How AI Recommends Hotels:2026 Index
Comprehensive analysis of 245,046 unique sources cited by ChatGPT, Gemini, Perplexity, and Grok. Based on 19,579 AI runs across 2,500 unique prompts covering 25 cities, 8 personas, and 9 hotel types.
Executive Summary
AI learns from aggregators but sends traffic direct
75-91% of hotel links from AI go directly to hotel websites, not OTAs.
Yet AI heavily consults OTAs as sources—all models scan an OTA/Meta more than 50% of the time. Booking is top choice in ChatGPT/Gemini, TripAdvisor appears in 95-100% of Grok & Perplexity responses, Expedia in 37-96% depending on model.
User-generated content shapes AI recommendations. Each model has its preferred UGC: Gemini favors YouTube (14%), GPT 5.1 relies on Reddit (15%), and Grok heavily indexes Facebook groups and Reddit. These platforms shape which hotels AI recommends—upending traditional SEO strategies.
Where Does AI Send Travelers?
Analysis of 31,138 unique hotels recommended by ChatGPT, Gemini, and Perplexity across ~10K prompts (averaging 3+ hotels per response). Do AI models link to hotel websites directly, or route travelers through OTAs?
Direct vs OTA by AI Model
% of hotel links by destination type (2,500 prompts per model)
| Model | Direct | OTA | TripAdvisor |
|---|---|---|---|
| GPT 5.1 | 87.9% | 12.1% | — |
| GPT 5.2 | 91.1% | 8.9% | 0.1% |
| Perplexity Sonar | 74.7% | 17.3% | 8% |
| Gemini 2.5 Flash | 89.4% | 10.6% | — |
All models favor direct hotel links (75-91%), but Perplexity Sonar is the most OTA-friendly at 25.3% (+ 8% TripAdvisor links). GPT 5.2 sends the most traffic directly to hotels (91.1%). Guess who has a partnership with TripAdvisor? Perplexity!
Chains vs Independent Hotels
Within direct links, how do AI models split between major chains and independent properties?
Chain vs Independent by AI Model
Chain = Marriott, Hilton, IHG, Hyatt, Accor, etc.
| Model | Chain | Independent | OTA/Meta |
|---|---|---|---|
| Gemini 2.5 Flash | 40.7% | 48.7% | 10.6% |
| GPT 5.2 | 37.2% | 53.9% | 8.9% |
| GPT 5.1 | 34.9% | 53% | 12.1% |
| Perplexity Sonar | 32.9% | 41.8% | 25.3% |
Gemini favors chains the most (40.7%), while Perplexity Sonar is most OTA-heavy (25.3%). GPT models split roughly 35-37% chain, 53% independent, 9-12% OTA.
By Star Rating
Chain vs Independent by Star Rating
Higher star ratings = more chain hotel links, less OTA
| Rating | Chain | Independent | OTA |
|---|---|---|---|
| 5 Stars | 55.8% | 42.2% | 2% |
| 4 Stars | 28.9% | 60.1% | 11% |
| 3 Stars | 18.7% | 52.5% | 28.8% |
5-star hotels: 56% chain, only 2% OTA. 3-star hotels: 29% OTA, only 19% chain. Higher star ratings correlate with more direct links to brand websites.
By Traveler Persona
Chain vs Independent by Traveler Persona
Business travelers get more chain links, leisure travelers see more OTAs
| Persona | Chain | Independent | OTA |
|---|---|---|---|
| Group Business | 45.1% | 44.5% | 10.4% |
| Solo Business | 38.5% | 45.7% | 15.8% |
| Elderly travellers | 33.2% | 52.3% | 14.5% |
| Luxury travelers | 32.3% | 58.4% | 9.3% |
| Families | 29.6% | 51.9% | 18.6% |
| Couples | 26.1% | 61.3% | 12.6% |
Business travelers get 45% chain links vs 23% for solo leisure travelers. Solo leisure sees the highest OTA rate (21%), likely due to price comparison needs.
