Original research · 2026-07 edition

AI SEO Statistics: Glass Repair (2026-07 edition)

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

See how AI talks about YOUR businessFull data table

The question bank

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

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

Why is my double pane window foggy inside and can it be fixed without replacing the whole thing?
How much does it cost to fix a 6-inch crack in a sliding glass door?
Is it cheaper to repair a window chip or just buy a new pane?
What's the average turnaround time for a custom-cut glass tabletop?
My window seal is broken, will that make my electric bill go up?
Can I use clear nail polish to stop a window crack from spreading temporarily?
What kind of glass is required by code for a bathroom window replacement?
How do I know if a glass repair company is overcharging me for emergency service?
Show all 40 questions
Should I hire a general handyman or a glass specialist for a cracked skylight?
What are the signs that a window frame is too rotted to hold new glass?
How much should I expect to pay for a 24-hour board-up service after a break-in?
Is it possible to replace just one pane in a double-pane window unit?
My kids threw a ball through a window; do I need to replace the frame too?
What's the difference between tempered and laminated glass for home security?
How do I find a glass repair shop that works with older wood sash windows?
Can a scratched glass patio door be buffed out or does it need replacing?
Are there any glass repair companies that offer financing for whole-house window replacements?
What should I ask a glass technician to ensure they are properly insured?
Why is there water leaking through the top of my window glass during heavy rain?
How long does the putty take to dry on a single-pane window repair?
Is it worth getting low-E glass if I'm only replacing one broken window in the room?
What are the red flags to look for in a glass repair quote?
Can a glazier fix a cracked mirror that is glued to the wall?
Will my homeowner's insurance cover a window that cracked because of extreme heat?
How do I measure a window for a glass-only replacement so I can get an accurate quote?
Do glass repair companies usually dispose of the old broken glass for you?
What is the price difference between standard glass and tinted glass for a front door?
My sliding glass door is stuck; is that a glass issue or a track issue?
Can I get a glass repair person to come out on a Sunday?
Is there a way to temporarily fix a hole in a window during winter until a pro arrives?
How much more expensive is it to get custom frosted glass for a shower door?
What is the warranty typically like for a professional window seal repair?
Can a glazier cut a hole in an existing window for a pet door?
How do I tell if my windows are single, double, or triple pane?
Why does my new window glass have a slight green tint compared to the old ones?
Is it safe to leave a cracked window as-is if it's in a guest room we don't use?
How do I clean up tiny glass shards from carpet before the repair person arrives?
What happens if the glass company breaks my window frame while trying to install the new pane?
Can I buy the glass myself at a hardware store and just hire someone to install it?
How do I verify that the glass being installed is actually tempered glass?

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 glass repair buyers.

Behavior rates across 40 glass repair buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional78%55%30%40%
Suggests DIY first35%28%23%85%
Names specific providers3%8%10%93%
Gives price or cost info20%25%20%75%
Tells to check reviews5%13%0%88%
Tells to verify credentials15%15%5%80%
Mentions case studies / portfolio5%0%0%95%
Mentions local proximity33%30%13%63%
Gives selection criteria30%33%10%63%
Warns about red flags8%13%8%83%
Asks a clarifying question73%65%0%13%
Recommends multiple quotes20%25%3%70%

By model

How each assistant handled Glass Repair questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same glass repair questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 77.5% (ChatGPT) down to 30% (Gemini), a 48-point gap on an identical question set.

Across the 40 glass repair answers it produced, ChatGPT recommended hiring a professional in 77.5% of them and suggested a DIY approach first 35% of the time. It named a specific provider in 2.5% of answers (about 0 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 72.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 15%, averaging 445 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 32.5%; a selection-criteria checklist appeared in 30% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 40 glass repair answers it produced, Claude recommended hiring a professional in 55% of them and suggested a DIY approach first 27.5% of the time. It named a specific provider in 7.5% of answers (about 0.4 distinct providers per answer) and included price or cost information 25% of the time. Claude asked a clarifying question before answering in 65% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 15%, averaging 281 words per answer. On the remaining cues it told the buyer to check reviews in 12.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 30%; a selection-criteria checklist appeared in 32.5% of its answers and a recommendation to gather multiple quotes in 25%.

