Which LLM Cites Whom: Model-by-Model Citation Analysis for Enterprise Brands

LLM citation analysis enterprise brand visibility is no longer a single measurement. Across thousands of queries run through Velocity AI's proprietary GEO tooling, one pattern repeats with striking consistency: a brand that earns strong citations in Gemini can be virtually absent from ChatGPT for the same category query. Aggregate visibility scores hide this divergence entirely, and for enterprise brands making content investment decisions, that blind spot is expensive.

This post breaks down how citation behavior differs by model, what drives those differences at a structural level, and what Fortune 500 marketing and strategy teams need to do about it.

The Aggregate Score Problem

Most enterprise teams tracking LLM visibility today are working with blended numbers. A platform reports that a brand appears in 42% of relevant AI responses. That number feels meaningful. It is not, on its own, actionable.

Consider what that 42% might actually contain: consistent citation in Gemini across product category queries, sporadic mention in Perplexity for branded searches, and near-zero presence in ChatGPT for the comparison and recommendation queries where purchase intent is highest. The aggregate looks healthy. The actual buyer journey coverage is not.

This is the core argument for model-level measurement. As we detailed in Building a GEO Scorecard: How to Measure LLM Visibility for Enterprise Brands, visibility measurement needs to be segmented by query type, query stage, and, critically, by the model receiving the query. Adding the model dimension transforms a scorecard from a status report into a decision-making tool.

How Velocity AI Tests Citation Behavior Per Model

Velocity AI's GEO tooling runs structured prompt batteries independently across four primary models: OpenAI GPT-4o, Google Gemini 1.5 Pro, Anthropic Claude 3.5, and Perplexity. Each model receives the same query set, which spans three query types: category-level discovery queries ("What are the leading enterprise [category] platforms?"), comparison queries ("How does [Brand A] compare to [Brand B]?"), and recommendation queries ("Which [category] vendor should I evaluate for a 10,000-employee organization?").

Results are logged at the model level across four dimensions:

• Citation frequency: How often the brand appears across the query set
• Source type cited: Whether the citation traces back to owned content, press coverage, analyst reports, or third-party review platforms
• Mention context: Whether the brand appears as a primary recommendation, a secondary mention, or a comparative reference
• Sentiment consistency: Whether the framing is neutral, positive, or qualified with caveats

This produces a citation matrix, not a single number. The matrix is where the actionable divergence lives.

Why ChatGPT and Gemini Cite Differently

The divergence between OpenAI models and Gemini is not random. It reflects structural differences in how each model was trained and how each retrieval system evaluates authority.

OpenAI Models: Editorial Weight and Breadth

GPT-4o and its predecessors were trained on a broad corpus of web content with significant weighting toward editorial sources: journalism, long-form publishing, industry analysis, and high-domain-authority reference material. When OpenAI models surface citations in retrieval-augmented contexts, they tend to favor:

• Bylined editorial content from recognized publications
• Detailed product pages with structured content and clear entity signals
• Third-party analyst coverage (Gartner, Forrester, IDC-adjacent sources)
• Content that has accumulated broad inbound link signals over time

Brands with deep PR and analyst relations programs, or those with well-developed thought leadership archives, tend to perform disproportionately well here.

Gemini: Google's Authority Framework Applied to AI

Gemini's citation behavior is more legible to teams that understand Google's E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) framework, because Gemini effectively applies a similar logic. Google's model tends to favor:

• Content indexed and ranked well within Google Search
• Sources with strong topical authority signals in Google's knowledge graph
• Content that follows structured markup and entity disambiguation practices
• Google-adjacent properties including YouTube, Google Scholar, and Google Business content

A brand that has invested heavily in technical SEO, structured data, and Google ecosystem presence will often outperform in Gemini relative to its actual market position. The inverse is equally common: brands with strong editorial profiles but weak Google optimization can underperform in Gemini despite being widely cited in other models.

As explored in Reading Between the Models: Why Your Brand Visibility Varies Across AI Assistants, these structural differences mean that optimization strategies designed for one model can actively conflict with optimization for another.

