Original research · 2026-07 edition

AI SEO Statistics: Blinds (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 blinds.

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

What's the best type of blind for a south-facing window that gets way too hot in the summer?
Is it worth paying for a professional to measure my windows or can I just do it myself with a tape measure?
How much should I expect to pay for custom faux wood blinds for a standard three-bedroom house?
I have oversized sliding glass doors, what are the modern alternatives to those old vertical plastic blinds?
Are motorized blinds actually reliable or do the motors burn out after a year?
Can I install blackout blinds without drilling into my window frames because I'm renting?
What's the difference between cellular shades and honeycomb blinds, or are they the same thing?
I have toddlers; what are the safest cordless blind options that won't break easily?
Show all 40 questions
How do I tell if a blind installation company is overcharging me for labor?
Is there a big price jump between off-the-shelf blinds from a big box store and custom-ordered ones?
My current blinds are yellowing from the sun; what material should I look for that won't discolor?
How long does it usually take from the initial consultation to actually having the blinds installed?
Should I go with real wood or faux wood blinds for a bathroom where it gets really humid?
What are some red flags I should look for when a contractor comes to give me a quote for window treatments?
Are Roman shades a nightmare to keep clean compared to roller shades?
Can someone help me automate my blinds to open and close with my smart home system?
Is it better to mount blinds inside the window frame or on the outside?
I'm moving into a new build next month, when should I start ordering my window coverings?
Do any blinds actually help with noise reduction if I live on a busy street?
What's the average lifespan of a high-quality roller shade before the spring mechanism fails?
Are there specific blinds that work better for arched or odd-shaped windows?
If I buy my own blinds online, will local handymen usually agree to install them for me?
What's the best way to get total darkness in a nursery for daytime naps?
How do I clean fabric vertical blinds without taking them all down?
Are shutters significantly more expensive than high-end blinds?
What's the warranty like on custom window treatments usually?
Can I get blinds that let me see out during the day but don't let people see in at night?
Why are my current blinds bowing in the middle, and how do I prevent that with new ones?
Is it cheaper to do a whole-house discount or just buy blinds room by room?
What kind of blinds are easiest to repair if a slat breaks?
Are there any eco-friendly or sustainable materials for window shades?
How do I find a local blind installer who specializes in motorized skylight covers?
Do custom blinds add any actual resale value to a home?
What's the difference between a light-filtering shade and a room-darkening one?
I have cats that climb everything; what blinds are the most pet-proof?
Should I get a professional in-home consultation or is a virtual one just as good?
Are there blinds that can be controlled by an app without needing a separate hub?
What's the typical deposit required when ordering custom-fit blinds?
How do I match the color of my blinds to my existing trim if I can't find an exact paint match?
Do I need to remove my old hardware before the new blind installers arrive?

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 blinds buyers.

Behavior rates across 40 blinds buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional48%23%18%53%
Suggests DIY first28%18%20%78%
Names specific providers13%28%40%55%
Gives price or cost info15%18%30%73%
Tells to check reviews8%5%0%90%
Tells to verify credentials10%8%5%90%
Mentions case studies / portfolio8%5%0%90%
Mentions local proximity18%15%15%65%
Gives selection criteria55%50%40%43%
Warns about red flags3%5%8%93%
Asks a clarifying question58%73%0%25%
Recommends multiple quotes8%13%0%85%

By model

How each assistant handled Blinds questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same blinds questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 47.5% (ChatGPT) down to 17.5% (Gemini), a 30-point gap on an identical question set.

Across the 40 blinds answers it produced, ChatGPT recommended hiring a professional in 47.5% of them and suggested a DIY approach first 27.5% of the time. It named a specific provider in 12.5% of answers (about 0.5 distinct providers per answer) and included price or cost information 15% of the time. ChatGPT asked a clarifying question before answering in 57.5% of cases, warned about red flags or scams in 2.5%, and told the buyer to verify credentials in 10%, averaging 422 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 7.5%, and framed the choice around local proximity in 17.5%; a selection-criteria checklist appeared in 55% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 blinds answers it produced, Claude recommended hiring a professional in 22.5% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 27.5% of answers (about 1 distinct providers per answer) and included price or cost information 17.5% of the time. Claude asked a clarifying question before answering in 72.5% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 7.5%, averaging 256 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 15%; a selection-criteria checklist appeared in 50% of its answers and a recommendation to gather multiple quotes in 12.5%.

