular platform.” This shows up in both visibility scores and brand-labeled citation trends across the dataset.

From an LLM’s perspective, proper noun repetition helps resolve ambiguity. It strengthens entity recognition and makes it easier for the model to generate confident citations or recommendations. If your brand isn’t mentioned in the same way across sections, you reduce your retrieval surface and make summarization riskier for the model.

To stay visible, your brand must be specific, consistent, and semantically distinct throughout your content.

Clean content layouts outperform dynamic or JavaScript-heavy pages

Generative engines index the web like browsers with limited rendering capacity. They struggle with JavaScript-heavy pages, dynamic tabs, and content hidden behind interaction triggers. Our analysis shows that clean, static HTML pages are cited far more often than those requiring advanced rendering.

If your most important product information is hidden in accordions, pop-ups, or JS-triggered dropdowns, there’s a high likelihood it’s being skipped by LLM crawlers. AI citation volume does not favor design complexity—it favors simplicity and structure.

Pages without subheadings, anchors, or formatting rarely break into the top 5

One of the most consistent negative signals in our dataset was the absence of skimmable formatting. Pages that lacked clear subheadings, logical sectioning, or consistent formatting almost never appeared in the top five cited results.

These pages may have strong content, but without visible structure, they are difficult to parse and dangerous to summarize. From a model’s point of view, dense text without section labels introduces too much uncertainty. LLMs avoid citing sources they can’t safely excerpt.

What this means for content teams

To earn citations from generative engines, your content must not only answer the query—it must be easy to interpret at the code and layout level. This means prioritizing:

• Semantic HTML for all major structural elements

• Skimmable formatting with lists, headings, and clear visual segmentation

• Redundant brand/entity labeling for easy recognition

• Avoidance of rendering dependencies like JavaScript or dynamic DOM changes
  
  

If you build your pages like a decision tree—with clear branches and labels—models will find you. If you bury the insight in dense, unstructured prose, they won’t.

What does each ai model prefer? Format bias by engine

One of the most overlooked dimensions in generative engine optimization is model-specific behavior. Not all AI models evaluate content the same way. While there are common traits across the board—like a preference for structure, clarity, and brand specificity—our data shows that different engines apply slightly different weighting when choosing which content to cite. Understanding these format biases can help marketers tailor their content to the strengths and tendencies of each engine.

GPT-4o prefers highly structured, comparison-oriented content with brand neutrality

OpenAI’s GPT-4o produces the most citation-dense results in the dataset—averaging 25.7 citations per response despite only representing 2.6% of total citation volume. This suggests GPT-4o is more selective, surfacing fewer results overall but leaning heavily on structured, multi-part pages when it does.

The top-cited content in GPT-4o outputs tends to be:

• Cleanly formatted comparison pages

• Tools or software guides with numbered rankings

• Brand-neutral language with descriptive clarity

This means GPT-4o rewards objectivity and structure. It avoids overly branded, promotional content unless the brand is already established as authoritative. Pages that resemble editorial product reviews or data-backed rankings are more likely to appear in its generative summaries.

Gemini 2.0 rewards structure, but pulls from a broader domain mix

Google Gemini 2.0 accounts for 25.3% of total citations in the dataset and produces an average of 13.3 citations per result. This positions it as a middle ground between the density of GPT-4o and the breadth of Perplexity.

While Gemini still favors clean structure and brand clarity—especially in comparison guides—it draws from a wider mix of domain types, including forums, niche publications, and brand-owned blogs. It also shows stronger brand clustering, often citing multiple pieces from the same domain in response to a single prompt.

If you want to perform well in Gemini, publish content that is:

• Organized into clearly segmented subtopics

• Repeatedly tied to your brand (brand mentions in headings, tables, etc.)

• Supported by a network of internal content across related terms

Gemini appears to respond well to semantic breadth and domain consistency.

Perplexity Sonar prioritizes breadth and freshness over authority

Perplexity accounts for 20.8% of total citations and is notable for its wider domain diversity. It frequently cites Reddit threads, niche industry blogs, and community-generated content that other engines ignore.

This suggests that Perplexity is more exploratory in its retrieval. Rather than relying solely on established publishers or high-authority domains, it gives weight to recentness, engagement signals, and conversational utility. It treats forums and social discourse as valid sources alongside traditional web content.

To gain traction in Perplexity:

• Publish on multiple platforms, including community spaces and long-tail keywords

• Optimize content for relevance over polish—informal, useful answers work

• Ensure your content is updated regularly to stay within Perplexity’s recency windows

While this engine is less selective, it still prefers clear formatting. Even in blogs or Reddit-style content, skimmable structure improves visibility.

