AI SEO Statistics: Recording Studios (2026-07 edition)
40 questions · 120 AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — sampled from real buyer journeys in recording studios.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
18-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 recording studios buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 50% | 23% | 30% | 65% |
| Suggests DIY first | 15% | 18% | 5% | 80% |
| Names specific providers | 13% | 10% | 18% | 75% |
| Gives price or cost info | 15% | 13% | 30% | 68% |
| Tells to check reviews | 5% | 8% | 3% | 88% |
| Tells to verify credentials | 8% | 3% | 5% | 88% |
| Mentions case studies / portfolio | 13% | 20% | 10% | 73% |
| Mentions local proximity | 10% | 15% | 10% | 73% |
| Gives selection criteria | 43% | 45% | 38% | 55% |
| Warns about red flags | 5% | 5% | 8% | 93% |
| Asks a clarifying question | 55% | 60% | 3% | 23% |
| Recommends multiple quotes | 3% | 3% | 0% | 95% |
By model
How each assistant handled Recording Studios questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same recording studios questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 50% (ChatGPT) down to 22.5% (Claude), a 28-point gap on an identical question set.
Across the 40 recording studios answers it produced, ChatGPT recommended hiring a professional in 50% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 12.5% of answers (about 0.3 distinct providers per answer) and included price or cost information 15% of the time. ChatGPT asked a clarifying question before answering in 55% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 7.5%, averaging 474 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 10%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 2.5%.
Across the 40 recording studios 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 10% of answers (about 0.3 distinct providers per answer) and included price or cost information 12.5% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 2.5%, averaging 266 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 20%, and framed the choice around local proximity in 15%; a selection-criteria checklist appeared in 45% of its answers and a recommendation to gather multiple quotes in 2.5%.
Across the 40 recording studios answers it produced, Gemini recommended hiring a professional in 30% of them and suggested a DIY approach first 5% of the time. It named a specific provider in 17.5% of answers (about 0.5 distinct providers per answer) and included price or cost information 30% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 5%, averaging 275 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 10%, and framed the choice around local proximity in 10%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a recording studios buyer to a professional (50%) and Claude the least (22.5%). ChatGPT produced the longest answers, at 474 words on average. Specific providers were named most often by Gemini (17.5%) — even there, roughly one answer in 6 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 18.2 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a recording studios buyer happens to ask matters most:
- Asks a clarifying question: from 2.5% (Gemini) to 60% (Claude) — a 58-point spread.
- Recommends hiring a professional: from 22.5% (Claude) to 50% (ChatGPT) — a 28-point spread.
- Gives price or cost information: from 12.5% (Claude) to 30% (Gemini) — a 18-point spread.
- Suggests a DIY approach first: from 5% (Gemini) to 17.5% (Claude) — a 13-point spread.
- Mentions case studies or portfolio: from 10% (Gemini) to 20% (Claude) — a 10-point spread.
The widest single gap — asks a clarifying question, 58 points — means a recording studios 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 recording studios market.
Where they agree
The points of near-consensus in Recording Studios.
On other behaviors the three models move almost in lockstep — the points of near-consensus for recording studios, 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).
- Recommends multiple quotes: 0%–2.5% across all three (a 3-point spread).
- Tells the buyer to check reviews: 2.5%–7.5% across all three (a 5-point spread).
- Tells the buyer to verify credentials: 2.5%–7.5% across all three (a 5-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "recommends multiple quotes" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (22.5%).
Every behavior, measured
All twelve coded behaviors for Recording Studios, averaged across the three models.
The behaviors AI models reproduce most often for recording studios are gives selection criteria (41.7% on average), asks a clarifying question (39.2%) and recommends hiring a professional (34.2%); the rarest are recommends multiple quotes (1.7%), tells the buyer to verify credentials (5%) and tells the buyer to check reviews (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: 41.7% on average (ChatGPT 42.5%, Claude 45%, Gemini 37.5%) — a 8-point spread.
- Asks a clarifying question: 39.2% on average (ChatGPT 55%, Claude 60%, Gemini 2.5%) — a 58-point spread.
- Recommends hiring a professional: 34.2% on average (ChatGPT 50%, Claude 22.5%, Gemini 30%) — a 28-point spread.
- Gives price or cost information: 19.2% on average (ChatGPT 15%, Claude 12.5%, Gemini 30%) — a 18-point spread.
- Mentions case studies or portfolio: 14.2% on average (ChatGPT 12.5%, Claude 20%, Gemini 10%) — a 10-point spread.
- Names a specific provider: 13.3% on average (ChatGPT 12.5%, Claude 10%, Gemini 17.5%) — a 8-point spread.
- Suggests a DIY approach first: 12.5% on average (ChatGPT 15%, Claude 17.5%, Gemini 5%) — a 13-point spread.
- Mentions local proximity: 11.7% on average (ChatGPT 10%, Claude 15%, Gemini 10%) — a 5-point spread.
- Warns about red flags or scams: 5.8% on average (ChatGPT 5%, Claude 5%, Gemini 7.5%) — a 3-point spread.
- Tells the buyer to check reviews: 5% on average (ChatGPT 5%, Claude 7.5%, Gemini 2.5%) — a 5-point spread.
- Tells the buyer to verify credentials: 5% on average (ChatGPT 7.5%, Claude 2.5%, Gemini 5%) — a 5-point spread.
- Recommends multiple quotes: 1.7% on average (ChatGPT 2.5%, Claude 2.5%, Gemini 0%) — a 3-point spread.
Trust signals
How well the models protect the recording studios buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the recording studios buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 5% of answers on average. Verifying credentials or certifications appeared in 5%. Warning about red flags or scams appeared in 5.8%.
On structuring the decision, a selection-criteria checklist showed up in 41.7% of answers on average and a recommendation to gather multiple quotes in 1.7%. The single least-reproduced protective signal for recording studios is "recommends multiple quotes" at 1.7% 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 Recording Studios providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 recording studios answers, a specific provider was named in 13.3% of responses on average — roughly 0.4 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for recording studios: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Recording Studios questions cover.
The 40 questions behind every percentage on this page were drawn from real recording studios (professional 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 recording studios 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 recording studios 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 →