Original research · 2026-07 edition

AI SEO Statistics: Psychologist (2026-07 edition)

15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-04

The question bank

The questions we tested — a frozen buyer-intent benchmark for psychologist.

The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.

How do I know if I'm just going through a rough patch or if I actually need to see a psychologist for clinical depression?
Can I treat my own social anxiety with self-help workbooks, or is it a waste of time compared to professional therapy?
What is the actual difference between a psychologist and a licensed counselor when it comes to treating trauma?
I'm looking for a psychologist who uses CBT; what specific questions should I ask during a consultation to see if they're actually experts?
What is the typical hourly rate for a private practice psychologist in a major city if I'm paying out of pocket?
Is it better to find a psychologist who lives near me for in-person sessions or is video therapy just as effective for long-term results?
What are some warning signs or red flags in a first session that suggest a psychologist might not be a good fit for me?
I've been having daily panic attacks and need help fast; how long is the average waitlist for a new patient intake right now?
Show all 15 questions
How do I choose between a psychologist who specializes in psychodynamic therapy versus one who does dialectical behavior therapy?
Are there psychologists who offer sliding scale fees for people who don't have mental health insurance coverage?
What should I expect to happen during the very first diagnostic interview with a psychologist?
If I don't feel a 'click' with my psychologist after three sessions, is it okay to switch, or should I try to make it work?
How does the process of getting a superbill from an out-of-network psychologist work for insurance reimbursement?
I need a psychologist who is culturally competent regarding the LGBTQ+ experience; how do I verify their background in this area?
How can I measure progress in therapy so I know I'm not just paying someone to listen to me vent every week?

Model by model

17.8% question-level model disagreement.

This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.

Behavior matrixModel-by-model evidence
Measured

Behavior prevalence across 15 psychologist benchmark questions, 2026-07 edition. Last column: equal-model mean.

Behavior prevalence across 15 psychologist benchmark questions, 2026-07 edition. Last column: equal-model mean.
BehaviorChatGPTClaudeGeminiEqual-model mean
Recommends hiring a professional80%60%46.7%62.2%
Suggests DIY first13.3%6.7%13.3%11.1%
Names specific providers6.7%26.7%13.3%15.6%
Gives price or cost info13.3%6.7%13.3%11.1%
Tells to check reviews13.3%0%0%4.4%
Tells to verify credentials46.7%26.7%0%24.5%
Mentions case studies / portfolio6.7%0%0%2.2%
Mentions local proximity33.3%26.7%26.7%28.9%
Gives selection criteria66.7%53.3%26.7%48.9%
Warns about red flags20%26.7%13.3%20%
Asks a clarifying question73.3%66.7%0%46.7%
Recommends multiple quotes0%13.3%0%4.4%

Question-level agreement

How often all measured models received the same binary code.

Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.

Behavior matrixModel-by-model evidence
Measured

All-model binary agreement by behavior across 15 benchmark questions.

All-model binary agreement by behavior across 15 benchmark questions.
BehaviorAll-model agreement
Recommends hiring a professional66.7%
Suggests DIY first86.7%
Names specific providers80%
Gives price or cost info93.3%
Tells to check reviews86.7%
Tells to verify credentials53.3%
Mentions case studies / portfolio93.3%
Mentions local proximity73.3%
Gives selection criteria60%
Warns about red flags86.7%
Asks a clarifying question13.3%
Recommends multiple quotes86.7%

By model

How each assistant handled Psychologist questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same psychologist questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (ChatGPT) down to 46.7% (Gemini), a 33-point gap on an identical question set.

