Original research · 2026-07 edition

AI SEO Statistics: Antique Shops (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-05

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The question bank

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

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

I found an old mahogany desk online, how can I tell if it's a genuine 19th-century piece or just a high-quality reproduction?
What are the biggest red flags to look for when buying mid-century modern furniture from an online estate sale?
Is it worth paying $200 for shipping on a vintage mirror, or should I just wait to find something similar at a local shop?
How do I verify the authenticity of a signed piece of art glass if the seller doesn't have a certificate of authenticity?
I want to start collecting Victorian-era jewelry but I'm worried about scams; what questions should I ask the shop owner before buying?
Does refinishing an antique dresser significantly lower its resale value compared to keeping the original beat-up finish?
What's the difference between antique and vintage when I'm searching for home decor online?
How can I tell if a website selling rare coins is a reputable dealer or just a dropshipping site?
Show all 15 questions
I have a $500 budget for a unique wedding gift; what kind of silver antiques would be a good investment at that price point?
What kind of return policies are standard for high-end online antique galleries if the item arrives damaged or not as described?
Are there specific wood grain patterns or joinery styles I should look for to prove a table was handmade before 1900?
Is it better to buy a restored vintage typewriter that works or an unrestored one for half the price and try to fix it myself?
How do I track down a specific discontinued pattern of fine china from the 1950s without overpaying on auction sites?
What are the risks of buying antique electrical items like lamps online, and should I expect to have them rewired immediately?
I need a statement piece for my office by next week; which online antique retailers are known for the fastest white-glove delivery?

Model by model

28-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 antique shops buyers.

Behavior rates across 15 antique shops buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional73%60%7%33%
Suggests DIY first60%40%13%47%
Names specific providers47%20%13%47%
Gives price or cost info13%27%0%60%
Tells to check reviews7%13%0%87%
Tells to verify credentials7%33%7%73%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity7%27%7%67%
Gives selection criteria13%73%20%27%
Warns about red flags20%60%20%33%
Asks a clarifying question27%53%0%27%
Recommends multiple quotes0%7%0%93%

By model

How each assistant handled Antique Shops questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same antique shops questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 73.3% (ChatGPT) down to 6.7% (Gemini), a 67-point gap on an identical question set.

Across the 15 antique shops answers it produced, ChatGPT recommended hiring a professional in 73.3% of them and suggested a DIY approach first 60% of the time. It named a specific provider in 46.7% of answers (about 2.5 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 26.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 615 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 6.7%; a selection-criteria checklist appeared in 13.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 antique shops answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 40% of the time. It named a specific provider in 20% of answers (about 0.9 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 60%, and told the buyer to verify credentials in 33.3%, averaging 306 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, 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 73.3% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 antique shops 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 13.3% of answers (about 0.3 distinct providers per answer) and included price or cost information 0% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 199 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 20% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route an antique shops buyer to a professional (73.3%) and Gemini the least (6.7%). ChatGPT produced the longest answers, at 615 words on average. Specific providers were named most often by ChatGPT (46.7%) — 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 28.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant an antique shops buyer happens to ask matters most:

  • Recommends hiring a professional: from 6.7% (Gemini) to 73.3% (ChatGPT) — a 67-point spread.
  • Gives selection criteria: from 13.3% (ChatGPT) to 73.3% (Claude) — a 60-point spread.
  • Asks a clarifying question: from 0% (Gemini) to 53.3% (Claude) — a 53-point spread.
  • Suggests a DIY approach first: from 13.3% (Gemini) to 60% (ChatGPT) — a 47-point spread.
  • Warns about red flags or scams: from 20% (ChatGPT) to 60% (Claude) — a 40-point spread.

The widest single gap — recommends hiring a professional, 67 points — means an antique shops 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 antique shops market.

Where they agree

The points of near-consensus in Antique Shops.

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

  • Mentions case studies or portfolio: 0% across all three models.
  • Recommends multiple quotes: 0%–6.7% across all three (a 7-point spread).
  • Tells the buyer to check reviews: 0%–13.3% across all three (a 13-point spread).
  • Mentions local proximity: 6.7%–26.7% across all three (a 20-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" (26.7%).

Every behavior, measured

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

The behaviors AI models reproduce most often for antique shops are recommends hiring a professional (46.7% on average), suggests a DIY approach first (37.8%) and gives selection criteria (35.5%); the rarest are mentions case studies or portfolio (0%), recommends multiple quotes (2.2%) and tells the buyer to check reviews (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:

  • Recommends hiring a professional: 46.7% on average (ChatGPT 73.3%, Claude 60%, Gemini 6.7%) — a 67-point spread.
  • Suggests a DIY approach first: 37.8% on average (ChatGPT 60%, Claude 40%, Gemini 13.3%) — a 47-point spread.
  • Gives selection criteria: 35.5% on average (ChatGPT 13.3%, Claude 73.3%, Gemini 20%) — a 60-point spread.
  • Warns about red flags or scams: 33.3% on average (ChatGPT 20%, Claude 60%, Gemini 20%) — a 40-point spread.
  • Names a specific provider: 26.7% on average (ChatGPT 46.7%, Claude 20%, Gemini 13.3%) — a 33-point spread.
  • Asks a clarifying question: 26.7% on average (ChatGPT 26.7%, Claude 53.3%, Gemini 0%) — a 53-point spread.
  • Tells the buyer to verify credentials: 15.6% on average (ChatGPT 6.7%, Claude 33.3%, Gemini 6.7%) — a 27-point spread.
  • Mentions local proximity: 13.4% on average (ChatGPT 6.7%, Claude 26.7%, Gemini 6.7%) — a 20-point spread.
  • Gives price or cost information: 13.3% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 0%) — a 27-point spread.
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 0%) — a 13-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 antique shops buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the antique shops buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 15.6%. Warning about red flags or scams appeared in 33.3%.

On structuring the decision, a selection-criteria checklist showed up in 35.5% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for antique shops 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 Antique Shops providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 antique shops answers, a specific provider was named in 26.7% of responses on average — roughly 1.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for antique shops: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Antique Shops questions cover.

The 15 questions behind every percentage on this page were drawn from real antique shops (ecommerce / online retail; 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 antique shops 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 2026-07-05, the figures describe this specific antique shops 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 2026-07-05, 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 →