Original research · 2026-07 edition

AI SEO Statistics: Translators (2026-07 edition)

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

See how AI talks about YOUR businessFull data table

The question bank

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

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

What is the difference between a certified translation and a regular one for immigration paperwork?
Is it better to hire a translation agency or a freelance translator for a 200-page book?
How do I know if a translator actually knows the technical jargon for medical device manufacturing?
How much should I expect to pay per word for a Spanish to English legal contract?
Can I just use a translation app for a business meeting or do I need a professional interpreter?
What are the red flags to watch out for when hiring someone on a freelance platform for translation?
Do translators usually charge by the word, the hour, or by the page?
I need a birth certificate translated for a visa application, does it have to be notarized as well?
Show all 40 questions
How can I test a translator's quality if I do not speak the target language myself?
Is machine translation with human editing actually cheaper than standard professional translation?
Why do some translators charge a minimum fee even for a one-paragraph email?
What is the standard turnaround time for a 5,000-word technical manual?
Does a translator need to be a native speaker of the language they are translating into?
Should I look for a translator who specializes in marketing if I am launching a product abroad?
How do I find a translator who understands specific regional dialects like Mexican versus Castilian Spanish?
Is it worth paying extra for a second proofreader to check the translator's work?
What certifications like ATA actually matter when hiring a professional translator?
How do I handle sensitive data and NDAs when working with a remote translator?
Can a translator help me localize my website or do I need a different service for that?
What happens if I find a mistake in the translation after I have already paid the invoice?
I have a handwritten old family letter in German, can a professional translator read old cursive?
Do I need to provide a glossary of terms to the translator before they start the project?
Is it cheaper to hire a translator from a country with a lower cost of living?
How do I get a certified translation of my diploma for a job application in another country?
What is the difference between translation and transcreation for a global ad campaign?
Can a translator work directly inside my website's CMS like WordPress or Shopify?
I need a rush job on a legal brief, what is the typical markup for 24-hour delivery?
How do I verify a translator's credentials if they are based in another country?
Is it better to use one big agency for ten languages or find ten individual freelancers?
What is the average cost to translate a 1,000-word blog post into French?
Do translators usually offer a free sample or do I have to pay for a test piece?
How do I know if a translation agency is just using AI and charging me full price?
I need an interpreter for a court date, is that the same person who translates documents?
What information should I include in a project brief for a technical translator?
Can a translator help with the layout and formatting of a translated PDF document?
Why is there such a huge price gap between different translation quotes I received?
Is there a specific type of liability insurance a professional translator should have?
How do I find a translator who is an expert in patent law specifically?
What are the risks of using a cheap translation service for a medical consent form?
Do I need to pay the translator the full amount upfront or after the work is delivered?

Model by model

22-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 translators buyers.

Behavior rates across 40 translators buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional73%58%45%63%
Suggests DIY first5%0%0%95%
Names specific providers8%23%13%73%
Gives price or cost info5%18%18%75%
Tells to check reviews10%13%0%80%
Tells to verify credentials23%35%8%60%
Mentions case studies / portfolio18%18%0%70%
Mentions local proximity13%13%5%80%
Gives selection criteria30%55%20%38%
Warns about red flags8%15%10%75%
Asks a clarifying question45%70%0%13%
Recommends multiple quotes0%13%0%88%

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 translators buyer to a professional (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.

The divergence index for this study is 21.8 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a translators buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 70% (Claude) — a 70-point spread.
  • Gives selection criteria: from 20% (Gemini) to 55% (Claude) — a 35-point spread.
  • Recommends hiring a professional: from 45% (Gemini) to 72.5% (ChatGPT) — a 28-point spread.
  • Tells the buyer to verify credentials: from 7.5% (Gemini) to 35% (Claude) — a 28-point spread.
  • Mentions case studies or portfolio: from 0% (Gemini) to 17.5% (ChatGPT) — a 18-point spread.

The widest single gap — asks a clarifying question, 70 points — means a translators 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 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%) — a 28-point spread.
  • Asks a clarifying question: 38.3% on average (ChatGPT 45%, Claude 70%, Gemini 0%) — a 70-point spread.
  • Gives selection criteria: 35% on average (ChatGPT 30%, Claude 55%, Gemini 20%) — a 35-point spread.
  • Tells the buyer to verify credentials: 21.7% on average (ChatGPT 22.5%, Claude 35%, Gemini 7.5%) — a 28-point spread.
  • Names a specific provider: 14.2% on average (ChatGPT 7.5%, Claude 22.5%, Gemini 12.5%) — a 15-point spread.
  • Gives price or cost information: 13.3% on average (ChatGPT 5%, Claude 17.5%, Gemini 17.5%) — a 13-point spread.
  • Mentions case studies or portfolio: 11.7% on average (ChatGPT 17.5%, Claude 17.5%, Gemini 0%) — a 18-point spread.
  • Warns about red flags or scams: 10.8% on average (ChatGPT 7.5%, Claude 15%, Gemini 10%) — a 8-point spread.
  • Mentions local proximity: 10% on average (ChatGPT 12.5%, Claude 12.5%, Gemini 5%) — a 8-point spread.
  • Tells the buyer to check reviews: 7.5% on average (ChatGPT 10%, Claude 12.5%, Gemini 0%) — a 13-point spread.
  • Recommends multiple quotes: 4.2% on average (ChatGPT 0%, Claude 12.5%, Gemini 0%) — a 13-point spread.
  • Suggests a DIY approach first: 1.7% on average (ChatGPT 5%, Claude 0%, Gemini 0%) — a 5-point spread.

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 — 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 — 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 were drawn from real translators (professional 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 translators 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 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 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 →