Original research · 2026-07 edition

AI SEO Statistics: Slps (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

See how AI talks about YOUR businessFull data table

The question bank

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

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

My 2-year-old only says five words, should I be worried or wait until they're 3?
What is the difference between a speech teacher at school and a private SLP?
How much does a private speech therapy session cost if my insurance doesn't cover it?
Can speech therapy actually help an adult who has stuttered since childhood?
How do I know if a speech therapist is actually good with kids who have sensory issues?
What questions should I ask during a speech therapy consultation to see if they're a good fit?
Is teletherapy as effective as in-person visits for a toddler with a speech delay?
My dad is having trouble swallowing after his stroke, what kind of specialist do we need to hire?
Show all 40 questions
What are the red flags I should look for when choosing a pediatric speech clinic?
Can I do speech therapy exercises at home instead of hiring a professional?
How long does it usually take to see progress in speech therapy for a child with a lisp?
Do I need a doctor's referral to see a speech-language pathologist?
My child is getting speech therapy at school but it's only 20 minutes a week, is that enough?
What does the CCC-SLP credential actually mean and is it mandatory?
Are there speech therapists who specialize specifically in non-verbal autism?
How do I find an SLP who focuses on gender-affirming voice training?
Is it worth paying out of pocket for a private evaluation if the school waitlist is 6 months long?
What's the average hourly rate for a speech pathologist in a major city?
Can speech therapy help with social cues and conversation skills for teenagers?
Does insurance usually cover speech therapy for developmental delay or does it need a specific diagnosis?
How can I tell if my child has a speech disorder or just a heavy accent from our home language?
What should I expect during the first initial assessment for speech therapy?
Are there SLPs who come to your house for sessions or do I always have to go to a clinic?
My toddler is a gestalt language processor, how do I find a therapist who understands that?
What's the difference between speech therapy and occupational therapy for a child who isn't talking?
Can an SLP help with chewy eating habits or extreme picky eating in kids?
How many times a week is standard for a child with childhood apraxia of speech?
Is it better to hire a solo practitioner or go to a larger rehabilitation center?
What happens if my child doesn't like the speech therapist, should I push through or switch?
Are there intensive summer programs for speech therapy instead of doing it year-round?
How do I check if a speech therapist has had any disciplinary actions or complaints?
Can speech therapy help an adult with mumbling or poor projection at work?
What are the pros and cons of group speech therapy sessions versus one-on-one?
Why is there such a long waitlist for every speech pathologist in my area?
Does Medicare cover speech therapy for cognitive issues related to early-onset dementia?
What's the best way to explain speech therapy to a 5-year-old so they aren't scared?
Can an SLP help with a child who understands everything but refuses to speak?
If we move states, will our current speech therapy evaluation be valid for a new provider?
How do I know if my child is graduating from speech therapy or if the therapist is just ending sessions?
Are there specific SLPs who deal with feeding tubes and the transition to solid foods?

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 slps buyers.

Behavior rates across 40 slps buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional80%75%48%58%
Suggests DIY first13%8%8%95%
Names specific providers3%5%5%90%
Gives price or cost info8%10%8%93%
Tells to check reviews8%5%0%90%
Tells to verify credentials40%13%8%60%
Mentions case studies / portfolio8%0%0%93%
Mentions local proximity40%25%5%58%
Gives selection criteria55%38%18%48%
Warns about red flags13%18%8%78%
Asks a clarifying question83%73%3%5%
Recommends multiple quotes0%5%0%95%

By model

How each assistant handled Slps questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same slps questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (ChatGPT) down to 47.5% (Gemini), a 33-point gap on an identical question set.

