Why Claude Recommends Different Companies Than ChatGPT

Same company ranks #1 on Claude, invisible on ChatGPT. Visibility differs 63 points across platforms.

Marco Di Cesare

Marco Di Cesare

January 14, 2026 · 12 min read

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I ran 96 YC-backed companies through ChatGPT, Claude, and Gemini. Each company got 16 prompts: 5 category queries (brand-agnostic like "Best X tools in 2025"), 5 brand queries, and 5 competitive queries.

The correlation between ChatGPT and Claude citation rates? 0.39.

That's weak. A company ranking well on ChatGPT has less than 40% statistical relationship with how it ranks on Claude. For Claude vs Gemini, it drops to 0.17—basically noise.


The Numbers

PlatformAvg Citation RatePosition AvgPosition Std Dev
Gemini71.3%1.82.2
ChatGPT69.7%6.319.5
Claude67.3%2.06.5

The averages look similar. But look at ChatGPT's position standard deviation: 19.5.

Gemini's standard deviation is 2.2. When Gemini cites you, it's consistently at position 1-2. When ChatGPT cites you, it could be position 1 or position 140. Same company, same query type.


The Distribution

VisibilityChatGPTClaudeGemini
0%000
1-24%040
25-49%000
50-74%928880
75-99%1214
100%322

Three patterns:

  1. Claude has a floor. 4 companies get cited in fewer than 25% of queries. ChatGPT and Gemini have zero companies in this bucket.

  2. Gemini clusters high. 14 companies reach 75-99% visibility on Gemini. ChatGPT has just 1.

  3. ChatGPT is binary. Almost everyone lands at 50-74%. You're either visible or you're not.


The Extremes

Position divergence is where things get insane:

CompanyChatGPTClaudeGemini
CloudHumans#140#1#1
Leapingai#96#62#1
Newscatcher API#72#1#9
AgentHub#56#15#1
Momentic Marketing#37#1#1

CloudHumans does customer support software. Claude and Gemini put them first. ChatGPT buries them at position 140.

That's not a ranking difference. That's invisibility.


Claude's Blind Spots

Four companies get cited well by ChatGPT and Gemini but barely exist on Claude:

CompanyChatGPTClaudeGemini
Leapingai69%6%62%
CloudHumans69%12%69%
AgentHub69%19%69%
Noble Stay69%19%69%

Leapingai is an AI call center company. ChatGPT cites them in 11 out of 16 queries. Claude cites them in 1.

When Claude does cite them, it's at position 62. When it doesn't, they don't exist.


Gemini's Advantage

Gemini has 14 companies at 75-99% visibility. ChatGPT has 1.

CompanyChatGPTClaudeGemini
Vapi69%69%94%
Waydev69%69%94%
Mem069%69%94%
Unsloth69%69%94%
Newscatcher API69%63%88%
Prosper69%69%88%
Abacum69%69%81%

Why? Gemini uses Google Search. If you rank well on Google, Gemini sees you.


ChatGPT Buries, Claude Elevates

5 companies get buried past position 20 on ChatGPT. Only 1 on Claude. Zero on Gemini.

Companies where Claude and Gemini put first, ChatGPT buries:

CompanyChatGPTClaudeGemini
Hyperbrowser#12#1#1
Afterquery#9#1#1
Codeviz#14#1#1
Mayalabs#5#1#1
LLM Data#13#7#1

Position 1 means "Use Afterquery." Position 9 means "Also consider Afterquery, among others..."


Sentiment Divergence

I tracked sentiment across all responses:

PlatformPositiveNeutralNegative
Claude12488525
ChatGPT8596223
Gemini4110459

Claude generates 46% more positive mentions than ChatGPT. And nearly 3x more than Gemini.

Gemini is almost entirely neutral. It cites you, but it doesn't advocate for you.


Technical GEO Doesn't Predict Visibility

I calculated a GEO score for each company based on schema markup, llms.txt, meta tags, and content quality. Then I correlated each factor with actual visibility:

FactorCorrelation
Sentiment Score0.68
GEO Score (composite)0.18
llms.txt file0.18
Content Quality0.12
Schema Markup0.10

Sentiment is the only factor that actually predicts visibility.

The technical stuff? Basically noise.

I dug into the academic research to understand why. The answer is fascinating: LLMs destroy schema markup during tokenization. When a model processes "@type": "Organization", it breaks this into individual tokens—@, type, Organization—that become indistinguishable from the same words in regular prose.

The markup's semantic meaning literally gets shredded before the model ever sees it.

