What ChatGPT says about you isn't what Claude says. Here's how to test all four—and what to do with the results.
I made a discovery that changed how I approach AI visibility.
A client came to me confident about their AI presence. "We checked—ChatGPT mentions us when you ask about our category."
So I tested Claude. Nothing.
Perplexity? They mentioned a competitor.
Gemini? Outdated information from 2022.
That's when I realized: testing one AI system tells you almost nothing. The real picture only emerges when you test across all four major LLMs—and understand why they disagree.
This guide will show you exactly how to run a comprehensive multi-LLM test, interpret the results, and use the differences to improve your visibility across all AI systems.
Why Multi-LLM Testing Matters
Each major AI system has different:
Training data and cutoff dates
ChatGPT and Claude have different knowledge bases
Perplexity actively searches the web
Gemini integrates with Google's index differently
Citation preferences
Some prioritize academic/authoritative sources
Some favor recent content
Some weight structured data more heavily
Response patterns
ChatGPT tends toward comprehensive lists
Claude often provides nuanced analysis
Perplexity cites sources explicitly
Gemini integrates with Google's knowledge graph
What this means: Being visible to one AI system doesn't mean you're visible to others. And each system reaches different users with different intent.
A comprehensive AI visibility strategy requires visibility across the full LLM landscape.
The Four Systems You Need to Test
ChatGPT (OpenAI)
Why it matters: Largest user base, most mainstream adoption. If someone asks AI for a recommendation, they're probably asking ChatGPT.
Characteristics:
Tends toward balanced, comprehensive responses
Often provides lists of options
Training data has specific cutoff (varies by version)
Won't browse web unless specifically using browsing feature
Best for queries like:
"What are the best tools for X?"
"How do I approach Y?"
"Explain Z to me"
Claude (Anthropic)
Why it matters: Rapidly growing, especially popular with professionals and enterprises. Known for nuanced, thoughtful responses.
Characteristics:
Often provides more analytical responses
Strong on nuanced/complex topics
Different training data than ChatGPT
May cite different sources for same query
Best for queries like:
"Help me think through X"
"What should I consider when Y?"
"Compare approaches to Z"
Perplexity
Why it matters: Explicitly designed for search with citations. Shows you exactly where it's pulling information from.
Characteristics:
Actively searches the web (not just training data)
Explicitly cites sources with links
Results change as web content changes
Most "real-time" of the major systems
Best for queries like:
"What are the latest developments in X?"
"Who are the leading companies in Y?"
"What does research say about Z?"
Gemini (Google)
Why it matters: Integrated with Google's ecosystem. May influence future search behavior and has access to Google's knowledge graph.
Characteristics:
Integrates with Google's index
Access to knowledge graph entities
May reflect Google Search rankings differently than others
Continues to evolve with Google's AI strategy
Best for queries like:
"What is X?"
"Who provides Y services?"
"How does Z work?"
The Complete Testing Protocol
Here's the exact process I use for multi-LLM visibility testing. Set aside 45-60 minutes for a thorough test.
Phase 1: Preparation (10 minutes)
Step 1: Define your category terms
List 3-5 ways someone might describe what you do:
Primary category: "AI Engine Optimization consultants"
Secondary category: "AI search visibility experts"
Related category: "AEO agencies"
Problem-based: "help businesses appear in ChatGPT"
Outcome-based: "improve AI visibility"
Step 2: Identify your top 3-5 competitors
Who should appear when someone asks about your category? List them—you'll be tracking their visibility too.
Step 3: Set up a tracking document
Create a spreadsheet with columns:
Query
ChatGPT response
Claude response
Perplexity response
Gemini response
Your mention (Y/N/Partial)
Competitor mentions
Notes
Phase 2: Category Queries (15 minutes)
Test how AI systems describe your entire category.
Query Set 1: Category Discovery
Ask each of the four AI systems:
"What companies provide [your primary category]?"
"Who are the leading [your category] providers?"
"What should I look for in a [your category]?"
Document for each response:
Are you mentioned?
What position? (First, middle, end of list?)
Are competitors mentioned?
What attributes are highlighted?
How is the category defined?
Query Set 2: Problem-Based Discovery
"How can I [solve the problem you solve]?"
