The confusion is understandable: “LLM mentions” and “LLMention” share the words, and both products publish a number that looks like visibility. But they measure opposite ends of the same pipeline: this site measures whether a page can be cited, and DataForSEO measures whether a brand is mentioned.
The two are unaffiliated. This site is not a partner, reseller or front end for DataForSEO, and there is no data relationship or shared operator between them. Similar vocabulary in two product names is not a partnership, and neither product replaces the other.
What does DataForSEO’s LLM Mentions API do?
As described on their site, accessed 23 October 2026, it provides data on keyword, brand and website mentions in LLMs, and it is positioned for tracking brand visibility in AI answers. The quotation is theirs, reproduced exactly:
“LLM Mentions API provides data on keyword, brand and website mentions in LLMs, including metrics like LLM chat mentions, sources and AI search volume.” — DataForSEO, LLM Mentions API overview
Their product page lists what that breaks into: Search Mentions for how and in what context a keyword or domain appears in LLM responses; Target Metrics that aggregate mention counts by location, language, LLM and domain; a multi-target variant for comparing brands side by side; Top Mentioned Domains, Pages and Brands; and historical timeseries, including new and lost mentions, across Google’s AI Overviews and ChatGPT with data going back to 2025. Lite endpoints return the same counts and AI search volume without the heavier breakdowns. Their public pages also include a free visibility trend tracker at llm.dataforseo.com, separate from the API.
What does LLMention measure instead?
This scanner fetches one URL the way a crawler would and scores the response against 40 published checks across 12 weighted dimensions. It reads robots.txt, llms.txt and the sitemap; it parses the HTML for entity markup, heading structure and extractable facts; and it reports every failed check with the evidence that produced it. It never queries a model.
The weights describe what makes a page citable. AI crawler access is 16% of the score because a page that cannot be fetched cannot be read at all. Content depth is 11% and citability is 11%, the dimension the published research supports most directly. The AI context file is 5%, and delivery is the smallest dimension at 2%: a real check, deliberately minor. None of those percentages says anything about whether an engine will name you.
What is different between the two?
The table states the five differences that decide which one a reader needs: the question, the data source, the unit of result, the blind spot, and the moment each is the right tool.
| Dimension | LLMention GEO Scanner | DataForSEO’s LLM Mentions API |
|---|---|---|
| The question each answers | Can a crawler reach this page, parse it, and find what an answer would need to quote? | Is this brand named in AI answers, how often, and in what context? |
| Data source | Your pages, fetched once, plus robots.txt, llms.txt and the sitemap. The response is scored against published rules. | A database of LLM prompts and responses, as their site describes it, queried by keyword, domain or brand. |
| Unit of result | A score out of 100, a grade, and the list of failed checks with the evidence each one saw. | Mention counts and AI search volume, grouped by location, language, LLM and domain, with historical series. |
| What each cannot see | Whether any engine mentions or cites you. It measures readiness, not answers. | Whether the cited page is yours, why a mention appeared, or whether pages that are never cited were crawled at all. |
| When each is the right tool | When you control the pages, have no evidence of citations yet, and want a list of edits rather than a trend line. | When the question is your presence in answers, a competitor comparison, or how that presence moved over time. |
When is each the right tool?
The choice follows from which end of the pipeline you are looking at, and it is possible to need both.
- Choose the readiness audit when the page is yours and you do not yet know whether a crawler can read it. Its output is a list of edits, and its rules are published so a result can be argued with.
- Choose mention tracking when you need to know whether your brand appears in AI answers, how that compares with a competitor’s, or whether it is rising or falling. No amount of page markup answers that.
- Choose both when what changed is the question. The audit tells you whether a change to the page was worth making; mention data tells you whether the change was followed by anything.
- Choose neither for conventional rankings. Rank tracking and server-log analytics measure different surfaces; the comparison hub puts all four side by side.
Can one of these replace the other?
No, and the reason is structural rather than commercial. Readiness is an input to being cited, not a measurement of it. A mention can occur without any crawl of your page: an answer can name a brand it read from a review site, a news article or a competitor’s comparison.
They are not substitutes, so this page does not rank them. A winner would be an artefact of the question rather than a fact about the products.
Where does the description of DataForSEO come from?
Every claim about their product is condensed from the sources below, read on 23 October 2026, and the quotation above is their own sentence. No price, and no ranking claimed by either side, is reproduced: those change without a code diff. The numbers about this site’s method are imported from lib/geo/catalog.ts rather than typed, so they cannot drift from the rules the scanner runs.
- DataForSEO, LLM Mentions API overview — read 23 October 2026; the quotation above is from this page
- DataForSEO, LLM Mentions API product page — read 23 October 2026; the endpoint list and data sources below are condensed from it
- DataForSEO, AI visibility trend tracker — read 23 October 2026; their free category tracker, separate from the API
- Generative Engine Optimization (KDD 2024) — the measurement the readiness weighting follows
- The rule set and its weights — every check, its weight and its pass condition