§01Why the models cite so differently
Each model was trained differently, uses different retrieval mechanisms, and has different defaults around brand specificity. Perplexity is explicitly a search product — it retrieves live web results and synthesizes them, so it naturally cites more URLs. Claude is designed for thoughtful, concise answers — it tends to recommend fewer brands but with higher confidence. ChatGPT balances familiarity with helpfulness. Gemini is optimizing for Google ecosystem alignment.
The practical implication: treat them as separate channels with separate rules, not as interchangeable. A strategy optimized for Perplexity will underperform on Claude.
§02Claude
Citation style: Selective and high-confidence. Claude cites fewer brands per response than any other model — but when it does, the citation is almost always among the genuine top 3 in the category.
What it rewards: Specificity and clarity. Claude responds well to brands that have a clear, differentiated explanation of who they’re for and what makes them different. Generic “AI-powered platform” positioning rarely makes it into Claude answers.
What it penalizes: Vagueness and noise. If your positioning sounds like every other tool in the category, Claude will default to the established brand it knows is safe to recommend.
Being cited by Claude puts you in a small group. On buyer-intent queries, Claude names an average of 2.8 brands per response — compared to 7.2 for Perplexity.— from our 1,000-query benchmark
How to improve Claude citation: Build pages that directly answer “[your product] vs [competitor]” and “[your product] for [specific use case].” Claude responds to structured, comparison-style content that lets it make a specific recommendation.
§03ChatGPT
Citation style: Brand-recognition weighted. ChatGPT heavily favors names its training data has seen often. Larger, more established brands have a systematic advantage.
What it rewards: Brand footprint. Third-party mentions on Reddit, G2, Product Hunt, and Hacker News carry significant weight. The more places your brand name appears alongside positive signals, the better your ChatGPT citation rate.
What it penalizes: New or niche brands, especially in fast-moving categories. ChatGPT’s training cutoff means it can recommend outdated tools in categories that have evolved quickly.
How to improve ChatGPT citation: Focus on third-party presence. G2 reviews, Reddit threads, comparison articles on third-party sites, and mention-rich community discussions all feed into the training signal that ChatGPT draws on.
§04Perplexity
Citation style: Broad and URL-heavy. Perplexity retrieves live results and cites them, so it behaves more like a search engine than the other models. Average of 7.2 brand citations per response.
What it rewards: Crawlable, well-structured content that directly answers the query. Clean URL structures, fast pages, and content that puts the answer near the top of the page. Comparison and “alternatives to” pages perform especially well.
What it penalizes: JS-heavy pages that are hard to crawl, thin content, pages that require login to access the key content.
How to improve Perplexity citation: If you have a technical SEO problem (slow pages, poor crawlability), fix that first — it directly hurts Perplexity performance. Then build the “best X for Y” and “X alternatives” pages, because Perplexity surfaces those heavily.
§05Gemini
Citation style: Highest variance of the four. The same query asked twice can return completely different brand sets. Google Search integration means it sometimes surfaces very fresh content that the other models haven’t trained on.
What it rewards: Google ecosystem presence — strong Search rankings, Google Business Profile completeness, YouTube presence, and structured data. Brands that rank well on Google Search tend to get cited by Gemini.
What it penalizes: Brands with poor Google presence. Unlike the others, Gemini’s citation behavior is closely tied to organic search signals.
How to improve Gemini citation: Traditional SEO wins here more than for the other models. Technical SEO, structured data (Schema.org), and Google Search ranking improvements flow directly into Gemini citation rates.
§06Strategy implications
Given how differently these models behave, the highest-leverage GEO strategy isn’t model-specific — it’s the things that help across all four:
- Comparison pages — these are the single page type that performs well across Claude, ChatGPT, and Perplexity simultaneously.
- Third-party mentions — Reddit, G2, and review sites feed both ChatGPT training data and Perplexity live retrieval.
- Specific, differentiated positioning — helps Claude and is table stakes for being mentioned on any non-branded query.
Start with your overall score, then look at your per-model breakdown. If you’re doing well on Perplexity but not Claude, you have a clarity/positioning problem. If you’re doing well on Claude but not Perplexity, you have a crawlability or content structure problem.