DataForSEO LLM Mentions API: Endpoints, Fields and Real Cost per Query

The DataForSEO LLM Mentions API searches a stored database of ChatGPT and Google AI Overview answers for mentions of domains and keywords. Search Mentions returns the original question, the answer text, cited sources and an estimate of AI search volume. It does not send a new prompt to a chatbot. The published rate is $0.1 per task plus $0.001 per returned row, so one request returning 1,000 rows costs $1.10. The sections below cover endpoints, fields, a Python request and worked budgets.

This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article. An AI assistant helped me draft it. Prices and limits are quoted from DataForSEO's pricing page and API documentation, checked 1 November 2026, and listed at the end.

What problem it solves

A brand can be named in an AI answer, cited as a source, both or neither. Those are different outcomes, and folding them into one "AI visibility" number makes a report less useful.

Three ways to investigate:

1. Send prompts directly to models. Full control of the questions, plus model call costs and sampling work.
2. Use an AI visibility dashboard. Less development work; metrics and raw data access depend on the product.
3. Search a database of existing answers. Retrieve stored records that match a domain or keyword.

The LLM Mentions API takes the third route. Each record shows the question behind an answer, the cited pages and a demand estimate for that question. The tradeoff: the questions are already stored, so they can be filtered but not replaced with a new prompt. That job belongs to a responses or scraper style API.

How big is the stored set?

The pricing page describes more than 280M real prompts and answers, with continuous updates and a 60 day full update cycle. The live counters on the product page showed a different figure on the same check: 420,701,746 prompts, made up of 30,625,875 from ChatGPT and 390,075,871 from Google AI Overviews. Treat both as vendor figures that change. On those counters, ChatGPT records are about 7% of the total, so a ChatGPT only report draws on a much smaller pool.

Endpoints at a glance

DataForSEO's product page and API documentation divide the work into these endpoints:

Endpoint	What it returns	When it is useful
Search Mentions	Matching questions, answers, sources and volume estimates	Reading the answers behind a mention
Target Metrics	Aggregated mentions and AI search volume for one target definition	A headline visibility figure
Multi-Target Metrics	Aggregated metrics for several targets	Comparing a brand with competitors
Top Mentioned Domains and Pages	Frequently cited domains and pages	Finding which sources repeatedly appear
Top Mentioned Brands and Brand Categories	Brands and categories in the dataset	Spotting other names in the same conversation
Historical, Timeseries Delta, Timeseries New and Lost	Mention data over time	Trends and gained or lost mentions

Lite versions return the same data in a flatter format at the same price, according to the pricing page.

The product page says the Historical endpoint has data going back to 2025. Its FAQ names three endpoints with historical data and, answering a question about the "cut-off date", says the data "is available up to 2025-08-01." That could mean a start date or an end date, so check the earliest and latest month a historical call returns before building a trend line.

DataForSEO LLM Mentions API pricing, worked through

The LLM Mentions pricing page lists one task charge plus one row charge for all endpoints under /ai_optimization/llm_mentions/.

Item	Price or service detail
Per task	$0.1
Per row returned	$0.001
1,000 rows in one request	1 × 0.1 + 1,000 × 0.001 = $1.10
1M rows in 1,000 requests	1,000 × 0.1 + 1,000,000 × 0.001 = $1,100
Mode	Live only, no Standard queue
Turnaround stated	Up to 2 seconds on average

The pricing page defines a row as an object containing data for a single domain or keyword mention. The Search Mentions documentation sample sets limit to 3 and reports a cost of 0.103: the $0.1 task fee plus three rows at $0.001. The $1,100 line assumes 1,000 requests; it is not a row only price.

The task fee is fixed, but the final charge depends on the billed rows returned. The Target Metrics documentation sample reports 0.101, which matches the task fee plus one row. That sample returns aggregates with items_count 0, and the documentation does not spell out how nested groups map to billed rows. So 0.101 is a documented example, not a guaranteed price per call. The budgets below use the sample row counts; the cost field in each response shows the real charge.

The pricing page says its average turnaround can vary with load and task complexity, and the Search Mentions documentation allows up to 120 seconds of execution. Set job timeouts against the second figure.

Sizing a pull before paying for it

The Search Mentions sample also reports total_count 11,859 next to items_count 3. The documentation defines total_count as the total amount of results relevant to the request, so a small request can size the full match set first. As an inference from the published rates, retrieving all 11,859 rows at 1,000 per request takes 12 requests: 12 × 0.1 + 11,859 × 0.001 = $1.20 + $11.86 = $13.06. The documentation suggests search_after_token to avoid timeouts beyond 1,000 results.

