AI Citation Behavior Across Models: Consistent Source Preferences Across a Growing AI Landscape

Executive Summary

This is the third quarterly analysis in Yext Research's ongoing AI citations series, tracking how major AI models select and cite sources when answering location-based business queries. This quarterly report will build on our Q4 2025 findings.

Between Q4 2025 and Q1 2026, total citation volume reached 155.5M across 1,623 brands scanned. To isolate actual AI behavior from expanded coverage, we focus on the 770 brands present in both quarters: citations for this cohort grew 2.77× and citations per scan rose +28% — confirming that AI models are genuinely citing businesses more, independent of how many brands we added.

This quarter marks a fundamental transition point. What began as experimental citation behavior in select AI models has become standard practice across the entire ecosystem. Every model in our analysis demonstrated substantial citation volume growth. The implications extend beyond volume. AI models are diverging in their source selection preferences, creating distinct pathways for businesses within our scanning universe.

Key finding: Gemini and Perplexity favor first-party websites, directing 52.0% and 50.9% of citations, respectively. OpenAI shows stronger directory dependence at 36.1% listings citations, while Anthropic maintains the most balanced approach across control categories.

Perhaps most critically, controllability within our client portfolio remains high despite rapid scaling. At 79.9%, businesses in our scanning universe can influence four out of five citations through owned web content and managed listings. This represents a measurable, actionable opportunity rather than theoretical market potential.

Strategic Takeaways

Three patterns emerge from our client portfolio analysis that fundamentally reshape how businesses should approach AI visibility.

1. The models are not monolithic, and neither should be your strategy. Each AI model demonstrates consistent architectural preferences that persist as citation volume scales. Gemini's 52.0% website citation rate creates measurable advantages for clients with a comprehensive owned web presence. OpenAI's listings dependence – 36.1% of citations – rewards clients who maintain robust profiles across major and an extended network of publishers. Anthropic's balanced approach, with notable emphasis on social and review content (19.5%), requires multi-channel strength across owned content, listings, and review management.

   These aren't superficial differences. They reflect fundamental distinctions in how each model's retrieval architecture evaluates and selects sources. As our universe continues to scale, these preferences become more pronounced, not less. Businesses that recognize this divergence and build accordingly gain compounding advantages as citation volume grows.

2. Controllability advantage persists at scale. Despite AI models accessing increasingly diverse source categories, 79.9% of citations within our client portfolio point to sources businesses can directly influence. This controllability score represents real competitive advantage for businesses that execute comprehensive brand visibility management.

   The 2.4-point controllability decline from Q4 reflects AI models accessing broader source diversity rather than fundamental erosion of business influence. As our scanning universe expands and models mature, the brands that maintain strong positions across both owned content and managed listings presence will capture an outsized share of AI citation volume.

3. Portfolio-level growth signals systematic transformation, not sampling artifacts. The 770-brand like-for-like cohort grew 2.77× quarter-over-quarter, and citations per scan rose +28%. This is the right lens: it controls for expanded coverage and isolates genuine AI behavior change. The 277% increase across our consistent 770-company cohort represents genuine evolution in AI citation behavior. We're not observing expanded coverage or new client additions – we're measuring the same businesses generating dramatically more citations as AI models mature their source attribution systems.

This transformation creates first-mover advantages that compound over time. Businesses with established optimizations based on visibility are building citation momentum as models scale their referencing behavior. Conversely, businesses without comprehensive digital infrastructure fall further behind each quarter as the window for easy entry narrows.

Findings

1. Citation Volume by Model

Citation volume within our 770-company client cohort reveals both the scale and trajectory of AI's evolution toward source-backed responses.

Perplexity generated 53.3M citations (34.3% share), Gemini (48.4M), OpenAI (34.9M), and Anthropic (18.9M) round out the model volume picture.

Across the 770-brand cohort, the cleaner signal is citations per scan, which rose +28% – confirming AI models are citing businesses more, even after controlling for expanded coverage.

What does this mean for businesses? The window for establishing strong citation foundations is narrowing rapidly. Models are scaling their referencing behavior faster than businesses are optimizing their visibility. Brands that delay investment in comprehensive content and listings management risk falling behind as citation volume accelerates across the AI landscape.

2. Zone of Control by Model

Model citation preferences create distinct optimization pathways within our client universe, revealing architectural differences with direct strategic implications. The data demonstrates that understanding these preferences isn't optional – it's fundamental to an effective AI visibility strategy.