Google AI Overview leans toward large, mainstream publishers

Google’s AI Overview—the model powering featured AI answers in Google Search—produced 51.4% of all citations in our dataset, making it by far the most dominant engine.

This engine shows a strong preference for:

• Frequently updated content

• Established publishers (e.g., CNET, PCMag, Investopedia)

• Structured editorial formats (how-tos, lists, product roundups)

While Google AI Overview isn’t necessarily limited to high-authority sites, it consistently elevates pages that combine freshness, strong structure, and editorial tone. Brand-specific content performs best when paired with journalistic clarity and decision-stage framing.

If you want to win in Google AI Overviews:

• Act like a publisher: update often, cover categories, diversify formats

• Optimize for clarity at every level—titles, sections, takeaways

• Use named entities (brand, product, category) prominently and consistently

What this means for content teams

The takeaway is simple but important: not all AI engines reward the same formats equally.

If you're publishing generic content for "AI search" without accounting for these model differences, you're missing an optimization layer that’s already shaping which content wins.

Each engine is training on different signals. Your job as a marketer is to match structure to context—and format to engine behavior.

Informational vs commercial intent: What gets rewarded?

Not all queries are created equal—and neither is the content that ranks for them. Our analysis of over 67,000 citations across AI search engines reveals a clear pattern: generative engines reward different content formats based on the intent of the prompt. Informational pages dominate top-of-funnel (TOFU) visibility, but they quickly lose relevance when users begin making decisions. As prompts shift from “What is X?” to “Which X should I use?”, content requirements change—dramatically.

Informational content succeeds early—but fades at the point of decision

Wikipedia is one of the most cited domains in our dataset, appearing in over 1,300 rows and across 38 unique search terms. This makes it a staple for definitional and glossary-style prompts, especially at the awareness stage. When a query requires a factual explanation or industry-neutral framing, Wikipedia is often the first citation selected by engines like Gemini and Perplexity.

However, this dominance evaporates when intent shifts to decision-making. For prompts like “Best LMS platforms” or “Top CRM tools for startups,” Wikipedia is rarely cited—if at all. Instead, LLMs turn to expert-driven content that offers structured comparisons, real-world use cases, and recommendation logic.

Commercial prompts reward expertise and specificity

In bottom-of-funnel (BOFU) queries, our data shows a decisive swing toward third-party authority domains. Sites like CNET, Zapier, Investopedia, and Thinkific consistently outrank Wikipedia and general blogs when the query involves product evaluation, purchasing, or vendor selection. 

To quantify this:

• SEO tools account for over 6,000 citations, making it the top-performing commercial category.

• LMS platforms, CRM tools, and HR software each earned 4,000–5,000 citations.

• These categories were cited disproportionately in prompts with clear commercial intent—“best,” “top,” “alternatives,” or “vs” style keywords.

LLMs appear to associate structured commercial content with greater confidence and reliability at the decision stage. The implication is clear: authority in informational content is not enough. If your content doesn’t help someone choose, compare, or act—it won’t get cited in BOFU scenarios.

Hybrid content consistently outperforms both pure explainer and pure pitch

Interestingly, the most cited pages in commercial categories are not pure sales pages, nor are they simplistic explainers. Instead, they’re hybrid formats—pages that blend education with recommendation. These include:

• Vendor comparisons with a short introduction to the category

• Guides that define a concept before ranking top tools

• Lists that include both pros/cons and contextual use cases

This hybrid model mirrors how real buyers research. They want to understand the space, but they also want a clear path to action. LLMs reflect that preference in the sources they cite.

Plain “What is X?” content underperforms without added value

Many marketers still rely on SEO-driven “What is [term]?” articles to gain top-of-funnel traffic. While this format may rank in traditional search, it performs poorly in generative results unless it offers additional depth. Our data shows that definition-only pages rarely get cited—unless they’re from extremely high-authority sources or are tied to subsequent comparison content.

To earn citations from generative engines, your TOFU content must:

• Offer more than a definition

• Introduce frameworks, examples, or applications

• Link to deeper, decision-stage content that adds downstream value

What this means for content teams

To win visibility across the funnel:

• Use definition-style content for early-stage prompts, but connect it to high-value content downstream.

• Create hybrid formats that educate and recommend in the same page.

• Design your BOFU content with decision logic, structured comparison, and brand signals.

