AI SEO Statistics: T Shirt (2026-07 edition)
15 questions · 45 AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — sampled from real buyer journeys in t shirt.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 15 questions
Model by model
23-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 t shirt buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 47% | 40% | 27% | 60% |
| Suggests DIY first | 7% | 0% | 0% | 93% |
| Names specific providers | 33% | 33% | 33% | 67% |
| Gives price or cost info | 13% | 27% | 40% | 47% |
| Tells to check reviews | 13% | 33% | 7% | 73% |
| Tells to verify credentials | 13% | 7% | 7% | 93% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 100% |
| Mentions local proximity | 20% | 40% | 20% | 47% |
| Gives selection criteria | 47% | 67% | 27% | 33% |
| Warns about red flags | 20% | 13% | 7% | 87% |
| Asks a clarifying question | 40% | 80% | 0% | 13% |
| Recommends multiple quotes | 7% | 27% | 0% | 67% |
By model
How each assistant handled T Shirt questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same t shirt questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 46.7% (ChatGPT) down to 26.7% (Gemini), a 20-point gap on an identical question set.
Across the 15 t shirt answers it produced, ChatGPT recommended hiring a professional in 46.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 33.3% of answers (about 1.5 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 13.3%, averaging 527 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 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 t shirt answers it produced, Claude recommended hiring a professional in 40% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 33.3% of answers (about 2.3 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 281 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 40%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 26.7%.
Across the 15 t shirt answers it produced, Gemini recommended hiring a professional in 26.7% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 33.3% of answers (about 0.5 distinct providers per answer) and included price or cost information 40% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 6.7%, averaging 226 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 20%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a t shirt buyer to a professional (46.7%) and Gemini the least (26.7%). ChatGPT produced the longest answers, at 527 words on average. Specific providers were named most often by ChatGPT (33.3%) — 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 23.3 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a t shirt buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 80% (Claude) — a 80-point spread.
- Gives selection criteria: from 26.7% (Gemini) to 66.7% (Claude) — a 40-point spread.
- Gives price or cost information: from 13.3% (ChatGPT) to 40% (Gemini) — a 27-point spread.
- Recommends multiple quotes: from 0% (Gemini) to 26.7% (Claude) — a 27-point spread.
- Tells the buyer to check reviews: from 6.7% (Gemini) to 33.3% (Claude) — a 27-point spread.
The widest single gap — asks a clarifying question, 80 points — means a t shirt 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 t shirt market.
Where they agree
The points of near-consensus in T Shirt.
On other behaviors the three models move almost in lockstep — the points of near-consensus for t shirt, where all three landed within a few points of each other:
- Names a specific provider: 33.3% across all three models.
- Mentions case studies or portfolio: 0% across all three models.
- Tells the buyer to verify credentials: 6.7%–13.3% across all three (a 7-point spread).
- Suggests a DIY approach first: 0%–6.7% 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" (13.3%).
Every behavior, measured
All twelve coded behaviors for T Shirt, averaged across the three models.
The behaviors AI models reproduce most often for t shirt are gives selection criteria (46.7% on average), asks a clarifying question (40%) and recommends hiring a professional (37.8%); the rarest are mentions case studies or portfolio (0%), suggests a DIY approach first (2.2%) and tells the buyer to verify credentials (8.9%). 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: 46.7% on average (ChatGPT 46.7%, Claude 66.7%, Gemini 26.7%) — a 40-point spread.
- Asks a clarifying question: 40% on average (ChatGPT 40%, Claude 80%, Gemini 0%) — a 80-point spread.
- Recommends hiring a professional: 37.8% on average (ChatGPT 46.7%, Claude 40%, Gemini 26.7%) — a 20-point spread.
- Names a specific provider: 33.3% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 33.3%).
- Gives price or cost information: 26.7% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 40%) — a 27-point spread.
- Mentions local proximity: 26.7% on average (ChatGPT 20%, Claude 40%, Gemini 20%) — a 20-point spread.
- Tells the buyer to check reviews: 17.8% on average (ChatGPT 13.3%, Claude 33.3%, Gemini 6.7%) — a 27-point spread.
- Warns about red flags or scams: 13.3% on average (ChatGPT 20%, Claude 13.3%, Gemini 6.7%) — a 13-point spread.
- Recommends multiple quotes: 11.1% on average (ChatGPT 6.7%, Claude 26.7%, Gemini 0%) — a 27-point spread.
- Tells the buyer to verify credentials: 8.9% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 6.7%) — a 7-point spread.
- Suggests a DIY approach first: 2.2% on average (ChatGPT 6.7%, Claude 0%, 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 t shirt buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the t shirt buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 17.8% of answers on average. Verifying credentials or certifications appeared in 8.9%. Warning about red flags or scams appeared in 13.3%.
On structuring the decision, a selection-criteria checklist showed up in 46.7% of answers on average and a recommendation to gather multiple quotes in 11.1%. The single least-reproduced protective signal for t shirt is "tells the buyer to verify credentials" at 8.9% 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 T Shirt providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 t shirt answers, a specific provider was named in 33.3% of responses on average — roughly 1.4 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for t shirt: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 T Shirt questions cover.
The 15 questions behind every percentage on this page were drawn from real t shirt (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 t shirt 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 2026-07-06, the figures describe this specific t shirt 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 2026-07-06, 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 →