Original research · 2026-07 edition

AI SEO Statistics: Fertility (2026-07 edition)

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

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The question bank

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

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

We've been trying for over a year without success, should we see a fertility specialist or just keep trying naturally?
What is the total out-of-pocket cost for a single IVF cycle including the medications and anesthesia?
How do I interpret my AMH and FSH levels and what do they mean for my chances of getting pregnant?
Is it better to go to a small boutique fertility clinic or a large national network for personalized care?
What specific questions should I ask during a first consultation with a reproductive endocrinologist?
Can I do anything to improve my egg quality naturally before starting an IVF cycle?
What's the difference between IUI and IVF success rates for a woman in her mid-30s?
Are there any fertility clinics in my state that offer money-back guarantees if the treatment fails?
Show all 40 questions
How long is the typical waitlist for an initial consultation at a top-rated fertility center?
Does health insurance usually cover diagnostic fertility testing even if they don't cover the actual IVF?
What are the most common side effects of the hormones used during the egg retrieval process?
Should I freeze my eggs now at 32 or is it safe to wait a few years until my career is more stable?
My OBGYN suggested Clomid, but should I go straight to a specialist for better monitoring?
How do I vet a fertility clinic's lab quality and the experience level of their embryologists?
What are the major red flags to look out for during a tour of a fertility clinic?
Is it significantly cheaper to travel abroad for IVF, and what are the medical risks of doing that?
How many embryos is it standard to transfer at once for a first-time patient to avoid multiples?
What is the success rate for frozen embryo transfers compared to fresh ones in recent years?
Are there specific financing plans or medical loans designed specifically for fertility treatments?
What's the process for using a donor egg, and how do we go about choosing a donor from a bank?
I have PCOS, so what is the most effective fertility treatment path for me to start with?
Why would a clinic recommend ICSI over standard fertilization during an IVF cycle?
Are there any holistic or lifestyle changes that actually impact the outcome of a fertility clinic cycle?
How do I find a clinic that is LGBTQ+ friendly and has experience with reciprocal IVF for lesbian couples?
What happens to our unused embryos after we are done having children and don't want more?
Is it normal for a fertility clinic to require a psychological evaluation before starting a cycle?
How do I accurately compare SART data between different clinics to see who has the best results for my age?
What are the hidden costs of egg freezing that aren't usually included in the advertised base price?
Can I switch fertility clinics in the middle of a cycle if I am unhappy with the communication?
What's the difference between a natural cycle IVF and a traditional medicated cycle?
How many rounds of IUI should we realistically try before moving on to more expensive IVF?
Do fertility clinics offer weekend or early morning appointments for patients with full-time jobs?
What is PGT-A testing and is it worth the extra three thousand dollars for a 38-year-old?
Are there any non-profit grants or scholarships available for couples who can't afford fertility care?
Why is my clinic recommending a freeze-all cycle instead of doing a fresh transfer right away?
How do I know if my male partner needs to see a specialized urologist before we begin IVF?
What should I expect during the egg retrieval recovery period in terms of pain and time off work?
Is it better to find a clinic that has an in-house pharmacy for the stimulation medications?
What are the signs that a fertility clinic is over-medicating patients just to boost their success stats?
How do I find a fertility specialist who specializes in endometriosis and recurrent pregnancy loss?

Model by model

15-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 fertility buyers.

Behavior rates across 40 fertility buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional63%45%23%55%
Suggests DIY first13%3%8%83%
Names specific providers3%8%10%90%
Gives price or cost info8%8%15%88%
Tells to check reviews10%15%5%88%
Tells to verify credentials13%10%8%90%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity28%18%8%73%
Gives selection criteria28%25%20%70%
Warns about red flags8%13%8%90%
Asks a clarifying question65%68%3%15%
Recommends multiple quotes13%5%0%85%

By model

How each assistant handled Fertility questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same fertility questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 62.5% (ChatGPT) down to 22.5% (Gemini), a 40-point gap on an identical question set.

Across the 40 fertility answers it produced, ChatGPT recommended hiring a professional in 62.5% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 2.5% of answers (about 0.2 distinct providers per answer) and included price or cost information 7.5% of the time. ChatGPT asked a clarifying question before answering in 65% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 12.5%, averaging 568 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 27.5%; a selection-criteria checklist appeared in 27.5% of its answers and a recommendation to gather multiple quotes in 12.5%.