By City
Chain vs Independent by City (All 25 Cities)
North American cities favor chains; European & Japanese cities favor independents
| City | Chain | Independent | OTA |
|---|---|---|---|
| Toronto | 47% | 45.1% | 7.9% |
| Dubai | 45.5% | 37.4% | 17.1% |
| New York | 45.3% | 49% | 5.6% |
| Los Angeles | 42.8% | 51.2% | 6% |
| Miami | 41.5% | 49% | 9.5% |
| Cairo | 39.6% | 18% | 42.4% |
Tokyo (75%), London (69%), Paris (69%) have the highest independent hotel rates. Shanghai (44%) and Cairo (42%) have the highest OTA rates—Trip.com dominates Shanghai (where the company was founded), while Booking.com leads in Cairo (strong MENA supply + large longtail of smaller hotels).
Top Hotel Chains (by AI recommendations)
Chains are aggregated with their sub-brands (e.g., Marriott includes Ritz-Carlton, Westin, Sheraton, St. Regis; Accor includes Sofitel, Fairmont, Raffles, Novotel, etc.).
Share of chain hotel links recommended by AI
| Rank | Chain | Links | Market Share |
|---|---|---|---|
| 1 | Marriott Group | 3,571 | 22.3% |
| 2 | Accor | 3,365 | 21% |
| 3 | IHG | 1,806 | 11.3% |
| 4 | Hilton Group | 1,504 | 9.4% |
| 5 | Hyatt | 1,008 | 6.3% |
| 6 | Four Seasons | 626 | 3.9% |
URLs in the AI's answer pointing to hotels. When AI recommends "Hotel X" and includes a clickable link, that's a link.
Websites the AI scanned to form its answer. These are the references AI consulted—not necessarily shown to users, but crucial for what AI "knows."
Which Sources Do AI Assistants Trust?
Each AI model has its own hierarchy of trusted sources. Understanding these preferences is crucial for optimizing your hotel's visibility across different platforms.
How Deep Do AI Models Search?
Average unique URLs and domains scanned per query. More sources = more opportunities for your hotel to appear.
Unique sources scanned per AI response
| Model | Avg URLs/Run | Avg Domains/Run | Total Runs |
|---|---|---|---|
| Grok (combined) | 58.5 | 12.4 | 9,719 |
| GPT 5.2 | 27.34 | 16.22 | 2,495 |
| GPT 5.1 | 11.8 | 7.79 | 2,481 |
| Gemini 2.5 Flash | 11.21 | 8.78 | 2,389 |
| Perplexity Sonar | 8.19 | 7.99 | 2,495 |
GPT 5.2 doubled its web search depth compared to 5.1 (27 vs 12 sources/run, 16 vs 8 domains). For hotels, this means consistency across sources matters more than ever—if you're only mentioned on 2-3 sites, you'll lose to competitors appearing on 10+. Perplexity's low count (8.19) reflects its default API limit of 10 searches.
Top 10 Sources by Model
% of runs where each domain was cited at least once. Each model has distinct source preferences.
Grok
Gemini 2.5 Flash
GPT 5.1
GPT 5.2
Perplexity Sonar
% of runs where each domain was cited at least once
| Domain | Grok | Perplexity | GPT 5.2 | GPT 5.1 | Gemini |
|---|---|---|---|---|---|
| tripadvisor.com | 99.9% | 95.5% | 20.5% | 9.6% | — |
| expedia.com | 96.4% | 68.6% | 28.9% | 38.4% | 37.3% |
| hotels.com | 81.7% | 36.8% | 31.9% | 8.6% | 30.6% |
| booking.com | 76.4% | 33.3% | 53.9% | 23.8% | 63.0% |
| wikipedia.org | 5.1% | — | 30.0% | 75.1% | 1.0% |
| facebook.com | 63.5% | — | — | — | — |
ChatGPT's Wikipedia Dependency
GPT 5.1 cited Wikipedia in 75% of responses—far more than any other model. GPT 5.2 dropped to 30%, but still relies on encyclopedia-style knowledge. Your Wikipedia presence directly impacts ChatGPT visibility.
GPT 5.1 → 5.2 shift: Wikipedia dropped from 75% to 30%, while hotel brand sites (Marriott, Hilton, Four Seasons) jumped 2-3×. GPT 5.2 now cites direct hotel sources more than any other model except Perplexity.
Key Changes: GPT 5.1 → 5.2
GPT 5.2 doubled its search depth and broadened its source base. Less reliance on Wikipedia (75% → 30%) and Reddit (14.6% → 2.3%).
Your entire digital footprint matters now. It's a strategy, not small tactics—AI scans broader, so be present everywhere that counts.
Which OTAs Do AI Models Cite?
