Original research · 2026-07 edition

AI SEO Statistics: Biotech (2026-07 edition)

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

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The question bank

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

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

What are the pros and cons of building a custom LIMS in-house versus buying a cloud-based SaaS solution for a mid-sized lab?
How do I evaluate the security protocols of a biotech data platform to ensure it meets strict GDPR and HIPAA requirements?
What is the typical implementation timeline for an AI-driven drug discovery platform from contract signing to full team onboarding?
I need a list of questions to ask a bioinformatics software vendor to see if their API can actually handle high-throughput sequencing data at scale.
Are there any specialized SaaS tools for managing clinical trial documents that offer automated version control and audit trails for FDA audits?
My startup has a $50k annual budget for lab management software; what features should I prioritize and what should I expect to compromise on?
What red flags should I look for when reviewing a biotech software provider's service level agreement regarding data uptime and recovery?
How can I tell if a molecular modeling software is actually using advanced machine learning or if it's just a marketing buzzword?
Show all 15 questions
We are struggling with data silos between our wet lab and dry lab; what kind of integration software should we look for to bridge that gap?
Is it better to hire a consultant to build a custom data pipeline or subscribe to a specialized biotech SaaS platform for proteomics analysis?
Which biotech SaaS providers offer the best technical support for scientists who aren't necessarily software engineers?
What are the hidden costs of switching from a legacy on-premise bioinformatics system to a modern cloud-native architecture?
Can you compare the pricing models of top electronic lab notebooks—is it usually per user or based on the volume of data stored?
We need to automate our CRISPR design workflow; what specific technical specs should we look for in a design-as-a-service software?
How do I verify a biotech tech vendor's claims about their data encryption and SOC2 compliance before we share proprietary genomic data?

Model by model

16-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 biotech buyers.

Behavior rates across 15 biotech buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional33%7%7%73%
Suggests DIY first27%0%0%73%
Names specific providers13%47%33%60%
Gives price or cost info0%7%20%80%
Tells to check reviews0%7%0%93%
Tells to verify credentials27%13%7%60%
Mentions case studies / portfolio13%7%0%87%
Mentions local proximity0%0%0%100%
Gives selection criteria27%53%67%40%
Warns about red flags0%7%13%87%
Asks a clarifying question20%27%0%67%
Recommends multiple quotes7%0%0%93%

By model

How each assistant handled Biotech questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same biotech questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 33.3% (ChatGPT) down to 6.7% (Claude), a 27-point gap on an identical question set.

Across the 15 biotech answers it produced, ChatGPT recommended hiring a professional in 33.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 13.3% of answers (about 0.9 distinct providers per answer) and included price or cost information 0% of the time. ChatGPT asked a clarifying question before answering in 20% of cases, warned about red flags or scams in 0%, and told the buyer to verify credentials in 26.7%, averaging 861 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 biotech answers it produced, Claude recommended hiring a professional in 6.7% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 46.7% of answers (about 2.2 distinct providers per answer) and included price or cost information 6.7% of the time. Claude asked a clarifying question before answering in 26.7% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 13.3%, averaging 347 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 6.7%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 biotech answers it produced, Gemini recommended hiring a professional in 6.7% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 33.3% of answers (about 1.1 distinct providers per answer) and included price or cost information 20% 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 6.7%, averaging 211 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 0%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a biotech buyer to a professional (33.3%) and Claude the least (6.7%). ChatGPT produced the longest answers, at 861 words on average. Specific providers were named most often by Claude (46.7%) — even there, roughly one answer in 2 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 15.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a biotech buyer happens to ask matters most:

  • Gives selection criteria: from 26.7% (ChatGPT) to 66.7% (Gemini) — a 40-point spread.
  • Names a specific provider: from 13.3% (ChatGPT) to 46.7% (Claude) — a 33-point spread.
  • Suggests a DIY approach first: from 0% (Claude) to 26.7% (ChatGPT) — a 27-point spread.
  • Asks a clarifying question: from 0% (Gemini) to 26.7% (Claude) — a 27-point spread.
  • Recommends hiring a professional: from 6.7% (Claude) to 33.3% (ChatGPT) — a 27-point spread.

The widest single gap — gives selection criteria, 40 points — means a biotech 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 biotech market.

Where they agree

The points of near-consensus in Biotech.

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

  • Mentions local proximity: 0% across all three models.
  • Tells the buyer to check reviews: 0%–6.7% across all three (a 7-point spread).
  • Recommends multiple quotes: 0%–6.7% across all three (a 7-point spread).
  • Mentions case studies or portfolio: 0%–13.3% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions local proximity" (identical coding in 100% of questions) and least consistently on "gives selection criteria" (40%).

Every behavior, measured

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

The behaviors AI models reproduce most often for biotech are gives selection criteria (48.9% on average), names a specific provider (31.1%) and recommends hiring a professional (15.6%); the rarest are mentions local proximity (0%), recommends multiple quotes (2.2%) and tells the buyer to check reviews (2.2%). 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:

  • Gives selection criteria: 48.9% on average (ChatGPT 26.7%, Claude 53.3%, Gemini 66.7%) — a 40-point spread.
  • Names a specific provider: 31.1% on average (ChatGPT 13.3%, Claude 46.7%, Gemini 33.3%) — a 33-point spread.
  • Recommends hiring a professional: 15.6% on average (ChatGPT 33.3%, Claude 6.7%, Gemini 6.7%) — a 27-point spread.
  • Tells the buyer to verify credentials: 15.6% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 6.7%) — a 20-point spread.
  • Asks a clarifying question: 15.6% on average (ChatGPT 20%, Claude 26.7%, Gemini 0%) — a 27-point spread.
  • Suggests a DIY approach first: 8.9% on average (ChatGPT 26.7%, Claude 0%, Gemini 0%) — a 27-point spread.
  • Gives price or cost information: 8.9% on average (ChatGPT 0%, Claude 6.7%, Gemini 20%) — a 20-point spread.
  • Mentions case studies or portfolio: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Warns about red flags or scams: 6.7% on average (ChatGPT 0%, Claude 6.7%, Gemini 13.3%) — a 13-point spread.
  • Tells the buyer to check reviews: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Recommends multiple quotes: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
  • Mentions local proximity: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the biotech buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the biotech buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 2.2% of answers on average. Verifying credentials or certifications appeared in 15.6%. Warning about red flags or scams appeared in 6.7%.

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 2.2%. The single least-reproduced protective signal for biotech is "tells the buyer to check reviews" at 2.2% 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 Biotech providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 biotech answers, a specific provider was named in 31.1% of responses on average — roughly 1.4 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for biotech: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Biotech questions cover.

The 15 questions behind every percentage on this page were drawn from real biotech (technology / SaaS; 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 biotech 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 biotech 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 →