Original research · 2026-07 edition

AI SEO Statistics: Food and Beverage (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

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The question bank

The questions we tested — sampled from real buyer journeys in food and beverage.

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

How do I find a reliable caterer for a 50-person corporate lunch on a tight budget?
What are the pros and cons of hiring a private chef versus booking a room at a restaurant for a 10-person dinner?
Is it cheaper to buy my own alcohol and just hire a professional bartender for a wedding?
What specific certifications should I look for when hiring a food truck for a neighborhood block party?
How much should I expect to pay per head for a high-end plated three-course dinner service?
What questions should I ask a catering company to ensure they can handle severe peanut and shellfish allergies?
I'm hosting a gala for 200 people; what is the difference in staffing needs between a buffet and a sit-down meal?
What are the red flags to watch out for when reviewing a contract from a hospitality group?
Show all 40 questions
How do I know if a caterer is actually using fresh, local ingredients or just saying they do for marketing?
When is the best time to book a food and beverage service for a holiday party to ensure I get my first choice?
Can I hire just a waitstaff team if I'm planning to cook the food myself for a large family reunion?
What's the typical gratuity or service charge for a full-service catering team in a major city?
How much space and what kind of electrical hookups does a professional mobile coffee bar require?
Should I hire a dedicated mixologist for a cocktail party or is a standard bartender enough?
What are the hidden costs like corkage fees or cake cutting fees that I should look for in a venue's food package?
How do I vet a catering company's reputation if they don't have many online reviews yet?
Is it possible to find a last-minute caterer for a funeral reception with only 48 hours' notice?
What is the standard cancellation policy for a large-scale hospitality contract?
What's the difference between a drop-off catering service and a full-service event company?
How do I compare quotes from three different caterers when their menu styles are completely different?
Do I need to provide a kitchen for a private chef, or do they usually bring their own portable equipment?
What are the most common mistakes people make when estimating alcohol quantities for an open bar?
How can I tell if a hospitality provider is properly insured for liquor liability?
What are some creative late-night snack options for a wedding that won't break the bank?
Is a tasting usually included in the initial quote, or do I have to pay extra to try the food before booking?
How do I find a caterer that specializes in authentic regional cuisines like Oaxacan or Szechuan for a themed event?
What are the signs that a catering company is overbooked and might provide poor service on my event date?
How many servers do I actually need for a 100-person cocktail party with passed hors d'oeuvres?
Can a food truck handle a fast-paced environment for a corporate lunch with 300 employees?
What should be included in a 'day-of' timeline provided by my food and beverage manager?
Are there specific catering companies that specialize in eco-friendly or zero-waste event service?
What is the average markup on wine when purchasing through a catering service versus a retail store?
How do I handle a situation where the caterer runs out of food before all guests have eaten?
What are the benefits of hiring a boutique catering firm over a large national hospitality chain?
How do I negotiate a better price for a mid-week event compared to a Saturday night booking?
What kind of setup time does a professional bartending service usually need before the first guest arrives?
Is it worth hiring a sommelier for a private wine-pairing dinner at home?
How do I ensure the food stays at the correct temperature if my venue doesn't have an on-site kitchen?
What's the best way to handle tipping for a team of 10+ catering staff members?
What are the current trends in interactive food stations that guests actually enjoy?

Model by model

20-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 food and beverage buyers.

Behavior rates across 40 food and beverage buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional58%35%28%60%
Suggests DIY first15%20%13%78%
Names specific providers5%8%18%85%
Gives price or cost info30%28%30%70%
Tells to check reviews15%8%3%80%
Tells to verify credentials28%13%8%68%
Mentions case studies / portfolio8%5%3%93%
Mentions local proximity28%20%18%65%
Gives selection criteria40%48%35%50%
Warns about red flags10%15%10%85%
Asks a clarifying question55%70%0%18%
Recommends multiple quotes5%5%0%93%

By model

How each assistant handled Food and Beverage questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same food and beverage questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 57.5% (ChatGPT) down to 27.5% (Gemini), a 30-point gap on an identical question set.