Across the 40 fertility answers it produced, Claude recommended hiring a professional in 45% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 7.5% of answers (about 0.2 distinct providers per answer) and included price or cost information 7.5% of the time. Claude asked a clarifying question before answering in 67.5% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 10%, averaging 301 words per answer. On the remaining cues it told the buyer to check reviews in 15%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 17.5%; a selection-criteria checklist appeared in 25% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 fertility answers it produced, Gemini recommended hiring a professional in 22.5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 10% of answers (about 0.4 distinct providers per answer) and included price or cost information 15% 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 261 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 7.5%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a fertility buyer to a professional (62.5%) and Gemini the least (22.5%). ChatGPT produced the longest answers, at 568 words on average. Specific providers were named most often by Gemini (10%) — even there, roughly one answer in 10 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 15.3 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a fertility buyer happens to ask matters most:

  • Asks a clarifying question: from 2.5% (Gemini) to 67.5% (Claude) — a 65-point spread.
  • Recommends hiring a professional: from 22.5% (Gemini) to 62.5% (ChatGPT) — a 40-point spread.
  • Mentions local proximity: from 7.5% (Gemini) to 27.5% (ChatGPT) — a 20-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 12.5% (ChatGPT) — a 13-point spread.
  • Suggests a DIY approach first: from 2.5% (Claude) to 12.5% (ChatGPT) — a 10-point spread.

The widest single gap — asks a clarifying question, 65 points — means a fertility 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 fertility market.

Where they agree

The points of near-consensus in Fertility.

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

  • Mentions case studies or portfolio: 0% across all three models.
  • Tells the buyer to verify credentials: 7.5%–12.5% across all three (a 5-point spread).
  • Warns about red flags or scams: 7.5%–12.5% across all three (a 5-point spread).
  • Names a specific provider: 2.5%–10% across all three (a 8-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" (15%).

Every behavior, measured

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

The behaviors AI models reproduce most often for fertility are asks a clarifying question (45% on average), recommends hiring a professional (43.3%) and gives selection criteria (24.2%); the rarest are mentions case studies or portfolio (0%), recommends multiple quotes (5.8%) and names a specific provider (6.7%). 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:

  • Asks a clarifying question: 45% on average (ChatGPT 65%, Claude 67.5%, Gemini 2.5%) — a 65-point spread.
  • Recommends hiring a professional: 43.3% on average (ChatGPT 62.5%, Claude 45%, Gemini 22.5%) — a 40-point spread.
  • Gives selection criteria: 24.2% on average (ChatGPT 27.5%, Claude 25%, Gemini 20%) — a 8-point spread.
  • Mentions local proximity: 17.5% on average (ChatGPT 27.5%, Claude 17.5%, Gemini 7.5%) — a 20-point spread.
  • Gives price or cost information: 10% on average (ChatGPT 7.5%, Claude 7.5%, Gemini 15%) — a 8-point spread.
  • Tells the buyer to check reviews: 10% on average (ChatGPT 10%, Claude 15%, Gemini 5%) — a 10-point spread.
  • Tells the buyer to verify credentials: 10% on average (ChatGPT 12.5%, Claude 10%, Gemini 7.5%) — a 5-point spread.
  • Warns about red flags or scams: 9.2% on average (ChatGPT 7.5%, Claude 12.5%, Gemini 7.5%) — a 5-point spread.
  • Suggests a DIY approach first: 7.5% on average (ChatGPT 12.5%, Claude 2.5%, Gemini 7.5%) — a 10-point spread.
  • Names a specific provider: 6.7% on average (ChatGPT 2.5%, Claude 7.5%, Gemini 10%) — a 8-point spread.
  • Recommends multiple quotes: 5.8% on average (ChatGPT 12.5%, Claude 5%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the fertility buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the fertility buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 10% of answers on average. Verifying credentials or certifications appeared in 10%. Warning about red flags or scams appeared in 9.2%.

On structuring the decision, a selection-criteria checklist showed up in 24.2% of answers on average and a recommendation to gather multiple quotes in 5.8%. The single least-reproduced protective signal for fertility is "recommends multiple quotes" at 5.8% 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 Fertility providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 fertility answers, a specific provider was named in 6.7% 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 fertility: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Fertility questions cover.

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