Original research · 2026-07 edition

AI SEO Statistics: Kitchen Renovation (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 kitchen renovation.

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

Is it worth it to renovate my kitchen before putting my house on the market next month?
How much does a mid-range kitchen remodel cost for a 150 square foot space?
What are the warning signs that a kitchen contractor isn't reliable?
Can I keep my existing cabinet boxes and just replace the doors to save money?
How do I know if the wall between my kitchen and living room is load-bearing?
What's the typical timeline for a full gut renovation of a kitchen?
Should I hire a kitchen designer or just work directly with a general contractor?
What are the pros and cons of quartz versus granite countertops for a family with young kids?
Show all 40 questions
Is it possible to live in the house while the kitchen is being remodeled, or should we move out?
How much of a deposit is reasonable to pay a kitchen contractor upfront?
Are there specific permits I need for moving a gas line during a kitchen reno?
What's the best way to vet a kitchen remodeler's previous work?
My kitchen cabinets are peeling; is this a sign I need a full replacement or can they be fixed?
How do I create a temporary kitchen setup while my main one is under construction?
What are the hidden costs people usually forget to budget for in a kitchen remodel?
Is it cheaper to buy my own appliances or have the contractor source them?
How can I tell if a kitchen renovation quote is too good to be true?
What's the difference between custom, semi-custom, and stock cabinets in terms of quality and price?
Should I replace my flooring before or after the new cabinets are installed?
What are the most durable backsplash materials that are easy to clean?
How do I find a local contractor who specializes in small, galley-style kitchens?
We found mold behind our sink; should we call a kitchen remodeler or a remediation specialist first?
What questions should I ask during a kitchen renovation consultation?
Is an island or a peninsula better for a kitchen with limited floor space?
How much ROI can I realistically expect from a $50,000 kitchen renovation?
What are the current trends in kitchen lighting that won't look dated in five years?
Should I choose a contractor who offers a fixed price or one who works on time and materials?
What should be included in a formal kitchen renovation contract to protect me?
Can I save money by doing the demolition of the old kitchen myself?
How do I handle a dispute with a kitchen contractor over the quality of the finish work?
What are the best cabinet hardware finishes for high-traffic kitchens?
Is it necessary to upgrade my electrical panel if I'm adding a double oven and a wine fridge?
How long does the design phase usually take before construction actually starts?
What's the best way to compare three different kitchen remodel quotes that have different scopes?
Are there eco-friendly or sustainable materials for kitchen cabinets that aren't overpriced?
What should I do if my contractor keeps pushing back the start date of my kitchen project?
How do I choose between an undermount and a farmhouse sink for a modern kitchen?
Is it better to go with a big design-build firm or a local independent handyman for a small kitchen update?
What type of insurance should a kitchen renovation company have to cover my home?
How do I plan a kitchen layout that maximizes workflow for two people cooking at once?

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 kitchen renovation buyers.

Behavior rates across 40 kitchen renovation buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional68%40%28%50%
Suggests DIY first23%8%13%85%
Names specific providers3%8%10%85%
Gives price or cost info25%33%33%50%
Tells to check reviews15%13%8%78%
Tells to verify credentials23%20%10%70%
Mentions case studies / portfolio18%5%5%80%
Mentions local proximity23%20%13%73%
Gives selection criteria33%35%25%63%
Warns about red flags8%5%8%83%
Asks a clarifying question53%65%0%23%
Recommends multiple quotes15%5%5%78%

By model

How each assistant handled Kitchen Renovation questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same kitchen renovation questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 67.5% (ChatGPT) down to 27.5% (Gemini), a 40-point gap on an identical question set.

Across the 40 kitchen renovation answers it produced, ChatGPT recommended hiring a professional in 67.5% of them and suggested a DIY approach first 22.5% 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 25% of the time. ChatGPT asked a clarifying question before answering in 52.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 22.5%, averaging 611 words per answer. On the remaining cues it told the buyer to check reviews in 15%, pointed to case studies or a portfolio in 17.5%, and framed the choice around local proximity in 22.5%; a selection-criteria checklist appeared in 32.5% of its answers and a recommendation to gather multiple quotes in 15%.

Across the 40 kitchen renovation answers it produced, Claude recommended hiring a professional in 40% of them and suggested a DIY approach first 7.5% 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 32.5% of the time. Claude asked a clarifying question before answering in 65% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 20%, averaging 288 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 5%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 kitchen renovation 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 10% of answers (about 0.2 distinct providers per answer) and included price or cost information 32.5% 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 10%, averaging 264 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 12.5%; a selection-criteria checklist appeared in 25% of its answers and a recommendation to gather multiple quotes in 5%.