"My company is struggling with [problem you solve]. What should I do?"
"What's the best way to [achieve outcome you deliver]?"
Document for each:
Are solutions like yours recommended?
Are specific providers named?
What approach is suggested?
Phase 3: Direct Brand Queries (15 minutes)
Test what AI systems know about your company specifically.
Query Set 3: Brand Knowledge
Ask each system:
"What does [your company name] do?"
"What is [your company name] known for?"
"Tell me about [your company name]"
Document for each:
Does the AI know you exist?
Is the information accurate?
Is it current or outdated?
What sources seem to inform the response?
Query Set 4: Brand + Category
"How does [your company] compare to other [category] providers?"
"Is [your company] a good choice for [your service]?"
"What do people say about [your company]?"
Document for each:
Are you positioned accurately?
What differentiators are mentioned?
Any inaccuracies to correct?
Phase 4: Comparison Queries (15 minutes)
Test how you're positioned against competitors.
Query Set 5: Direct Comparisons
"[Your company] vs [Competitor]—which should I choose?"
"What's the difference between [your company] and [competitor]?"
"Comparing [your company] and [competitor] for [use case]"
Document for each:
Are you accurately represented?
What differentiators are highlighted?
Is the comparison fair?
What's missing?
Query Set 6: Best-For Scenarios
"Which [category] is best for [your ideal customer type]?"
"What [category] solution works best for [specific use case]?"
"I'm a [your target persona]—what [category] should I use?"
Document for each:
Are you mentioned for your ideal scenarios?
Are competitors mentioned for scenarios they don't serve well?
Opportunities to better position for specific use cases?
Interpreting Your Results
After completing the protocol, you'll have a matrix of responses. Here's how to analyze it:
Pattern 1: Consistent Visibility
You appear across all four systems for category queries.
This is the goal state. Your foundation is strong.
Next steps:
Optimize for positioning (first mention vs. last)
Improve how you're described (accuracy, differentiators)
Monitor for changes
Pattern 2: Partial Visibility
You appear in some systems but not others.
This is most common. It reveals specific gaps.
If you're missing from ChatGPT:
Check if your content is structured for extraction
Verify you're not blocking OpenAI crawlers
Your content may lack citable statements ChatGPT prefers
If you're missing from Claude:
Claude may weight different authority signals
Check your expertise/credential signals
Review content depth and nuance
If you're missing from Perplexity:
Your web content isn't ranking/visible
Check technical SEO fundamentals
Verify content is being indexed
If you're missing from Gemini:
Google Knowledge Graph may not recognize your entity
Check your Google Business presence
Verify schema markup
Pattern 3: Invisible
You don't appear in any system for category queries.
This requires foundational work.
Immediate priorities:
Verify AI crawlers can access your content
Add explicit definitions of what you do
Build citable, factual content
Strengthen authority signals
Pattern 4: Misrepresented
You appear, but information is wrong or outdated.
This is actually good news—it means you're visible enough to be mentioned.
Correction strategy:
Identify the incorrect information
Find where AI might have gotten it (old pages, third-party sites)
Update your own content with correct, prominent information
Wait for systems to re-train or re-crawl
For Perplexity, correct information may update faster
Pattern 5: Competitor-Dominated
Competitors appear consistently; you don't.
This reveals specific competitive disadvantages.
Analysis questions:
What content do competitors have that you don't?
How is their content structured differently?
What authority signals do they have?
What categories are they owning?
The Difference Analysis
Here's the most valuable part of multi-LLM testing: understanding WHY systems respond differently.
When ChatGPT Includes You But Claude Doesn't:
Possible causes:
Different training data emphasis
ChatGPT may have crawled pages Claude missed
Claude may have different authority thresholds
Content structure may match ChatGPT's extraction patterns better
Action: Look at what content ChatGPT seems to cite. Ensure similar depth/structure across your site so Claude picks it up too.
When Perplexity Includes You But Others Don't:
Possible causes:
Your recent content is strong (Perplexity crawls live web)
Other systems have outdated training data
Your SEO is working but content isn't in training data
Action: This is actually positive—maintain your web presence. Older systems will catch up as they retrain. Focus on building more linkable, authoritative content.