• Replace generic explanations with layered expertise—especially in commercial categories.

In AI search, informational intent gets you in the door. Commercial clarity is what gets you cited.

Which sites win long-term in AI search? 

It’s easy to assume that a single well-optimized article can put your brand on the generative search map. But the data tells a different story. The websites that consistently earn citations from AI models aren’t just publishing great content—they’re doing it repeatedly across a wide range of relevant topics.

Top performers combine citation volume with topic breadth

Two domains stand out in our analysis:

• Wikipedia earned 1,382 citations across 38 unique prompts.

• Forbes followed with 1,141 citations across 31 prompts.

These are what we call composite performers—sites that don’t just succeed with a viral page or timely guide, but maintain visibility across dozens of different query types. Their influence isn’t confined to one vertical, one content format, or one high-ranking URL. Instead, they’ve established domain-level authority by consistently showing up as useful across multiple AI-generated responses.

This matters because generative engines like ChatGPT, Gemini, and Perplexity are citation pattern learners. They don’t just evaluate content in isolation—they identify which domains reliably provide quality information across prompts. The more times your domain shows up in different contexts, the more likely it is to be retrieved again.

High-breadth + high-volume = long-term generative visibility

In our dataset, domains that received over 1,000 citations across more than 25 unique search prompts consistently outperformed competitors in both ranking position and overall visibility. These sites benefit from a compound effect:

• Every new page they publish has a higher chance of being retrieved.

• Their existing pages are reinforced by topical adjacency and internal linking.

• Their brand becomes part of the model’s implicit trust set.

In contrast, “one-hit wonders”—sites with a single high-performing page but no supporting content—tend to fade from generative outputs over time. Without topic breadth, there is no reinforcement. Without depth, there is no coverage. LLMs do not see that site as a durable authority.

AI engines reward domains they “trust to explain more than one thing”

The underlying logic is similar to human search behavior. If a site helped you once, you’re more likely to trust it again. LLMs behave the same way: they gravitate toward domains that have proven useful across a variety of questions. They learn that some brands can explain one thing very well—but others can explain everything their audience needs to know.

That’s why composite performance is the best long-term signal of GEO success. It means your site doesn’t just rank occasionally—it’s part of the answer landscape.

What this means for content teams

• Don’t stop at a high-performing blog or comparison page—build clusters around it.

• Target multiple search intents across each thematic category (intro, vs, best, alternatives, reviews).

• Create internal link structures that show thematic depth and surface adjacency.

• Audit your content library by “prompt footprint”—how many distinct AI queries could each page support?

To sustain visibility in AI search, you need more than a great page—you need a domain that models recognize as reliable, versatile, and worth repeating.

Which industries attract the most visibility in generative ai search?

AI models are not neutral when it comes to topic coverage. Certain industries—and the content published within them—consistently earn more citations across generative engines like GPT-4o, Gemini, and Perplexity. These verticals tend to reflect clear commercial intent, complex decision-making, or highly searched tools and platforms.

Software and SaaS lead AI visibility

Software-related queries dominate generative search citations. Across CRM platforms, SEO tools, HR systems, cybersecurity solutions, and project management apps, software accounted for more than 30% of total citations in the dataset. This reflects both the density of product options and the need for structured comparisons in decision-stage content.

EdTech and online learning are citation-rich niches

Content related to eLearning platforms and course builders (e.g., “best LMS,” “online course platforms”) earned a disproportionate share of citations. Thinkific and LearnWorlds, for example, consistently appeared in top-ranked outputs—not because of brand size, but because they published comparison-rich content in a highly queried niche.

Finance and investing content holds strong mid-funnel influence

Financial education and investing platforms (like Investopedia-style content) earned a combined 5–6% of total citations, often surfacing in queries that mix informational and commercial intent. These citations spanned personal finance, fintech tools, and investment advice—categories where trust, clarity, and structured examples matter.

Healthcare, travel, and cybersecurity have niche strength

Although smaller in share, industries like healthcare software, travel booking tools, and cybersecurity platforms showed consistent presence across multiple prompts, especially where decision complexity or compliance issues elevate content value. These sectors benefit from structured, expert-driven content—particularly buyer guides and product explainers.

What this means for content teams

If your brand operates in one of these high-performing verticals, the opportunity to win citations in AI search is both present and proven. And if you’re in a lower-citation vertical, the gap may be even more valuable—fewer players investing in AI visibility means a first-mover advantage is still possible.

Don’t just ask who gets cited. Ask which topics AI models consider worth citing—and shape your content accordingly.