Across the 40 slps answers it produced, ChatGPT recommended hiring a professional in 80% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 2.5% of answers (about 0 distinct providers per answer) and included price or cost information 7.5% of the time. ChatGPT asked a clarifying question before answering in 82.5% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 40%, averaging 486 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 40%; a selection-criteria checklist appeared in 55% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 40 slps answers it produced, Claude recommended hiring a professional in 75% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 10% of the time. Claude asked a clarifying question before answering in 72.5% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 12.5%, averaging 293 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 slps answers it produced, Gemini recommended hiring a professional in 47.5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 7.5% 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 7.5%, averaging 277 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 5%; a selection-criteria checklist appeared in 17.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a slps buyer to a professional (80%) and Gemini the least (47.5%). ChatGPT produced the longest answers, at 486 words on average. Specific providers were named most often by Claude (5%) — even there, roughly one answer in 20 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 slps buyer happens to ask matters most:

  • Asks a clarifying question: from 2.5% (Gemini) to 82.5% (ChatGPT) — a 80-point spread.
  • Gives selection criteria: from 17.5% (Gemini) to 55% (ChatGPT) — a 38-point spread.
  • Mentions local proximity: from 5% (Gemini) to 40% (ChatGPT) — a 35-point spread.
  • Recommends hiring a professional: from 47.5% (Gemini) to 80% (ChatGPT) — a 33-point spread.
  • Tells the buyer to verify credentials: from 7.5% (Gemini) to 40% (ChatGPT) — a 33-point spread.

The widest single gap — asks a clarifying question, 80 points — means a slps 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 slps market.

Where they agree

The points of near-consensus in Slps.

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

  • Names a specific provider: 2.5%–5% across all three (a 3-point spread).
  • Gives price or cost information: 7.5%–10% across all three (a 3-point spread).
  • Suggests a DIY approach first: 7.5%–12.5% across all three (a 5-point spread).
  • Recommends multiple quotes: 0%–5% across all three (a 5-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "suggests a DIY approach first" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (5%).

Every behavior, measured

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

The behaviors AI models reproduce most often for slps are recommends hiring a professional (67.5% on average), asks a clarifying question (52.5%) and gives selection criteria (36.7%); the rarest are recommends multiple quotes (1.7%), mentions case studies or portfolio (2.5%) and tells the buyer to check reviews (4.2%). 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:

  • Recommends hiring a professional: 67.5% on average (ChatGPT 80%, Claude 75%, Gemini 47.5%) — a 33-point spread.
  • Asks a clarifying question: 52.5% on average (ChatGPT 82.5%, Claude 72.5%, Gemini 2.5%) — a 80-point spread.
  • Gives selection criteria: 36.7% on average (ChatGPT 55%, Claude 37.5%, Gemini 17.5%) — a 38-point spread.
  • Mentions local proximity: 23.3% on average (ChatGPT 40%, Claude 25%, Gemini 5%) — a 35-point spread.
  • Tells the buyer to verify credentials: 20% on average (ChatGPT 40%, Claude 12.5%, Gemini 7.5%) — a 33-point spread.
  • Warns about red flags or scams: 12.5% on average (ChatGPT 12.5%, Claude 17.5%, Gemini 7.5%) — a 10-point spread.
  • Suggests a DIY approach first: 9.2% on average (ChatGPT 12.5%, Claude 7.5%, Gemini 7.5%) — a 5-point spread.
  • Gives price or cost information: 8.3% on average (ChatGPT 7.5%, Claude 10%, Gemini 7.5%) — a 3-point spread.
  • Names a specific provider: 4.2% on average (ChatGPT 2.5%, Claude 5%, Gemini 5%) — a 3-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: 2.5% on average (ChatGPT 7.5%, Claude 0%, Gemini 0%) — a 8-point spread.
  • Recommends multiple quotes: 1.7% on average (ChatGPT 0%, Claude 5%, Gemini 0%) — a 5-point spread.

Trust signals

How well the models protect the slps buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the slps 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 20%. Warning about red flags or scams appeared in 12.5%.

On structuring the decision, a selection-criteria checklist showed up in 36.7% of answers on average and a recommendation to gather multiple quotes in 1.7%. The single least-reproduced protective signal for slps 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 Slps providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 slps answers, a specific provider was named in 4.2% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for slps: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Slps questions cover.

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