OTA citation rates vary dramatically by model. Grok cites OTAs in nearly every response, while GPT models are more selective. This has major implications for distribution strategy.
OTA Citations by AI Model
% of runs where each OTA was cited. Grok cites OTAs far more than other models.
% of runs where each OTA was cited at least once
| OTA | Grok | Perplexity | GPT 5.2 | GPT 5.1 | Gemini |
|---|---|---|---|---|---|
| TripAdvisor | 99.9% | 95.5% | 20.5% | 9.6% | — |
| Expedia | 96.4% | 68.6% | 28.9% | 38.4% | 37.3% |
| Hotels.com | 81.7% | 36.8% | 31.9% | 8.6% | 30.6% |
| Booking.com | 76.4% | 33.3% | 53.9% | 23.8% | 63% |
| Travelocity | 56.9% | 13% | 14.9% | 7% | 8.8% |
| Agoda | 46.3% | 7% | — | — | 33.5% |
Expedia Group dominance: Expedia + Hotels.com + Travelocity combined reach 99%+ in Grok. TripAdvisor is #1 for Grok (99.9%) and Perplexity (95.5%), but barely appears in GPT models. Booking.com leads in Gemini (63%) and GPT 5.2 (54%).
User-Generated Content: Grok vs Everyone Else
Social platforms and community forums influence AI hotel recommendations — but Grok is in a league of its own, citing UGC in nearly every response while other models barely touch it. Gemini loves YouTube (hello Google) and ChatGPT loves Reddit, but less with the upgrade to 5.2. Guess who is/was one of the biggest shareholders of Reddit pre-IPO? Sam Altman, CEO of OpenAI (!)
Grok: The UGC King
Grok cites user-generated content in nearly every response. Facebook groups and Reddit dominate.
Other Models: Different Patterns
GPT 5.1 leans on Reddit (14.6%), Gemini prefers YouTube (13.6%). Note the different scale.
Top 10 Subreddits (Grok)
| # | Subreddit | Citations |
|---|---|---|
| 1 | r/chubbytravel | 1,299 |
| 2 | r/FATTravel | 726 |
| 3 | r/JapanTravelTips | 641 |
| 4 | r/askTO | 452 |
| 5 | r/melbourne | 438 |
| 6 | r/travel | 357 |
Top Facebook Groups (Grok)
| # | Group | Citations |
|---|---|---|
| 1 | Barcelona Travel Tips | 676 |
| 2 | Marriott Bonvoy Elites | 590 |
| 3 | Mexico City Travel Tips | 533 |
| 4 | Amsterdam Travel Tips | 500 |
| 5 | Travel Thailand Group | 460 |
FIRE communities are shaping AI: Wealth-focused subreddits r/chubbytravel (1,299 citations) and r/FATTravel (726) dominate Grok's sources. These "Fat FIRE" travel communities discussing high-budget trips are teaching AI what luxury hotels to recommend. Overall, Reddit discussions are a strong place for hotels to infuse their brand... even more for upscale ones!
UGC Sources Are Shifting Fast
AI's reliance on user-generated content is volatile. Reddit's influence dropped significantly between GPT 5.1 and 5.2 (14.6% → 2.3%). Facebook groups remain Grok-exclusive for now—but that could change. Most notably: ChatGPT is starting to cite YouTube based on our most recent monitoring outside this report.
GPT-5.2: YouTube Citations Rising
Weekly data from our UI monitoring (200 prompts/day). ChatGPT started citing YouTube in late December.
Data from our ongoing UI monitoring (outside this report's scope). This shows YouTube citations are emerging as a new signal for ChatGPT.
Which Hotel Chains Get Cited?
When AI recommends chain hotels, which brands capture the most share? Analysis of 23K+ chain citations reveals Marriott's dominance—but with surprising variance across models.
Chain Market Share by AI Model
% of chain citations going to each brand. Marriott leads across all models, but GPT 5.1 is particularly biased (39%).
