AI SEO Statistics: Web3 (2026-07 edition)

Reading the 45 answers model by model shows how differently the three assistants treat the same web3 questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (ChatGPT) down to 53.3% (Gemini), a 27-point gap on an identical question set.

Across the 15 web3 answers it produced, ChatGPT recommended hiring a professional in 80% of them and suggested a DIY approach first 33.3% 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 20% of the time. ChatGPT asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 0%, averaging 744 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 26.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 web3 answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 53.3% of answers (about 2.4 distinct providers per answer) and included price or cost information 33.3% of the time. Claude asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 13.3%, averaging 350 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 20%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 web3 answers it produced, Gemini recommended hiring a professional in 53.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 46.7% of answers (about 2.1 distinct providers per answer) and included price or cost information 40% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 222 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 13.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a web3 buyer to a professional (80%) and Gemini the least (53.3%). ChatGPT produced the longest answers, at 744 words on average. Specific providers were named most often by Claude (53.3%) — even there, roughly one answer in 2 carried a name.

On other behaviors the three models move almost in lockstep — the points of near-consensus for web3, where all three landed within a few points of each other:

• 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).
• Suggests a DIY approach first: 20%–33.3% across all three (a 13-point spread).
• Tells the buyer to verify credentials: 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 "tells the buyer to check reviews" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (20%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the web3 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 6.7%. Warning about red flags or scams appeared in 20%.

On structuring the decision, a selection-criteria checklist showed up in 44.4% of answers on average and a recommendation to gather multiple quotes in 4.5%. The single least-reproduced protective signal for web3 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.

The 15 questions behind every percentage on this page were drawn from real web3 (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 web3 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.