Across the 40 glass repair answers it produced, Gemini recommended hiring a professional in 30% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 10% of answers (about 0.3 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 5%, averaging 291 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 12.5%; a selection-criteria checklist appeared in 10% of its answers and a recommendation to gather multiple quotes in 2.5%.

Taken together, ChatGPT is the assistant most likely to route a glass repair buyer to a professional (77.5%) and Gemini the least (30%). ChatGPT produced the longest answers, at 445 words on average. Specific providers were named most often by Gemini (10%) — even there, roughly one answer in 10 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 19.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a glass repair buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 72.5% (ChatGPT) — a 73-point spread.
  • Recommends hiring a professional: from 30% (Gemini) to 77.5% (ChatGPT) — a 48-point spread.
  • Gives selection criteria: from 10% (Gemini) to 32.5% (Claude) — a 23-point spread.
  • Recommends multiple quotes: from 2.5% (Gemini) to 25% (Claude) — a 23-point spread.
  • Mentions local proximity: from 12.5% (Gemini) to 32.5% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 73 points — means a glass repair 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 glass repair market.

Where they agree

The points of near-consensus in Glass Repair.

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

  • Gives price or cost information: 20%–25% across all three (a 5-point spread).
  • Mentions case studies or portfolio: 0%–5% across all three (a 5-point spread).
  • Warns about red flags or scams: 7.5%–12.5% across all three (a 5-point spread).
  • Names a specific provider: 2.5%–10% across all three (a 8-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 95% of questions) and least consistently on "asks a clarifying question" (12.5%).

Every behavior, measured

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

The behaviors AI models reproduce most often for glass repair are recommends hiring a professional (54.2% on average), asks a clarifying question (45.8%) and suggests a DIY approach first (28.3%); the rarest are mentions case studies or portfolio (1.7%), tells the buyer to check reviews (5.8%) and names a specific provider (6.7%). 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:

  • Recommends hiring a professional: 54.2% on average (ChatGPT 77.5%, Claude 55%, Gemini 30%) — a 48-point spread.
  • Asks a clarifying question: 45.8% on average (ChatGPT 72.5%, Claude 65%, Gemini 0%) — a 73-point spread.
  • Suggests a DIY approach first: 28.3% on average (ChatGPT 35%, Claude 27.5%, Gemini 22.5%) — a 13-point spread.
  • Mentions local proximity: 25% on average (ChatGPT 32.5%, Claude 30%, Gemini 12.5%) — a 20-point spread.
  • Gives selection criteria: 24.2% on average (ChatGPT 30%, Claude 32.5%, Gemini 10%) — a 23-point spread.
  • Gives price or cost information: 21.7% on average (ChatGPT 20%, Claude 25%, Gemini 20%) — a 5-point spread.
  • Recommends multiple quotes: 15.8% on average (ChatGPT 20%, Claude 25%, Gemini 2.5%) — a 23-point spread.
  • Tells the buyer to verify credentials: 11.7% on average (ChatGPT 15%, Claude 15%, Gemini 5%) — a 10-point spread.
  • Warns about red flags or scams: 9.2% on average (ChatGPT 7.5%, Claude 12.5%, Gemini 7.5%) — a 5-point spread.
  • Names a specific provider: 6.7% on average (ChatGPT 2.5%, Claude 7.5%, Gemini 10%) — a 8-point spread.
  • Tells the buyer to check reviews: 5.8% on average (ChatGPT 5%, Claude 12.5%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 1.7% on average (ChatGPT 5%, Claude 0%, Gemini 0%) — a 5-point spread.

Trust signals

How well the models protect the glass repair buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the glass repair buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 5.8% of answers on average. Verifying credentials or certifications appeared in 11.7%. Warning about red flags or scams appeared in 9.2%.

On structuring the decision, a selection-criteria checklist showed up in 24.2% of answers on average and a recommendation to gather multiple quotes in 15.8%. The single least-reproduced protective signal for glass repair is "tells the buyer to check reviews" at 5.8% 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 Glass Repair providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 glass repair answers, a specific provider was named in 6.7% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for glass repair: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Glass Repair questions cover.

The 40 questions behind every percentage on this page were drawn from real glass repair (home services; 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 glass repair 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 glass repair 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 →