When Gemini Includes You But Others Don't:
Possible causes:
Google Knowledge Graph recognizes your entity
Your Google Business presence is strong
You may have schema markup others don't parse
Action: Double down on structured data. Ensure your entity definition is clear. Other systems may begin to recognize these signals too.
When Others Include You But Perplexity Doesn't:
Possible causes:
Your live web content may have issues
Recent competitors may have overtaken you
Technical SEO problems
Action: Perplexity reflects current web state. Fix any technical issues and ensure your best content is prominent and crawlable.
Building Your Multi-LLM Action Plan
Based on your results, prioritize actions:
Priority 1: Technical Access (Invisible on all)
If no AI system mentions you:
Week 1:
Check robots.txt for AI crawler blocks
Verify JavaScript rendering isn't hiding content
Ensure basic SEO allows crawling/indexing
Week 2:
Add explicit entity definition to homepage
Create structured content with citable facts
Publish "What is [your service]?" content
Priority 2: Content Gaps (Partial visibility)
If you appear on some systems but not others:
Week 1:
Analyze what content successful mentions cite
Identify structural patterns in that content
Map gaps between visible and invisible content
Week 2-3:
Restructure key pages with citable elements
Add definition blocks, statistics, comparisons
Strengthen authority signals (bios, credentials, evidence)
Priority 3: Positioning (Visible but poorly positioned)
If you appear but not prominently or accurately:
Week 1:
Document exactly how you're described
Identify inaccuracies or missed differentiators
Find source of incorrect information
Week 2-3:
Update content to emphasize correct positioning
Add comparison content showing differentiators
Create content addressing specific use cases
Priority 4: Competitive Response (Competitors dominating)
If competitors appear and you don't:
Week 1:
Analyze competitor content structure
Identify their citation-worthy elements
Map their authority signals
Week 2-4:
Create content that matches or exceeds their depth
Build authority signals in your space
Target specific queries they're winning
Ongoing Monitoring Protocol
Multi-LLM testing isn't a one-time activity. Set up regular monitoring:
Weekly (5 minutes):
Test one category query across all four systems
Note any changes in your visibility
Track new competitor mentions
Monthly (30 minutes):
Run full protocol for priority queries
Document trends over time
Identify new queries to target
Quarterly (60 minutes):
Complete comprehensive test
Analyze changes since last quarter
Adjust strategy based on patterns
The Multi-LLM Advantage
Most businesses either:
Don't test AI visibility at all
Test only ChatGPT and assume it represents everything
You now have a systematic approach to understanding your visibility across the entire AI landscape.
This matters because:
Different users prefer different AI systems
Different AI systems reach users with different intent
Visibility gaps on one system are often fixable
Cross-system consistency signals strong fundamentals
The businesses winning AI search are the ones treating it as a multi-platform challenge—just like they once treated multi-channel marketing.
What I've Learned from Hundreds of Tests
After running this protocol on hundreds of B2B websites, patterns emerge:
Average first-test results:
12% appear on all four systems
34% appear on two or three
31% appear on only one
23% appear on none
Most common gaps:
Perplexity visibility without ChatGPT/Claude (good SEO, weak content structure)
ChatGPT visibility without Claude (content structure works for one but not both)
Complete invisibility (fundamental technical or content issues)
Fastest improvements come from:
Fixing technical blocking issues (immediate impact on Perplexity)
Adding structured definitions (improves all systems)
Building citable statistics (improves citation likelihood everywhere)
The AI search landscape is fragmented. Users are spread across systems. Your visibility strategy needs to account for all of them.
Start with the test. Understand your baseline. Then systematically close the gaps.
Want a professional multi-LLM assessment? Our AI Visibility Audit tests your presence across all four systems with 47 standardized queries, analyzes the differences, and delivers a prioritized roadmap for cross-platform visibility.
Elizabeta Kuzevska is the Co — Founder of Revenue Experts AI, building AI Revenue Intelligence Systems powered by 100+ specialized agents. Her methodology integrates multi-agent architectures with human expertise to transform how B2B companies generate revenue. See the courses and try some agents
Connect on x: @ekuzevska
Connect on LinkedIn: https://www.linkedin.com/in/elizabeta-kuzevska-digital-marketing-ai-engineering/