Same with llms.txt. LLMs use semantic retrieval—they query vector indexes based on meaning, not metadata files. Your llms.txt is invisible to this process unless it happens to contain semantically relevant text.

What the research says actually matters

According to recent empirical studies:

FactorImpact
Brand search volume0.33 correlation (strongest technical signal)
Wikipedia presence47.9% of ChatGPT's top-10 citations
Cross-platform presence2.8x more likely to appear if on 4+ platforms
Content freshness65% of AI bot hits target content from past year

The pattern is clear: LLMs don't reward technical optimization. They reward market presence and reputation.

This explains why Lightly (GEO score 39) outperforms companies with scores in the 70s-80s. They have stronger market presence and more consistent positive coverage.


Only 3 Companies Win Everywhere

Out of 96 YC companies, only 3 achieve 75%+ visibility on all three platforms:

CompanyChatGPTClaudeGeminiGEO Score
Flow.club100%94%94%67
Sully100%100%75%59
Weave75%100%75%72

That's 3%. Everyone else has at least one platform where they're significantly weaker.

Flow.club does virtual coworking. Sully is a healthcare AI assistant. Weave is patient communication software.

What do they have in common? All have clear, established categories. All have consistent messaging. None are in crowded AI-tool categories where dozens of competitors fight for the same keywords.


Why This Happens

Three architectural differences explain the divergence:

Different Search Backends

  • ChatGPT uses Bing
  • Gemini uses Google Search
  • Claude uses multiple providers

CloudHumans probably ranks well on Google but poorly on Bing. Gemini cites them first. ChatGPT buries them at position 140.

Different Filtering Logic

Claude applies stricter quality thresholds. That's why 4 companies fall into the 1-24% bucket—they either meet Claude's bar or they don't.

ChatGPT is more inclusive. If you exist somewhere in Bing's index, ChatGPT will cite you. But the position is unpredictable.

Different Position Certainty

ChatGPT's high position variance (std dev 19.5) suggests it's synthesizing from many competing sources with uncertainty about which is most relevant. Gemini's consistency (std dev 2.2) suggests it has strong opinions about source hierarchy—likely because it relies on Google Search rankings.


What This Means

You can't "optimize" your way to AI visibility with technical tricks. The 0.68 sentiment correlation tells you what actually matters: how people talk about you.

What to stop doing

  • Don't add schema markup expecting AI visibility gains. It helps Google, not LLMs.
  • Don't create llms.txt files. LLMs don't read them.
  • Don't chase GEO scores. The composite score has 0.18 correlation—basically noise.

What to start doing

  • Earn positive coverage. Get mentioned in publications, communities, and reviews. The 0.68 sentiment correlation is real.
  • Build Wikipedia presence. 47.9% of ChatGPT's top citations come from Wikipedia.
  • Expand platform presence. Companies on 4+ platforms are 2.8x more likely to appear in ChatGPT.
  • Keep content fresh. 65% of AI bot traffic goes to content from the past year.

Platform-specific strategies

ChatGPT: Broad web presence matters. Get mentioned in forums, review sites, aggregators—anything Bing indexes.

Claude: Authoritative positioning matters. Major publications, expert reviews, consistent positive sentiment. Claude's threshold is high but consistent.

Gemini: Google Search rankings translate directly. If you rank well on Google, Gemini will cite you.

The weak cross-platform correlations (0.17-0.39) mean you need different strategies for each.


The Top 3 Summary

MetricChatGPTClaudeGemini
Avg visibility69.7%67.3%71.3%
Position std dev19.56.52.2
Positive sentiment8512441
Companies at baseline91/9685/9674/96

Gemini is the most consistent and most generous with visibility. ChatGPT is the most unpredictable. Claude generates the most positive advocacy.


Try It

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Methodology

Sample: 96 YC-backed companies analyzed January 2026

Platforms:

  • ChatGPT (gpt-5-nano + Bing web search)
  • Claude (claude-haiku-4-5 + web search)
  • Gemini (gemini-2.5-flash + Google Search)

Queries: 16 prompts per company

  • 5 category prompts (brand-agnostic: "Best X tools in 2025")
  • 5 brand prompts (direct evaluation)
  • 5 competitive prompts (comparison queries)
  • 1 description prompt

Citation Rate: Percentage of category prompts where brand was mentioned (measures true AI visibility without brand bias)

Position: Average mention position across successful queries (1 = first mentioned)

Correlation: Pearson correlation coefficient between platform citation rates


Tags:Original ResearchGEOPlatform ComparisonData

Last updated: January 14, 2026

Marco Di Cesare

Marco Di Cesare

Founder, Loamly

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