Across the 15 psychologist answers it produced, ChatGPT recommended hiring a professional in 80% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 6.7% of answers (about 0.2 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 46.7%, averaging 550 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 6.7%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 psychologist answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 26.7% of answers (about 0.5 distinct providers per answer) and included price or cost information 6.7% of the time. Claude asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 26.7%, averaging 301 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 26.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 psychologist answers it produced, Gemini recommended hiring a professional in 46.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 13.3% of answers (about 0.3 distinct providers per answer) and included price or cost information 13.3% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 0%, averaging 265 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 26.7%; 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 psychologist buyer to a professional (80%) and Gemini the least (46.7%). ChatGPT produced the longest answers, at 550 words on average. Specific providers were named most often by Claude (26.7%) — even there, roughly one answer in 4 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

Question-level model disagreement is 17.8% — the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where which assistant a psychologist buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 73.3% (ChatGPT) — a 73-point spread.
  • Tells the buyer to verify credentials: from 0% (Gemini) to 46.7% (ChatGPT) — a 47-point spread.
  • Gives selection criteria: from 26.7% (Gemini) to 66.7% (ChatGPT) — a 40-point spread.
  • Recommends hiring a professional: from 46.7% (Gemini) to 80% (ChatGPT) — a 33-point spread.
  • Names a specific provider: from 6.7% (ChatGPT) to 26.7% (Claude) — a 20-point spread.

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

Where they agree

The points of near-consensus in Psychologist.

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

  • Suggests a DIY approach first: 6.7%–13.3% across all three (a 7-point spread).
  • Gives price or cost information: 6.7%–13.3% across all three (a 7-point spread).
  • Mentions local proximity: 26.7%–33.3% across all three (a 7-point spread).
  • Mentions case studies or portfolio: 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 "gives price or cost information" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (13.3%).

Every behavior, measured

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

The behaviors AI models reproduce most often for psychologist are recommends hiring a professional (62.2% on average), gives selection criteria (48.9%) and asks a clarifying question (46.7%); the rarest are mentions case studies or portfolio (2.2%), recommends multiple quotes (4.4%) and tells the buyer to check reviews (4.4%). 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:

  • Recommends hiring a professional: 62.2% on average (ChatGPT 80%, Claude 60%, Gemini 46.7%) — a 33-point spread.
  • Gives selection criteria: 48.9% on average (ChatGPT 66.7%, Claude 53.3%, Gemini 26.7%) — a 40-point spread.
  • Asks a clarifying question: 46.7% on average (ChatGPT 73.3%, Claude 66.7%, Gemini 0%) — a 73-point spread.
  • Mentions local proximity: 28.9% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 26.7%) — a 7-point spread.
  • Tells the buyer to verify credentials: 24.5% on average (ChatGPT 46.7%, Claude 26.7%, Gemini 0%) — a 47-point spread.
  • Warns about red flags or scams: 20% on average (ChatGPT 20%, Claude 26.7%, Gemini 13.3%) — a 13-point spread.
  • Names a specific provider: 15.6% on average (ChatGPT 6.7%, Claude 26.7%, Gemini 13.3%) — a 20-point spread.
  • Suggests a DIY approach first: 11.1% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
  • Gives price or cost information: 11.1% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
  • Tells the buyer to check reviews: 4.4% on average (ChatGPT 13.3%, Claude 0%, Gemini 0%) — a 13-point spread.
  • Recommends multiple quotes: 4.4% on average (ChatGPT 0%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the psychologist buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the psychologist buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 4.4% of answers on average. Verifying credentials or certifications appeared in 24.5%. Warning about red flags or scams appeared in 20%.

On structuring the decision, a selection-criteria checklist showed up in 48.9% of answers on average and a recommendation to gather multiple quotes in 4.4%. The single least-reproduced protective signal for psychologist is "tells the buyer to check reviews" at 4.4% 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 Psychologist providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 psychologist answers, a specific provider was named in 15.6% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for psychologist: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Psychologist questions cover.

The 15 questions behind every percentage on this page form a frozen psychologist (healthcare services; buyer hiring decisions for this specific service) buyer-intent benchmark. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact psychologist 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 2026-07-04, the figures describe this specific psychologist 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 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-04 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →

Citation

Cite this edition.

Authority Specialist. “AI SEO Statistics: Psychologist (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/health/psychologist