Original research · 2026-07 edition

AI SEO Statistics: Restaurant (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

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The question bank

The questions we tested — sampled from real buyer journeys in restaurant.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

I'm looking for a quiet restaurant for an anniversary dinner where we can actually hear each other talk without loud music.
Is it more cost-effective to host a 20-person engagement party at home with catering or just book a private room at a local bistro?
What are the red flags I should look for in online reviews when trying to find a high-quality seafood place?
How much should I realistically budget per person for a 5-course tasting menu at a Michelin-rated spot including wine pairings?
What's the difference in service and atmosphere between a traditional steakhouse and a modern gastropub for a business meeting?
Are there any restaurants open past 11 PM on a weeknight in the downtown area that aren't just fast food or bars?
What should I ask a restaurant manager to ensure they can safely handle a severe nut allergy for a large group booking?
I need a last-minute table for six tonight for a birthday dinner; which apps or methods are best for finding an immediate opening?
Show all 15 questions
How can I tell if a restaurant is truly kid-friendly or if they just tolerate children but don't have the right amenities?
Does it usually cost extra to bring my own wine to a high-end restaurant, and what is the typical corkage fee?
I'm looking for a restaurant with a private room that has a projector or TV for a small corporate presentation.
Is paying for a 'chef's table' experience worth the premium price compared to just ordering off the standard menu?
What are the best ways to get a reservation at a popular 'no-reservation' spot without waiting in line for two hours?
Should I choose a rooftop lounge or a waterfront patio for a first date if I want a romantic but relaxed vibe?
What are some signs that a farm-to-table restaurant is actually sourcing locally versus just using it as a marketing term?

Model by model

16-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about restaurant buyers.

Behavior rates across 15 restaurant buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional20%13%0%80%
Suggests DIY first27%27%27%87%
Names specific providers7%20%53%47%
Gives price or cost info13%20%13%93%
Tells to check reviews27%20%27%80%
Tells to verify credentials13%7%0%80%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity27%33%33%60%
Gives selection criteria53%53%53%80%
Warns about red flags13%20%20%80%
Asks a clarifying question67%53%7%27%
Recommends multiple quotes0%0%0%100%

By model

How each assistant handled Restaurant questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same restaurant questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 20% (ChatGPT) down to 0% (Gemini), a 20-point gap on an identical question set.

Across the 15 restaurant answers it produced, ChatGPT recommended hiring a professional in 20% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 13.3%, averaging 423 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 restaurant answers it produced, Claude recommended hiring a professional in 13.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 20% of answers (about 1.1 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 273 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 restaurant answers it produced, Gemini recommended hiring a professional in 0% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 53.3% of answers (about 1.6 distinct providers per answer) and included price or cost information 13.3% of the time. Gemini asked a clarifying question before answering in 6.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 0%, averaging 231 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a restaurant buyer to a professional (20%) and Gemini the least (0%). ChatGPT produced the longest answers, at 423 words on average. Specific providers were named most often by Gemini (53.3%) — even there, roughly one answer in 2 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 15.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a restaurant buyer happens to ask matters most:

  • Asks a clarifying question: from 6.7% (Gemini) to 66.7% (ChatGPT) — a 60-point spread.
  • Names a specific provider: from 6.7% (ChatGPT) to 53.3% (Gemini) — a 47-point spread.
  • Recommends hiring a professional: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.
  • Tells the buyer to verify credentials: from 0% (Gemini) to 13.3% (ChatGPT) — a 13-point spread.
  • Gives price or cost information: from 13.3% (ChatGPT) to 20% (Claude) — a 7-point spread.

The widest single gap — asks a clarifying question, 60 points — means a restaurant buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the restaurant market.

Where they agree

The points of near-consensus in Restaurant.

On other behaviors the three models move almost in lockstep — the points of near-consensus for restaurant, where all three landed within a few points of each other:

  • Suggests a DIY approach first: 26.7% across all three models.
  • Mentions case studies or portfolio: 0% across all three models.
  • Gives selection criteria: 53.3% across all three models.
  • Recommends multiple quotes: 0% across all three models.

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (26.7%).

Every behavior, measured

All twelve coded behaviors for Restaurant, averaged across the three models.

The behaviors AI models reproduce most often for restaurant are gives selection criteria (53.3% on average), asks a clarifying question (42.2%) and mentions local proximity (31.1%); the rarest are recommends multiple quotes (0%), mentions case studies or portfolio (0%) and tells the buyer to verify credentials (6.7%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Gives selection criteria: 53.3% on average (ChatGPT 53.3%, Claude 53.3%, Gemini 53.3%).
  • Asks a clarifying question: 42.2% on average (ChatGPT 66.7%, Claude 53.3%, Gemini 6.7%) — a 60-point spread.
  • Mentions local proximity: 31.1% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 33.3%) — a 7-point spread.
  • Suggests a DIY approach first: 26.7% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 26.7%).
  • Names a specific provider: 26.7% on average (ChatGPT 6.7%, Claude 20%, Gemini 53.3%) — a 47-point spread.
  • Tells the buyer to check reviews: 24.5% on average (ChatGPT 26.7%, Claude 20%, Gemini 26.7%) — a 7-point spread.
  • Warns about red flags or scams: 17.8% on average (ChatGPT 13.3%, Claude 20%, Gemini 20%) — a 7-point spread.
  • Gives price or cost information: 15.5% on average (ChatGPT 13.3%, Claude 20%, Gemini 13.3%) — a 7-point spread.
  • Recommends hiring a professional: 11.1% on average (ChatGPT 20%, Claude 13.3%, Gemini 0%) — a 20-point spread.
  • Tells the buyer to verify credentials: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
  • Recommends multiple quotes: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the restaurant buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the restaurant buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 24.5% of answers on average. Verifying credentials or certifications appeared in 6.7%. Warning about red flags or scams appeared in 17.8%.

On structuring the decision, a selection-criteria checklist showed up in 53.3% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for restaurant is "recommends multiple quotes" at 0% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name Restaurant providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 restaurant answers, a specific provider was named in 26.7% of responses on average — roughly 1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for restaurant: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Restaurant questions cover.

The 15 questions behind every percentage on this page were drawn from real restaurant (hospitality; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact restaurant question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.

How to read this

A note on the numbers.

A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific restaurant question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →