AI SEO Statistics: Blockchain (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 blockchain.
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.
Show all 15 questions
Model by model
18.9% 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 prevalence across 15 blockchain benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 73.3% | 60% | 53.3% | 62.2% |
| Suggests DIY first | 6.7% | 6.7% | 0% | 4.5% |
| Names specific providers | 33.3% | 46.7% | 73.3% | 51.1% |
| Gives price or cost info | 13.3% | 33.3% | 26.7% | 24.4% |
| Tells to check reviews | 6.7% | 13.3% | 0% | 6.7% |
| Tells to verify credentials | 6.7% | 13.3% | 6.7% | 8.9% |
| Mentions case studies / portfolio | 0% | 13.3% | 6.7% | 6.7% |
| Mentions local proximity | 6.7% | 6.7% | 0% | 4.5% |
| Gives selection criteria | 20% | 60% | 40% | 40% |
| Warns about red flags | 0% | 6.7% | 13.3% | 6.7% |
| Asks a clarifying question | 26.7% | 40% | 0% | 22.2% |
| Recommends multiple quotes | 0% | 0% | 0% | 0% |
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.
All-model binary agreement by behavior across 15 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 80% |
| Suggests DIY first | 86.7% |
| Names specific providers | 40% |
| Gives price or cost info | 80% |
| Tells to check reviews | 86.7% |
| Tells to verify credentials | 80% |
| Mentions case studies / portfolio | 80% |
| Mentions local proximity | 86.7% |
| Gives selection criteria | 20% |
| Warns about red flags | 80% |
| Asks a clarifying question | 40% |
| Recommends multiple quotes | 100% |
By model
How each assistant handled Blockchain questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same blockchain questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 73.3% (ChatGPT) down to 53.3% (Gemini), a 20-point gap on an identical question set.
Across the 15 blockchain answers it produced, ChatGPT recommended hiring a professional in 73.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 33.3% of answers (about 1.3 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 26.7% of cases, warned about red flags or scams in 0%, and told the buyer to verify credentials in 6.7%, averaging 808 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 6.7%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 blockchain 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 46.7% of answers (about 2.5 distinct providers per answer) and included price or cost information 33.3% of the time. Claude asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 13.3%, averaging 362 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 13.3%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 blockchain answers it produced, Gemini recommended hiring a professional in 53.3% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 73.3% of answers (about 2.9 distinct providers per answer) and included price or cost information 26.7% 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 198 words per answer. On the remaining cues it told the buyer to check reviews in 0%, 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 40% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a blockchain buyer to a professional (73.3%) and Gemini the least (53.3%). ChatGPT produced the longest answers, at 808 words on average. Specific providers were named most often by Gemini (73.3%) — even there, roughly one answer in 1 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 18.9% — 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 blockchain buyer happens to ask matters most:
- Names a specific provider: from 33.3% (ChatGPT) to 73.3% (Gemini) — a 40-point spread.
- Gives selection criteria: from 20% (ChatGPT) to 60% (Claude) — a 40-point spread.
- Asks a clarifying question: from 0% (Gemini) to 40% (Claude) — a 40-point spread.
- Recommends hiring a professional: from 53.3% (Gemini) to 73.3% (ChatGPT) — a 20-point spread.
- Gives price or cost information: from 13.3% (ChatGPT) to 33.3% (Claude) — a 20-point spread.
The widest single gap — names a specific provider, 40 points — means a blockchain 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 blockchain market.
Where they agree
The points of near-consensus in Blockchain.
On other behaviors the three models move almost in lockstep — the points of near-consensus for blockchain, where all three landed within a few points of each other:
- Recommends multiple quotes: 0% across all three models.
- Tells the buyer to verify credentials: 6.7%–13.3% across all three (a 7-point spread).
- Suggests a DIY approach first: 0%–6.7% across all three (a 7-point spread).
- Mentions local proximity: 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 "recommends multiple quotes" (identical coding in 100% of questions) and least consistently on "gives selection criteria" (20%).
Every behavior, measured
All twelve coded behaviors for Blockchain, averaged across the three models.
The behaviors AI models reproduce most often for blockchain are recommends hiring a professional (62.2% on average), names a specific provider (51.1%) and gives selection criteria (40%); the rarest are recommends multiple quotes (0%), mentions local proximity (4.5%) and suggests a DIY approach first (4.5%). 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 73.3%, Claude 60%, Gemini 53.3%) — a 20-point spread.
- Names a specific provider: 51.1% on average (ChatGPT 33.3%, Claude 46.7%, Gemini 73.3%) — a 40-point spread.
- Gives selection criteria: 40% on average (ChatGPT 20%, Claude 60%, Gemini 40%) — a 40-point spread.
- Gives price or cost information: 24.4% on average (ChatGPT 13.3%, Claude 33.3%, Gemini 26.7%) — a 20-point spread.
- Asks a clarifying question: 22.2% on average (ChatGPT 26.7%, Claude 40%, Gemini 0%) — a 40-point spread.
- Tells the buyer to verify credentials: 8.9% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
- Tells the buyer to check reviews: 6.7% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 0%) — a 13-point spread.
- Mentions case studies or portfolio: 6.7% on average (ChatGPT 0%, Claude 13.3%, Gemini 6.7%) — 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.
- Suggests a DIY approach first: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
- Mentions local proximity: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
- Recommends multiple quotes: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the blockchain buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the blockchain 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 8.9%. Warning about red flags or scams appeared in 6.7%.
On structuring the decision, a selection-criteria checklist showed up in 40% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for blockchain 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 Blockchain providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 blockchain answers, a specific provider was named in 51.1% of responses on average — roughly 2.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for blockchain: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Blockchain questions cover.
The 15 questions behind every percentage on this page form a frozen blockchain (technology / SaaS; 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 blockchain 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 blockchain 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: Blockchain (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/technology/blockchain