Gemini shows the strongest bias toward first-party content, directing 52.0% of citations to brand-controlled websites and local pages. This preference creates measurable advantages for clients with comprehensive owned web presence, structured data implementation, and location-specific content depth. Perplexity follows closely at 50.9% website citations, suggesting similar architectural preferences for authoritative, brand-owned sources.

OpenAI diverges significantly, with 36.1% of citations pointing to listings – the highest dependence among models we analyzed. This pattern means businesses optimizing for OpenAI visibility should prioritize comprehensive listings profile management, accurate business information across major platforms, and consistent data across an extended publisher network.

Anthropic maintains the most balanced approach, with notably higher social and review citation rates at 19.5% – nearly four times higher than other models. This suggests Anthropic's retrieval system places greater weight on user-generated content and social proof when evaluating business information. For businesses, this means active review management and social media presence become critical components of Anthropic optimization strategy.

These patterns aren't random preferences – they reflect fundamental differences in how each model's architecture retrieves and evaluates sources. As our client universe continues to scale, businesses that recognize these distinctions and build multi-channel strategies accordingly will capture disproportionate citation volume as AI models mature.

Key takeaway: The controllability score (Websites + Listings combined) moved from 88.2% in Q4 to 79.9% in Q1, a shift of 8.3 points. Given the scale of coverage expansion this quarter, the stability of this signal is itself the story: the source mix that AI models draw from has not fundamentally changed as volume scaled.

3. Quarter-over-Quarter Trend Analysis

The three-quarter trend shows consistent volume growth across all models. Raw volumes grew substantially quarter-over-quarter, but the jump reflects both genuine citation growth and Yext's expanded scan coverage (1,115 brands in Q4 to 1,623 in Q1, ~2.5× more total scans). The like-for-like 770-brand cohort is the cleaner read: 2.77× growth overall, +28% on a per-scan basis.

Raw citation counts – reflects both genuine citation growth and expanded scan coverage. See methodology for per-scan figures.

Perplexity went from 7.0M to 53.3M citations (34% of total).

Gemini went from 5.0M to 48.4M citations (31% of total).

OpenAI went from 3.2M to 34.9M citations (22% of total).

Anthropic went from near-zero to 18.9M citations (12% of total).

4. Distribution Patterns

4A. Client Portfolio Performance by Sector

Sector performance within our 770-company cohort reveals how AI citation activity varies by business type, though these patterns reflect our client portfolio composition rather than broader market dynamics. The data provides insight into how different business models generate citation volume as our scanning universe scales.

Food & Beverage leads citation volume at 46.9M (31.2% of total). When consumers ask AI about nearby dining options, our Food & Beverage clients appear frequently in responses, driving substantial citation volume.

Business Services clients follow at 29.6M citations (19.7%), while Healthcare clients generated 25.3M citations (16.8%).

Retail showed 16.1M citations (10.7%), potentially indicating different citation patterns for product-focused versus location-focused business models within our client universe. This divergence is worth monitoring as it may reflect how consumers interact with AI for discovery across different business types.

Across all sectors, citations per scan rose +28% quarter-over-quarter. So, what emerges is a clear picture: businesses operating in sectors with high location-query frequency see accelerated citation growth as AI models mature. This isn't market dynamics at work – it's the intersection of our client portfolio composition with AI models' increasing reliance on location-specific business information.

4B. Most Cited Third-Party Domains

Domain-level analysis reveals which sources AI models rely on most heavily, providing practical insight into where brands should prioritize their visibility strategies. The top cited domains are dominated by listings and review platforms – underscoring the importance of maintaining accurate, complete profiles across these properties. First-party brand domains are also heavily cited but are excluded from this list.

Conclusion

Our analysis of 155.5M total citations (and 106M from the consistent 770-company cohort) confirms that AI citation behavior is maturing across all major models. The cleanest signal: citations per scan rose +28% for the like-for-like cohort, demonstrating genuine growth in how often AI models cite businesses, independent of Yext's expanded scan coverage.

Model differentiation is accelerating rather than converging. Gemini and Perplexity's preference for brand-controlled websites and local pages (52.0% and 50.9% respectively) creates distinct advantages for businesses with comprehensive owned content strategies. OpenAI's listings dependence (36.1%) rewards comprehensive listings management, while Anthropic's balanced approach demands multi-channel strength.