Market share % of chain citations by model
| Brand | Grok | Gemini | GPT 5.1 | GPT 5.2 | Perplexity |
|---|---|---|---|---|---|
| Marriott | 26.2% | 21% | 39% | 22.4% | 34.1% |
| Accor | 22% | 19% | 19% | 14.5% | 12.2% |
| Four Seasons | 11.9% | 15.1% | 17.4% | 11.3% | 20.9% |
| IHG | 9.4% | 5% | 6.6% | 12.2% | 8% |
| Hilton | 6% | 11.5% | 4.9% | 11.7% | 3.2% |
| Hyatt | 7% | 13.8% | 2.2% | 10.4% | 0.1% |
Marriott dominates, but models diverge. Perplexity loves luxury (Four Seasons 21%, Ritz-Carlton 18%) but almost ignores Hyatt (0.1%). Hilton underperforms everywhere (3-12%).
How We Collected This Data
Data Collection
- 2,500 unique prompts across 25 major travel destinations
- 8 traveler personas: Solo Business, Families, Couples, Elderly, Luxury, Leisure Groups, Business Groups, Solo Leisure
- 9 hotel types: Luxury, Resort, Business, Spa, Boutique, Aparthotel, Budget, Eco-lodge, Hostel
- Three star tiers (when applicable): 3, 4, 5 stars
AI Models Tested (6)
- Grok: 9,719 runs / 2,500 prompts (target 4× each, hence 10k)
- GPT 5.1: 2,481 runs / 2,500 prompts
- GPT 5.2: 2,495 runs / 2,500 prompts
- Perplexity Sonar: 2,495 runs / 2,500 prompts (API defaults search depth to 10)
- Gemini Flash 2.5: 2,389 runs / 2,500 prompts
UI vs API Collection
Data was collected through different interfaces depending on model availability:
- UIGrok — Web interface (X.com)
- APIChatGPT — API (to compare GPT 5.1 vs 5.2)
- APIGemini — API (Gemini 3 wasn't available yet)
- APIPerplexity — Sonar API
Coming soon: A comparative study on UI vs API differences in AI recommendations.
Cities Covered (25)
Paris, London, Tokyo, New York, Barcelona, Rome, Dubai, Singapore, Sydney, Melbourne, Hong Kong, Bangkok, Istanbul, Athens, Amsterdam, Berlin, Miami, Los Angeles, Mexico City, Buenos Aires, Rio de Janeiro, Cairo, Cape Town, Toronto, Shanghai
Example Prompts
"I'm planning a luxury weekend in Barcelona for a honeymoon and want a hotel with stunning suites, private spa access, and romantic dining options within walking distance of the Gothic Quarter—can you recommend the best value-for-splurge options that maximize intimate experiences and memorable aesthetics?"
"What are the best budget hostels in Berlin that offer excellent location, clean private rooms, and easy access to major landmarks for a solo adventure where I can meet other travelers?"
Data Summary
- 2,500 unique prompts
- 19,579 total runs
- 245,046 unique source URLs cited across all models
- 31,138 unique hotels mentioned
- Data collected: December 2025 – January 2026
Data Processing
- Source deduplication: URLs normalized, domains extracted (e.g., tripadvisor.co.uk → tripadvisor.com)
- Run-level uniqueness: Each source counted once per run, regardless of how many times it appears
- Hotel links: Deduplicated by (prompt_id, hotel_name, domain) to avoid double-counting
Data Access
We believe in open research. Contact us for access to methodology details or to discuss partnership opportunities.
Key Takeaways for Hotels
This data isn't just interesting—it's actionable. Here's what hoteliers should do with these insights.
Measure AI Influence
Track pre/post booking: "Did guests use AI to find you?" Survey new guests about their discovery journey. AI is becoming a major channel—measure it like you measure OTAs.
Analyze Your AI Traffic
Check your server logs for AI referrers (ChatGPT, Perplexity, etc.). Understand which AI models are already sending you traffic and where the gaps are.
Position Strategically
Decide where you want to appear and start measuring. AIs are smart enough to understand positioning— be intentional about your niche and target personas.
Consistency Across Reviews
AI models scan multiple sources. Ensure your messaging, amenities, and positioning are consistent across TripAdvisor, Google, Booking.com, and your own site.
Structured Data Matters
Clear entity markup and structured data help AI understand who you are. Schema.org, Wikipedia, and knowledge graphs feed directly into AI training.
Rethink Your Claims
AI models are smart enough to catch bullshit on you :). If you claim "boutique luxury" but reviews say "decent business hotel," AI will reflect reality, not marketing.
See How Your Hotel Appears in AI Search
Get a personalized AI visibility report for your property. Understand where you stand and how to improve your presence in ChatGPT, Gemini, Perplexity, and Grok.