Across the 40 food and beverage answers it produced, ChatGPT recommended hiring a professional in 57.5% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 5% of answers (about 0.2 distinct providers per answer) and included price or cost information 30% of the time. ChatGPT asked a clarifying question before answering in 55% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 27.5%, averaging 580 words per answer. On the remaining cues it told the buyer to check reviews in 15%, pointed to case studies or a portfolio in 7.5%, and framed the choice around local proximity in 27.5%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 food and beverage answers it produced, Claude recommended hiring a professional in 35% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 7.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 27.5% of the time. Claude asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 12.5%, averaging 293 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 47.5% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 food and beverage answers it produced, Gemini recommended hiring a professional in 27.5% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 17.5% of answers (about 0.8 distinct providers per answer) and included price or cost information 30% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 7.5%, averaging 244 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 17.5%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a food and beverage buyer to a professional (57.5%) and Gemini the least (27.5%). ChatGPT produced the longest answers, at 580 words on average. Specific providers were named most often by Gemini (17.5%) — even there, roughly one answer in 6 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 19.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a food and beverage buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 70% (Claude) — a 70-point spread.
  • Recommends hiring a professional: from 27.5% (Gemini) to 57.5% (ChatGPT) — a 30-point spread.
  • Tells the buyer to verify credentials: from 7.5% (Gemini) to 27.5% (ChatGPT) — a 20-point spread.
  • Names a specific provider: from 5% (ChatGPT) to 17.5% (Gemini) — a 13-point spread.
  • Tells the buyer to check reviews: from 2.5% (Gemini) to 15% (ChatGPT) — a 13-point spread.

The widest single gap — asks a clarifying question, 70 points — means a food and beverage 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 food and beverage market.

Where they agree

The points of near-consensus in Food and Beverage.

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

  • Gives price or cost information: 27.5%–30% across all three (a 3-point spread).
  • Mentions case studies or portfolio: 2.5%–7.5% across all three (a 5-point spread).
  • Warns about red flags or scams: 10%–15% across all three (a 5-point spread).
  • Recommends multiple quotes: 0%–5% across all three (a 5-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 92.5% of questions) and least consistently on "asks a clarifying question" (17.5%).

Every behavior, measured

All twelve coded behaviors for Food and Beverage, averaged across the three models.

The behaviors AI models reproduce most often for food and beverage are asks a clarifying question (41.7% on average), gives selection criteria (40.8%) and recommends hiring a professional (40%); the rarest are recommends multiple quotes (3.3%), mentions case studies or portfolio (5%) and tells the buyer to check reviews (8.3%). Each figure below is the share of a model's 40 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Asks a clarifying question: 41.7% on average (ChatGPT 55%, Claude 70%, Gemini 0%) — a 70-point spread.
  • Gives selection criteria: 40.8% on average (ChatGPT 40%, Claude 47.5%, Gemini 35%) — a 13-point spread.
  • Recommends hiring a professional: 40% on average (ChatGPT 57.5%, Claude 35%, Gemini 27.5%) — a 30-point spread.
  • Gives price or cost information: 29.2% on average (ChatGPT 30%, Claude 27.5%, Gemini 30%) — a 3-point spread.
  • Mentions local proximity: 21.7% on average (ChatGPT 27.5%, Claude 20%, Gemini 17.5%) — a 10-point spread.
  • Suggests a DIY approach first: 15.8% on average (ChatGPT 15%, Claude 20%, Gemini 12.5%) — a 8-point spread.
  • Tells the buyer to verify credentials: 15.8% on average (ChatGPT 27.5%, Claude 12.5%, Gemini 7.5%) — a 20-point spread.
  • Warns about red flags or scams: 11.7% on average (ChatGPT 10%, Claude 15%, Gemini 10%) — a 5-point spread.
  • Names a specific provider: 10% on average (ChatGPT 5%, Claude 7.5%, Gemini 17.5%) — a 13-point spread.
  • Tells the buyer to check reviews: 8.3% on average (ChatGPT 15%, Claude 7.5%, Gemini 2.5%) — a 13-point spread.
  • Mentions case studies or portfolio: 5% on average (ChatGPT 7.5%, Claude 5%, Gemini 2.5%) — a 5-point spread.
  • Recommends multiple quotes: 3.3% on average (ChatGPT 5%, Claude 5%, Gemini 0%) — a 5-point spread.

Trust signals

How well the models protect the food and beverage buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the food and beverage buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.3% of answers on average. Verifying credentials or certifications appeared in 15.8%. Warning about red flags or scams appeared in 11.7%.

On structuring the decision, a selection-criteria checklist showed up in 40.8% of answers on average and a recommendation to gather multiple quotes in 3.3%. The single least-reproduced protective signal for food and beverage is "recommends multiple quotes" at 3.3% 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 Food and Beverage providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 food and beverage answers, a specific provider was named in 10% of responses on average — roughly 0.4 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for food and beverage: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Food and Beverage questions cover.

The 40 questions behind every percentage on this page were drawn from real food and beverage (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 food and beverage 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 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific food and beverage 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.

40 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 2026-07-06, 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 →