Across the 40 blinds answers it produced, Gemini recommended hiring a professional in 17.5% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 40% of answers (about 1.5 distinct providers per answer) and included price or cost information 30% 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 264 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 15%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a blinds buyer to a professional (47.5%) and Gemini the least (17.5%). ChatGPT produced the longest answers, at 422 words on average. Specific providers were named most often by Gemini (40%) — even there, roughly one answer in 3 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 20.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a blinds buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 72.5% (Claude) — a 73-point spread.
  • Recommends hiring a professional: from 17.5% (Gemini) to 47.5% (ChatGPT) — a 30-point spread.
  • Names a specific provider: from 12.5% (ChatGPT) to 40% (Gemini) — a 28-point spread.
  • Gives price or cost information: from 15% (ChatGPT) to 30% (Gemini) — a 15-point spread.
  • Gives selection criteria: from 40% (Gemini) to 55% (ChatGPT) — a 15-point spread.

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

Where they agree

The points of near-consensus in Blinds.

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

  • Mentions local proximity: 15%–17.5% across all three (a 3-point spread).
  • Tells the buyer to verify credentials: 5%–10% across all three (a 5-point spread).
  • Warns about red flags or scams: 2.5%–7.5% across all three (a 5-point spread).
  • Tells the buyer to check reviews: 0%–7.5% across all three (a 8-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "warns about red flags or scams" (identical coding in 92.5% of questions) and least consistently on "asks a clarifying question" (25%).

Every behavior, measured

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

The behaviors AI models reproduce most often for blinds are gives selection criteria (48.3% on average), asks a clarifying question (43.3%) and recommends hiring a professional (29.2%); the rarest are mentions case studies or portfolio (4.2%), tells the buyer to check reviews (4.2%) and warns about red flags or scams (5%). 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:

  • Gives selection criteria: 48.3% on average (ChatGPT 55%, Claude 50%, Gemini 40%) — a 15-point spread.
  • Asks a clarifying question: 43.3% on average (ChatGPT 57.5%, Claude 72.5%, Gemini 0%) — a 73-point spread.
  • Recommends hiring a professional: 29.2% on average (ChatGPT 47.5%, Claude 22.5%, Gemini 17.5%) — a 30-point spread.
  • Names a specific provider: 26.7% on average (ChatGPT 12.5%, Claude 27.5%, Gemini 40%) — a 28-point spread.
  • Suggests a DIY approach first: 21.7% on average (ChatGPT 27.5%, Claude 17.5%, Gemini 20%) — a 10-point spread.
  • Gives price or cost information: 20.8% on average (ChatGPT 15%, Claude 17.5%, Gemini 30%) — a 15-point spread.
  • Mentions local proximity: 15.8% on average (ChatGPT 17.5%, Claude 15%, Gemini 15%) — a 3-point spread.
  • Tells the buyer to verify credentials: 7.5% on average (ChatGPT 10%, Claude 7.5%, Gemini 5%) — a 5-point spread.
  • Recommends multiple quotes: 6.7% on average (ChatGPT 7.5%, Claude 12.5%, Gemini 0%) — a 13-point spread.
  • Warns about red flags or scams: 5% on average (ChatGPT 2.5%, Claude 5%, Gemini 7.5%) — a 5-point spread.
  • Tells the buyer to check reviews: 4.2% on average (ChatGPT 7.5%, Claude 5%, Gemini 0%) — a 8-point spread.
  • Mentions case studies or portfolio: 4.2% on average (ChatGPT 7.5%, Claude 5%, Gemini 0%) — a 8-point spread.

Trust signals

How well the models protect the blinds buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the blinds buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 4.2% of answers on average. Verifying credentials or certifications appeared in 7.5%. Warning about red flags or scams appeared in 5%.

On structuring the decision, a selection-criteria checklist showed up in 48.3% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for blinds is "tells the buyer to check reviews" at 4.2% 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 Blinds providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 blinds answers, a specific provider was named in 26.7% of responses on average — roughly 1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for blinds: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Blinds questions cover.

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