Original research · 2026-07 edition

AI SEO Statistics: Psychiatrist (2026-07 edition)

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

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in psychiatrist.

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

What is the main difference between seeing a psychiatrist and just talking to a therapist for chronic anxiety?
I’ve been feeling really low for three weeks; how do I know if I need medication or if I should just try to exercise more first?
How much does a typical initial psychiatric consultation cost if I am paying out of pocket without insurance?
What specific questions should I ask a new psychiatrist during the first appointment to make sure they are a good fit for me?
Can a psychiatrist help with an adult ADHD diagnosis, or do I need to see a neurologist for that?
Is it better to find a psychiatrist who does talk therapy or one who strictly focuses on medication management?
I am really worried about side effects; how do psychiatrists usually handle it if a patient is scared of taking meds?
How can I find a psychiatrist in my area that actually has openings within the next month instead of a six month wait?
Show all 15 questions
What are some red flags to look out for when I am reading online reviews for a local psychiatric clinic?
Do I need a formal referral from my primary care doctor to see a psychiatrist, or can I just book an appointment directly?
Is online tele-psychiatry just as effective as going to a physical office for treating something like bipolar disorder?
My teenager is struggling with severe mood swings; should I look for a general psychiatrist or a specialist in adolescent care?
What should I do if my psychiatrist isn't responding to my messages about a bad reaction to a new prescription?
Are there specific certifications I should look for if I want a psychiatrist who specializes in trauma-informed care?
How do I go about switching psychiatrists if I feel like my current one is just rushing me through a 15-minute appointment?

Model by model

19-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 psychiatrist buyers.

Behavior rates across 15 psychiatrist buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional87%73%47%53%
Suggests DIY first20%20%7%80%
Names specific providers20%13%20%73%
Gives price or cost info7%7%13%93%
Tells to check reviews7%7%7%100%
Tells to verify credentials27%20%7%60%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity47%20%27%60%
Gives selection criteria53%60%40%47%
Warns about red flags13%13%7%87%
Asks a clarifying question73%67%0%7%
Recommends multiple quotes0%0%0%100%

By model

How each assistant handled Psychiatrist questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same psychiatrist questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 86.7% (ChatGPT) down to 46.7% (Gemini), a 40-point gap on an identical question set.

Across the 15 psychiatrist answers it produced, ChatGPT recommended hiring a professional in 86.7% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 20% of answers (about 0.7 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 26.7%, averaging 491 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 46.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 psychiatrist answers it produced, Claude recommended hiring a professional in 73.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 13.3% of answers (about 0.4 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 13.3%, and told the buyer to verify credentials in 20%, averaging 292 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 60% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 psychiatrist answers it produced, Gemini 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 20% of answers (about 0.4 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 6.7%, and told the buyer to verify credentials in 6.7%, averaging 280 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 26.7%; 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 psychiatrist buyer to a professional (86.7%) and Gemini the least (46.7%). ChatGPT produced the longest answers, at 491 words on average. Specific providers were named most often by ChatGPT (20%) — even there, roughly one answer in 5 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 18.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a psychiatrist buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 73.3% (ChatGPT) — a 73-point spread.
  • Recommends hiring a professional: from 46.7% (Gemini) to 86.7% (ChatGPT) — a 40-point spread.
  • Mentions local proximity: from 20% (Claude) to 46.7% (ChatGPT) — a 27-point spread.
  • Tells the buyer to verify credentials: from 6.7% (Gemini) to 26.7% (ChatGPT) — a 20-point spread.
  • Gives selection criteria: from 40% (Gemini) to 60% (Claude) — a 20-point spread.

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

Where they agree

The points of near-consensus in Psychiatrist.

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

  • Tells the buyer to check reviews: 6.7% across all three models.
  • Mentions case studies or portfolio: 0% across all three models.
  • Recommends multiple quotes: 0% across all three models.
  • Gives price or cost information: 6.7%–13.3% across all three (a 7-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to check reviews" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (6.7%).

Every behavior, measured

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

The behaviors AI models reproduce most often for psychiatrist are recommends hiring a professional (68.9% on average), gives selection criteria (51.1%) and asks a clarifying question (46.7%); the rarest are recommends multiple quotes (0%), mentions case studies or portfolio (0%) and tells the buyer to check reviews (6.7%). 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: 68.9% on average (ChatGPT 86.7%, Claude 73.3%, Gemini 46.7%) — a 40-point spread.
  • Gives selection criteria: 51.1% on average (ChatGPT 53.3%, Claude 60%, Gemini 40%) — a 20-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: 31.1% on average (ChatGPT 46.7%, Claude 20%, Gemini 26.7%) — a 27-point spread.
  • Names a specific provider: 17.8% on average (ChatGPT 20%, Claude 13.3%, Gemini 20%) — a 7-point spread.
  • Tells the buyer to verify credentials: 17.8% on average (ChatGPT 26.7%, Claude 20%, Gemini 6.7%) — a 20-point spread.
  • Suggests a DIY approach first: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Warns about red flags or scams: 11.1% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Gives price or cost information: 8.9% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 6.7%).
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
  • Recommends multiple quotes: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the psychiatrist buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the psychiatrist buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 17.8%. Warning about red flags or scams appeared in 11.1%.

On structuring the decision, a selection-criteria checklist showed up in 51.1% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for psychiatrist is "recommends multiple quotes" at 0% 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 Psychiatrist providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 psychiatrist answers, a specific provider was named in 17.8% of responses on average — roughly 0.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for psychiatrist: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Psychiatrist questions cover.

The 15 questions behind every percentage on this page were drawn from real psychiatrist (healthcare 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 psychiatrist 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 psychiatrist 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 2026-07-04, 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 →