The controllability advantage persists at scale. At 79.9%, businesses within our scanning universe maintain substantial influence over their AI citation landscape despite models accessing increasingly diverse source categories. This controllability represents competitive advantage for businesses that execute comprehensive brand visibility strategies rather than piecemeal optimization approaches.

As our universe continues to scale and AI models mature their citation systems, the strategic imperative becomes clear: businesses need multi-channel optimization that accounts for architectural differences across AI models. The window for establishing strong citation foundations is narrowing as volume accelerates, making comprehensive digital infrastructure investment increasingly urgent for sustained AI visibility.

Control Category Framework

To understand which citations a brand can influence, each source is classified into one of four control tiers. This framework drives all analysis in this report and provides the basis for the controllability score.

CATEGORY	CONTROL	DESCRIPTION
Websites	Full	Brand-owned domains where businesses control all content, structure, and updates
Listings	Moderate	Directory profiles where brands can manage core business data but not page structure
Reviews & Social	Limited	Platforms where brands can respond and engage but cannot control organic content
News/Forums/Gov't	None	External sources where brands have no direct influence over content or structure

The controllability score combines categories 1 and 2 (Websites + Listings) – the share of citations pointing to sources where brands can directly manage information. This metric summarizes how much of the AI citation landscape a business can realistically influence.

Limitations

Several factors should be considered when interpreting these findings from our client portfolio analysis.

• Response variability introduces measurement uncertainty. AI responses are non-deterministic, meaning identical queries may yield different citations across multiple requests. While our large-scale analysis minimizes this impact, individual citation decisions remain probabilistic rather than deterministic.
• Classification boundaries use domain-level heuristics that may not capture edge cases, such as branded subdomains on third-party platforms or co-branded content arrangements. Our categorical framework provides directional accuracy rather than perfect precision.
• Geographic coverage reflects our client portfolio composition rather than uniform market representation. Some regions have limited data representation, particularly where certain AI models have restricted availability or where our client presence is smaller.
• Portfolio composition effects mean our sector and geographic distributions correspond to our client base concentration rather than broader market dynamics. These patterns provide insight into client performance within our scanning universe while avoiding generalizations about markets we don't comprehensively cover.

Areas for Further Research

This client portfolio analysis opens several avenues for deeper investigation as our universe continues scaling.

• Citation consistency measurement could quantify how often identical queries return the same sources across multiple requests. Understanding this variance would help businesses gauge the reliability of their AI visibility positioning and inform optimization strategy confidence intervals.
• Cross-model citation overlap analysis could identify sources that appear consistently across all AI models. These "universal citations" would reveal the highest-leverage optimization targets for businesses seeking maximum impact across the AI landscape.
• Platform impact correlation could compare controllability scores for businesses using comprehensive digital presence management versus those with unmanaged profiles. This analysis could quantify the measurable value of active optimization within our client universe.
• Review signal correlation could examine relationships between review volume, star ratings, and citation probability across different AI models. This analysis could reveal whether review management directly influences AI visibility or operates through indirect reputation signals.

Methodology

Data is sourced from Yext's AI Citations Explorer, which systematically queries AI models with location-aware prompts for businesses within our client universe. The system scans for clients loaded into our platform, capturing how AI models cite these businesses and their competitors when responding to relevant location-based queries.

Our analysis focuses on a consistent cohort of 770 companies scanned in both Q4 2025 and Q1 2026, enabling quarter-over-quarter comparison that isolates actual citation behavior changes from portfolio expansion effects. This cohort methodology ensures that growth percentages reflect genuine increases in citation volume rather than expanded scanning coverage or new client additions.

The analysis covers clients across 7 industry sectors and 4 geographic regions, with scanning coverage expanding as new businesses join our platform. Citation volumes reflect our client portfolio composition and scanning coverage rather than general market dynamics, providing actionable insights for businesses within our managed ecosystem while avoiding generalizations about markets we don't comprehensively scan.

Each citation is classified into one of four control categories based on the degree of influence a business has over the source. Models are queried under consistent conditions each quarter to enable meaningful trend analysis, with all citations captured through direct API access to ensure measurement consistency.

Important context: Sector distributions correspond to our client base concentration rather than broader AI usage patterns or market dynamics. This client-focused methodology enables actionable insights for businesses within our scanning universe while maintaining appropriate scope boundaries around what our data can reliably demonstrate.