Taken together, ChatGPT is the assistant most likely to route a kitchen renovation buyer to a professional (67.5%) and Gemini the least (27.5%). ChatGPT produced the longest answers, at 611 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 21.4 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a kitchen renovation buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 65% (Claude) — a 65-point spread.
  • Recommends hiring a professional: from 27.5% (Gemini) to 67.5% (ChatGPT) — a 40-point spread.
  • Suggests a DIY approach first: from 7.5% (Claude) to 22.5% (ChatGPT) — a 15-point spread.
  • Tells the buyer to verify credentials: from 10% (Gemini) to 22.5% (ChatGPT) — a 13-point spread.
  • Mentions case studies or portfolio: from 5% (Claude) to 17.5% (ChatGPT) — a 13-point spread.

The widest single gap — asks a clarifying question, 65 points — means a kitchen renovation 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 kitchen renovation market.

Where they agree

The points of near-consensus in Kitchen Renovation.

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

  • Warns about red flags or scams: 5%–7.5% across all three (a 3-point spread).
  • Names a specific provider: 2.5%–10% across all three (a 8-point spread).
  • Gives price or cost information: 25%–32.5% across all three (a 8-point spread).
  • Tells the buyer to check reviews: 7.5%–15% across all three (a 8-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "suggests a DIY approach first" (identical coding in 85% of questions) and least consistently on "asks a clarifying question" (22.5%).

Every behavior, measured

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

The behaviors AI models reproduce most often for kitchen renovation are recommends hiring a professional (45% on average), asks a clarifying question (39.2%) and gives selection criteria (30.8%); the rarest are warns about red flags or scams (6.7%), names a specific provider (6.7%) and recommends multiple quotes (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:

  • Recommends hiring a professional: 45% on average (ChatGPT 67.5%, Claude 40%, Gemini 27.5%) — a 40-point spread.
  • Asks a clarifying question: 39.2% on average (ChatGPT 52.5%, Claude 65%, Gemini 0%) — a 65-point spread.
  • Gives selection criteria: 30.8% on average (ChatGPT 32.5%, Claude 35%, Gemini 25%) — a 10-point spread.
  • Gives price or cost information: 30% on average (ChatGPT 25%, Claude 32.5%, Gemini 32.5%) — a 8-point spread.
  • Mentions local proximity: 18.3% on average (ChatGPT 22.5%, Claude 20%, Gemini 12.5%) — a 10-point spread.
  • Tells the buyer to verify credentials: 17.5% on average (ChatGPT 22.5%, Claude 20%, Gemini 10%) — a 13-point spread.
  • Suggests a DIY approach first: 14.2% on average (ChatGPT 22.5%, Claude 7.5%, Gemini 12.5%) — a 15-point spread.
  • Tells the buyer to check reviews: 11.7% on average (ChatGPT 15%, Claude 12.5%, Gemini 7.5%) — a 8-point spread.
  • Mentions case studies or portfolio: 9.2% on average (ChatGPT 17.5%, Claude 5%, Gemini 5%) — a 13-point spread.
  • Recommends multiple quotes: 8.3% on average (ChatGPT 15%, Claude 5%, Gemini 5%) — a 10-point spread.
  • Names a specific provider: 6.7% on average (ChatGPT 2.5%, Claude 7.5%, Gemini 10%) — a 8-point spread.
  • Warns about red flags or scams: 6.7% on average (ChatGPT 7.5%, Claude 5%, Gemini 7.5%) — a 3-point spread.

Trust signals

How well the models protect the kitchen renovation buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the kitchen renovation buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 11.7% of answers on average. Verifying credentials or certifications appeared in 17.5%. Warning about red flags or scams appeared in 6.7%.

On structuring the decision, a selection-criteria checklist showed up in 30.8% of answers on average and a recommendation to gather multiple quotes in 8.3%. The single least-reproduced protective signal for kitchen renovation is "warns about red flags or scams" 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 Kitchen Renovation providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 kitchen renovation answers, a specific provider was named in 6.7% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for kitchen renovation: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Kitchen Renovation questions cover.

The 40 questions behind every percentage on this page were drawn from real kitchen renovation (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 kitchen renovation 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 kitchen renovation 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 →