Original research · 2026-07 edition

AI SEO Statistics: Winery (2026-07 edition)

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

The question bank

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

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

What are the best child-friendly wineries that have outdoor play areas or large lawns for kids?
Is it more cost-effective to rent a winery venue and hire outside catering or choose an all-inclusive package?
How can I verify if a boutique winery actually grows their own grapes or if they are just a tasting room for bulk wine?
What is the typical cost per person for a private cellar tour and library tasting for a group of eight?
Should I book a large commercial estate or a small family-owned vineyard for a more intimate anniversary dinner?
Are there any local vineyards that provide a shuttle or van service so our group doesn't have to drive?
What are some red flags that a winery is a tourist trap rather than a quality producer?
I need to find a vineyard with a private room for a surprise proposal this Saturday, who usually has last-minute availability?
Show all 15 questions
Does joining a wine club actually save money in the long run if you factor in shipping costs and commitment minimums?
Looking for a winery with a scenic view for a 40th birthday lunch with a budget of roughly $120 per head.
Do most wineries in the area allow dogs in the outdoor seating sections or are pets generally banned?
Which vineyards offer a more educational experience focused on viticulture and soil rather than just a standard flight?
Are there any wineries with paved or ADA-compliant paths through the vines for guests with limited mobility?
If we buy several cases after a tasting, will the winery typically ship them to our home address or do we need to transport them?
For a bachelorette group, is it better to book a formal seated tasting or a more casual standing bar experience?

Model by model

24-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 winery buyers.

Behavior rates across 15 winery buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional13%13%7%87%
Suggests DIY first13%33%13%60%
Names specific providers13%7%27%67%
Gives price or cost info27%20%27%67%
Tells to check reviews27%33%0%53%
Tells to verify credentials7%7%7%87%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity20%47%7%47%
Gives selection criteria47%80%47%27%
Warns about red flags7%20%13%87%
Asks a clarifying question53%87%7%0%
Recommends multiple quotes7%13%0%87%

By model

How each assistant handled Winery questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same winery questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 13.3% (ChatGPT) down to 6.7% (Gemini), a 7-point gap on an identical question set.

Across the 15 winery answers it produced, ChatGPT recommended hiring a professional in 13.3% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 13.3% of answers (about 1 distinct providers per answer) and included price or cost information 26.7% of the time. ChatGPT asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 6.7%, averaging 473 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 20%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 winery answers it produced, Claude recommended hiring a professional in 13.3% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 86.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 275 words per answer. On the remaining cues it told the buyer to check reviews in 33.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 46.7%; a selection-criteria checklist appeared in 80% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 winery answers it produced, Gemini recommended hiring a professional in 6.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 26.7% of answers (about 0.6 distinct providers per answer) and included price or cost information 26.7% of the time. Gemini asked a clarifying question before answering in 6.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 224 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a winery buyer to a professional (13.3%) and Gemini the least (6.7%). ChatGPT produced the longest answers, at 473 words on average. Specific providers were named most often by Gemini (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 24.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a winery buyer happens to ask matters most:

  • Asks a clarifying question: from 6.7% (Gemini) to 86.7% (Claude) — a 80-point spread.
  • Mentions local proximity: from 6.7% (Gemini) to 46.7% (Claude) — a 40-point spread.
  • Tells the buyer to check reviews: from 0% (Gemini) to 33.3% (Claude) — a 33-point spread.
  • Gives selection criteria: from 46.7% (ChatGPT) to 80% (Claude) — a 33-point spread.
  • Suggests a DIY approach first: from 13.3% (ChatGPT) to 33.3% (Claude) — a 20-point spread.

The widest single gap — asks a clarifying question, 80 points — means a winery 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 winery market.

Where they agree

The points of near-consensus in Winery.

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

  • Tells the buyer to verify credentials: 6.7% across all three models.
  • Mentions case studies or portfolio: 0% across all three models.
  • Recommends hiring a professional: 6.7%–13.3% across all three (a 7-point spread).
  • Gives price or cost information: 20%–26.7% 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" (0%).

Every behavior, measured

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

The behaviors AI models reproduce most often for winery are gives selection criteria (57.8% on average), asks a clarifying question (48.9%) and gives price or cost information (24.5%); the rarest are mentions case studies or portfolio (0%), recommends multiple quotes (6.7%) 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: 57.8% on average (ChatGPT 46.7%, Claude 80%, Gemini 46.7%) — a 33-point spread.
  • Asks a clarifying question: 48.9% on average (ChatGPT 53.3%, Claude 86.7%, Gemini 6.7%) — a 80-point spread.
  • Gives price or cost information: 24.5% on average (ChatGPT 26.7%, Claude 20%, Gemini 26.7%) — a 7-point spread.
  • Mentions local proximity: 24.5% on average (ChatGPT 20%, Claude 46.7%, Gemini 6.7%) — a 40-point spread.
  • Suggests a DIY approach first: 20% on average (ChatGPT 13.3%, Claude 33.3%, Gemini 13.3%) — a 20-point spread.
  • Tells the buyer to check reviews: 20% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 0%) — a 33-point spread.
  • Names a specific provider: 15.6% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 26.7%) — a 20-point spread.
  • Warns about red flags or scams: 13.3% on average (ChatGPT 6.7%, Claude 20%, Gemini 13.3%) — a 13-point spread.
  • Recommends hiring a professional: 11.1% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Tells the buyer to verify credentials: 6.7% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 6.7%).
  • Recommends multiple quotes: 6.7% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the winery buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the winery 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 6.7%. Warning about red flags or scams appeared in 13.3%.

On structuring the decision, a selection-criteria checklist showed up in 57.8% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for winery is "tells the buyer to verify credentials" at 6.7% 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 Winery providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 winery answers, a specific provider was named in 15.6% of responses on average — roughly 0.6 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for winery: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Winery questions cover.

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