AI SEO Statistics: Translators (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your translators SEO strategy.
This benchmark explains how AI assistants advise buyers. The related service page turns those findings into the technical, content, authority, and conversion priorities for this market.
The question bank
The questions we tested: a frozen buyer-intent benchmark for translators.
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 40 questions
Model by model
21.8% 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 40 translators benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 72.5% | 57.5% | 45% | 58.3% |
| Suggests DIY first | 5% | 0% | 0% | 1.7% |
| Names specific providers | 7.5% | 22.5% | 12.5% | 14.2% |
| Gives price or cost info | 5% | 17.5% | 17.5% | 13.3% |
| Tells to check reviews | 10% | 12.5% | 0% | 7.5% |
| Tells to verify credentials | 22.5% | 35% | 7.5% | 21.7% |
| Mentions case studies / portfolio | 17.5% | 17.5% | 0% | 11.7% |
| Mentions local proximity | 12.5% | 12.5% | 5% | 10% |
| Gives selection criteria | 30% | 55% | 20% | 35% |
| Warns about red flags | 7.5% | 15% | 10% | 10.8% |
| Asks a clarifying question | 45% | 70% | 0% | 38.3% |
| Recommends multiple quotes | 0% | 12.5% | 0% | 4.2% |
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 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 62.5% |
| Suggests DIY first | 95% |
| Names specific providers | 72.5% |
| Gives price or cost info | 75% |
| Tells to check reviews | 80% |
| Tells to verify credentials | 60% |
| Mentions case studies / portfolio | 70% |
| Mentions local proximity | 80% |
| Gives selection criteria | 37.5% |
| Warns about red flags | 75% |
| Asks a clarifying question | 12.5% |
| Recommends multiple quotes | 87.5% |
By model
How each assistant handled Translators questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same translators questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 72.5% (ChatGPT) down to 45% (Gemini), a 28-point gap on an identical question set.
Across the 40 translators answers it produced, ChatGPT recommended hiring a professional in 72.5% of them and suggested a DIY approach first 5% of the time. It named a specific provider in 7.5% of answers (about 0.3 distinct providers per answer) and included price or cost information 5% of the time. ChatGPT asked a clarifying question before answering in 45% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 22.5%, averaging 491 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 17.5%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 30% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 40 translators answers it produced, Claude recommended hiring a professional in 57.5% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 22.5% of answers (about 0.5 distinct providers per answer) and included price or cost information 17.5% of the time. Claude asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 35%, averaging 291 words per answer. On the remaining cues it told the buyer to check reviews in 12.5%, pointed to case studies or a portfolio in 17.5%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 55% of its answers and a recommendation to gather multiple quotes in 12.5%.
Across the 40 translators answers it produced, Gemini recommended hiring a professional in 45% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 12.5% of answers (about 0.6 distinct providers per answer) and included price or cost information 17.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 7.5%, averaging 278 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 20% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a buyer researching translators toward professional help (72.5%) and Gemini the least (45%). ChatGPT produced the longest answers, at 491 words on average. Specific providers were named most often by Claude (22.5%). Even there, roughly one answer in 4 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 21.8%. This is 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 the choice of assistant matters most for a buyer researching translators:
- Asks a clarifying question: from 0% (Gemini) to 70% (Claude). The spread is 70 points.
- Gives selection criteria: from 20% (Gemini) to 55% (Claude). The spread is 35 points.
- Recommends hiring a professional: from 45% (Gemini) to 72.5% (ChatGPT). The spread is 28 points.
- Tells the buyer to verify credentials: from 7.5% (Gemini) to 35% (Claude). The spread is 28 points.
- Mentions case studies or portfolio: from 0% (Gemini) to 17.5% (ChatGPT). The spread is 18 points.
The widest single gap concerns asks a clarifying question at 70 points. This means a buyer researching translators 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 translators market.
Where they agree
The points of near-consensus in Translators.
On other behaviors the three models move almost in lockstep. The points of near-consensus for translators, where all three landed within a few points of each other:
- Suggests a DIY approach first: 0%–5% across all three (a 5-point spread).
- Mentions local proximity: 5%–12.5% across all three (a 8-point spread).
- Warns about red flags or scams: 7.5%–15% across all three (a 8-point spread).
- Gives price or cost information: 5%–17.5% across all three (a 13-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" (12.5%).
Every behavior, measured
All twelve coded behaviors for Translators, averaged across the three models.
The behaviors AI models reproduce most often for translators are recommends hiring a professional (58.3% on average), asks a clarifying question (38.3%) and gives selection criteria (35%); the rarest are suggests a DIY approach first (1.7%), recommends multiple quotes (4.2%) and tells the buyer to check reviews (7.5%). 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: 58.3% on average (ChatGPT 72.5%, Claude 57.5%, Gemini 45%). The spread is 28 points.
- Asks a clarifying question: 38.3% on average (ChatGPT 45%, Claude 70%, Gemini 0%). The spread is 70 points.
- Gives selection criteria: 35% on average (ChatGPT 30%, Claude 55%, Gemini 20%). The spread is 35 points.
- Tells the buyer to verify credentials: 21.7% on average (ChatGPT 22.5%, Claude 35%, Gemini 7.5%). The spread is 28 points.
- Names a specific provider: 14.2% on average (ChatGPT 7.5%, Claude 22.5%, Gemini 12.5%). The spread is 15 points.
- Gives price or cost information: 13.3% on average (ChatGPT 5%, Claude 17.5%, Gemini 17.5%). The spread is 13 points.
- Mentions case studies or portfolio: 11.7% on average (ChatGPT 17.5%, Claude 17.5%, Gemini 0%). The spread is 18 points.
- Warns about red flags or scams: 10.8% on average (ChatGPT 7.5%, Claude 15%, Gemini 10%). The spread is 8 points.
- Mentions local proximity: 10% on average (ChatGPT 12.5%, Claude 12.5%, Gemini 5%). The spread is 8 points.
- Tells the buyer to check reviews: 7.5% on average (ChatGPT 10%, Claude 12.5%, Gemini 0%). The spread is 13 points.
- Recommends multiple quotes: 4.2% on average (ChatGPT 0%, Claude 12.5%, Gemini 0%). The spread is 13 points.
- Suggests a DIY approach first: 1.7% on average (ChatGPT 5%, Claude 0%, Gemini 0%). The spread is 5 points.
Trust signals
How well the models protect the translators buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the translators buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 7.5% of answers on average. Verifying credentials or certifications appeared in 21.7%. Warning about red flags or scams appeared in 10.8%.
On structuring the decision, a selection-criteria checklist showed up in 35% of answers on average and a recommendation to gather multiple quotes in 4.2%. The single least-reproduced protective signal for translators is "recommends multiple quotes" at 4.2% on average. This is 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 Translators providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 translators answers, a specific provider was named in 14.2% of responses on average, or roughly 0.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for translators: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Translators questions cover.
The 40 questions behind every percentage on this page form a frozen translators (professional services; 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 translators question set rather than 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. It is not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific translators 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 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 2026-07-06 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: Translators (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/professional/translators