The Source-Type Signal: What Each Model Values

Across Velocity AI's benchmark data, the source types driving citations break down in consistent patterns by model:

OpenAI GPT-4o

• Primary drivers: industry publication coverage, analyst firm mentions, detailed comparison content on high-authority domains
• Secondary drivers: well-structured product and solution pages, case study content with named clients and metrics

Google Gemini

• Primary drivers: owned web properties with strong Google indexing signals, YouTube content, Google Business profiles for localized brands
• Secondary drivers: press releases indexed and ranked in Google News, structured FAQ content with schema markup

Perplexity

• Primary drivers: recent content (Perplexity weights recency heavily), Reddit and forum discussions, technical documentation
• Secondary drivers: news coverage from the prior 90 days, comparison and review content from vertical-specific platforms

Anthropic Claude

• Primary drivers: long-form authoritative content, compliance and regulatory documentation, academic and research references
• Secondary drivers: detailed product documentation, white papers with citations

The practical implication is that a content investment in, say, a well-placed bylined article in a trade publication will drive measurable citation lift in GPT-4o but may produce little movement in Gemini. A structured data overhaul of owned web properties may flip that dynamic entirely.

Model Coverage Across the Buyer Journey

Enterprise buyers do not use a single LLM. Research-stage queries often happen in Perplexity, which surfaces recent and technical content. Discovery and comparison queries are increasingly common in ChatGPT, particularly through Microsoft Copilot integrations embedded in enterprise productivity suites. Gemini is the default model inside Google Workspace AI features and Google's AI Overviews in search. Claude is gaining ground in legal, compliance, and regulated-industry workflows.

This distribution means that for most Fortune 500 brands, a single-model optimization strategy leaves significant buyer journey coverage gaps. A procurement team evaluating a new enterprise vendor may encounter that vendor first in a Gemini-powered AI Overview during a Google search, then run a comparison query in ChatGPT, then do technical due diligence in Perplexity. If the brand is present in only one of those touchpoints, it is losing ground in the others without knowing it.

This fragmentation in the AI-mediated buyer journey parallels the cross-channel complexity that off-the-shelf GEO trackers are not designed to capture, because most commercial tools aggregate across models rather than separating them.

Allocating Content Investment by Model Gap

Model-level citation data becomes most valuable when it drives budget decisions. Once a brand has its citation matrix, the gap analysis reveals where incremental content investment produces the highest marginal return.

A brand with strong Gemini presence but low ChatGPT citation rates should prioritize:

1. Editorial placement in high-authority publications that OpenAI's training and retrieval systems weight heavily
2. Analyst relations outreach to firms whose content surfaces consistently in GPT-4o responses for the relevant category
3. Structured comparison content on the brand's own domain, optimized for the specific query patterns where ChatGPT cites competitors but not the brand

A brand with the inverse pattern, strong ChatGPT presence but low Gemini citation, should prioritize:

1. Technical SEO and structured data implementation aligned with Google's entity recognition frameworks
2. Google-ecosystem content including YouTube explainers, Google Scholar-indexed research, and optimized Google Business profiles
3. Schema markup and FAQ content that matches the structured query patterns Gemini rewards

The key discipline is sequencing investment by gap size and by the commercial weight of the model in the brand's specific buyer journey. That requires data, not assumptions.

Key Takeaways

• Aggregate scores obscure model gaps. A brand's overall LLM visibility score can look healthy while it is effectively absent from the model that handles the highest-intent buyer queries in its category.

• OpenAI and Gemini weight different authority signals. Editorial coverage and analyst mentions drive ChatGPT citations; Google indexing quality and E-E-A-T signals drive Gemini citations. The same content investment will not move both equally.

• Source type determines citation mechanics. Each LLM has identifiable preferences for the type of source it cites, from editorial and analyst content in GPT-4o to recent forum and technical content in Perplexity. Knowing which source type drives citations in each model is prerequisite to optimizing for it.

• Buyer journeys span multiple models. Enterprise procurement teams use different LLMs at different stages of evaluation. Brands that optimize for only one model cede ground at other stages of the buying process without visibility into the loss.

• Model-level data enables precise investment allocation. A citation matrix by model and query type turns content investment decisions from guesswork into gap-filling. Teams can prioritize interventions by the size of the gap and the commercial weight of the model.

• Velocity AI's GEO tooling provides the measurement layer. Running structured prompt batteries across GPT-4o, Gemini, Claude, and Perplexity independently, with logging by source type and mention context, produces the model-by-model intelligence that enterprise brands need to compete in AI-mediated discovery.