Original research · 2026-07 edition

AI SEO Statistics: Bar (2026-07 edition)

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

See how AI talks about YOUR businessFull data table

The question bank

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

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 place to get drinks with a client where we can actually hear each other talk without loud music.
Is it cheaper to buy my own alcohol and hire a freelance bartender for a wedding or just book an all-inclusive open bar package?
What specific licenses and insurance coverage should I ask for when vetting a mobile bar service for a private backyard party?
What’s the average per-person cost for a 4-hour open bar with mid-shelf liquor in a major metropolitan area?
What are the pros and cons of a cash bar versus a consumption bar for a corporate holiday event with 50 employees?
Where can I find a bar that accommodates large groups of 20 or more people on a Saturday night without charging a massive reservation fee?
What are some warning signs that a high-end cocktail lounge might be overcharging for low-quality house spirits?
I need to book a private room for a 30th birthday party next weekend, which types of bars usually have the best last-minute availability?
Show all 15 questions
Are there any bars that specialize in high-quality non-alcoholic craft cocktails for a group that doesn't drink alcohol?
I have a 500 dollar budget for a small office happy hour, what's the best way to make that stretch for 15 people without looking cheap?
How many bartenders do I actually need to hire for a 150-guest wedding to ensure the line doesn't get too long?
Do mobile bar companies usually provide the ice and glassware or is that something the host is expected to coordinate?
What should I look for in a bar if I want a speakeasy vibe for a first date that is intimate but not too pretentious?
How can I tell if a bar actually uses fresh-squeezed juices and handmade syrups versus just using pre-made bottled mixes?
What kind of cancellation policy is standard when putting down a deposit for a private bar rental or a roped-off VIP section?

Model by model

21-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 bar buyers.

Behavior rates across 15 bar buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional20%20%13%67%
Suggests DIY first20%20%20%87%
Names specific providers7%27%27%73%
Gives price or cost info27%33%40%67%
Tells to check reviews27%27%7%67%
Tells to verify credentials0%7%7%87%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity33%53%27%47%
Gives selection criteria47%67%33%33%
Warns about red flags13%20%13%80%
Asks a clarifying question60%60%13%20%
Recommends multiple quotes0%7%0%93%

By model

How each assistant handled Bar questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same bar questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 20% (ChatGPT) down to 13.3% (Gemini), a 7-point gap on an identical question set.

Across the 15 bar answers it produced, ChatGPT recommended hiring a professional in 20% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 6.7% of answers (about 0.2 distinct providers per answer) and included price or cost information 26.7% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 0%, averaging 511 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 46.7% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 bar answers it produced, Claude recommended hiring a professional in 20% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 26.7% of answers (about 1.1 distinct providers per answer) and included price or cost information 33.3% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 282 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 53.3%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 bar answers it produced, Gemini recommended hiring a professional in 13.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 26.7% of answers (about 0.9 distinct providers per answer) and included price or cost information 40% of the time. Gemini asked a clarifying question before answering in 13.3% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 276 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, 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 33.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a bar buyer to a professional (20%) and Gemini the least (13.3%). ChatGPT produced the longest answers, at 511 words on average. Specific providers were named most often by Claude (26.7%) — even there, roughly one answer in 4 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 21.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a bar buyer happens to ask matters most:

  • Asks a clarifying question: from 13.3% (Gemini) to 60% (ChatGPT) — a 47-point spread.
  • Gives selection criteria: from 33.3% (Gemini) to 66.7% (Claude) — a 33-point spread.
  • Mentions local proximity: from 26.7% (Gemini) to 53.3% (Claude) — a 27-point spread.
  • Names a specific provider: from 6.7% (ChatGPT) to 26.7% (Claude) — a 20-point spread.
  • Tells the buyer to check reviews: from 6.7% (Gemini) to 26.7% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 47 points — means a bar 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 bar market.

Where they agree

The points of near-consensus in Bar.

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

  • Suggests a DIY approach first: 20% across all three models.
  • Mentions case studies or portfolio: 0% across all three models.
  • Recommends hiring a professional: 13.3%–20% across all three (a 7-point spread).
  • Tells the buyer to verify credentials: 0%–6.7% across all three (a 7-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 100% of questions) and least consistently on "asks a clarifying question" (20%).

Every behavior, measured

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

The behaviors AI models reproduce most often for bar are gives selection criteria (48.9% on average), asks a clarifying question (44.4%) and mentions local proximity (37.8%); the rarest are mentions case studies or portfolio (0%), recommends multiple quotes (2.2%) and tells the buyer to verify credentials (4.5%). 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: 48.9% on average (ChatGPT 46.7%, Claude 66.7%, Gemini 33.3%) — a 33-point spread.
  • Asks a clarifying question: 44.4% on average (ChatGPT 60%, Claude 60%, Gemini 13.3%) — a 47-point spread.
  • Mentions local proximity: 37.8% on average (ChatGPT 33.3%, Claude 53.3%, Gemini 26.7%) — a 27-point spread.
  • Gives price or cost information: 33.3% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 40%) — a 13-point spread.
  • Suggests a DIY approach first: 20% on average (ChatGPT 20%, Claude 20%, Gemini 20%).
  • Names a specific provider: 20% on average (ChatGPT 6.7%, Claude 26.7%, Gemini 26.7%) — a 20-point spread.
  • Tells the buyer to check reviews: 20% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 6.7%) — a 20-point spread.
  • Recommends hiring a professional: 17.8% on average (ChatGPT 20%, Claude 20%, Gemini 13.3%) — a 7-point spread.
  • Warns about red flags or scams: 15.5% on average (ChatGPT 13.3%, Claude 20%, Gemini 13.3%) — a 7-point spread.
  • Tells the buyer to verify credentials: 4.5% on average (ChatGPT 0%, Claude 6.7%, Gemini 6.7%) — a 7-point spread.
  • Recommends multiple quotes: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the bar buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the bar buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 20% of answers on average. Verifying credentials or certifications appeared in 4.5%. Warning about red flags or scams appeared in 15.5%.

On structuring the decision, a selection-criteria checklist showed up in 48.9% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for bar is "recommends multiple quotes" at 2.2% 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 Bar providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 bar answers, a specific provider was named in 20% of responses on average — roughly 0.7 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for bar: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Bar questions cover.

The 15 questions behind every percentage on this page were drawn from real bar (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 bar 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 bar 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 →