Example 1: a weekly visibility number for five brands

A report covers one brand and four competitors, with one Target Metrics call per brand each week.

• 5 brands × 4 weeks = 20 calls
• 20 × $0.101 = $2.02 for four weeks

This assumes the sample's one billed row per call; it is not a verified bill.

Example 2: monthly answer review

Each of those five brands also gets a monthly Search Mentions review. If each request returns the full 1,000 rows:

• 5 × (0.1 + 1,000 × 0.001) = 5 × $1.10 = $5.50

A limit of 1,000 is a cap, not a promise of 1,000 matches; fewer rows cost less. With the Example 1 assumption unchanged, the two workloads total $2.02 + $5.50 = $7.52 for four weeks, before any top domain or historical pulls.

Example 3: a first test

DataForSEO's account pricing page lists a $1 trial credit available to new accounts. At the sample charges, that covers nine Target Metrics calls ($0.909), or one Search Mentions call returning 900 rows (0.1 + 900 × 0.001 = $1.00).

Paid use starts with a $50 minimum top up, and the balance does not expire. The same page lists daily spending limits for the account or individual APIs. The 1 July 2026 pricing update removed the former $100 monthly commitment for the LLM Mentions and Backlinks APIs.

Request fields that matter

These come from the Search Mentions Live documentation.

• Endpoint: POST https://api.dataforseo.com/v3/ai_optimization/llm_mentions/search_mentions/live, one task per call, up to 10,000 calls a minute.
• target: up to 10 entities, each a domain or a keyword. search_filter sets include or exclude, and at least one must be an include. A domain omits https:// and www., and include_subdomains defaults to false.
• search_scope: for domains, any, sources or search_results (ChatGPT only). For keywords, any, question, answer, brand_entities or fan_out_queries.
• match_type: word_match by default, matching the term as a whole word; partial_match also finds it inside longer words.
• platform: chat_gpt or google; leave it out to include both.
• location_code and language_code: default to 2840 (United States) and English. ChatGPT data is limited to the United States and English.
• filters: up to 8. order_by: up to 3 rules.
• limit: default 100, maximum 1,000. offset: up to 1,000,000, with search_after_token beyond that.

A brand name in answer is a different event from a domain in sources, so pick the event the report measures first.

Why a brand can come back with no match

A missing match establishes only that this query returned no record. Before reporting a brand as absent, check the settings: a domain target skips subdomains unless include_subdomains is true, word_match misses the term inside longer words, ChatGPT has no documented coverage outside the United States and English, and the stored prompt set may not contain the questions where the brand appears.

Response fields that matter

Field	Meaning	Platform notes
platform, model_name	The AI surface and model	Google items use google_ai_overview
question	The stored prompt	Both
answer	Answer text in Markdown	Both
sources	Sources the model cited or relied on in its final answer	Both
search_results	All web results the model retrieved, including duplicates and unused entries	ChatGPT only
ai_search_volume, monthly_searches	Demand estimates for the question	Both
first_response_at, last_response_at	First recorded and last updated, in UTC	Both
brand_entities	Brands mentioned in the answer	ChatGPT only
fan_out_queries	Related queries derived from the main query	ChatGPT only

Keep three questions apart. Was the brand mentioned in the answer? Was its website cited in sources? Did its pages only appear in search_results? That last field includes unused entries, so appearing there is not a citation, and counting all three together overstates what a brand achieved.

AI search volume estimates demand for AI searches and prompts; it is not website visits or sales.

Target Metrics returns aggregated_metrics grouped by location, language, platform and cited source domain, adds search result domain and brand entity groupings for ChatGPT, and returns a total. internal_list_limit (1 to 10, default 10) caps the sources_domain and search_results_domain lists, and initial_dataset_filters filters records before aggregation.

A first request in Python

This wraps the request body from DataForSEO's Search Mentions documentation in requests. Untested: the snippet has not been run against an API account. It reads DFS_LOGIN and DFS_PASSWORD from environment variables rather than embedding credentials.

import os
# Untested; request body taken from the Search Mentions Live docs example.
import requests

URL = "https://api.dataforseo.com/v3/ai_optimization/llm_mentions/search_mentions/live"
AUTH = (os.environ["DFS_LOGIN"], os.environ["DFS_PASSWORD"])

payload = [{
    "language_name": "English",
    "location_code": 2840,
    "target": [
        {"domain": "dataforseo.com", "search_filter": "exclude"},
        {"keyword": "bmw", "search_scope": ["answer"]},
    ],
    "platform": "google",
    "filters": [ ["ai_search_volume", ">", 1000] ],
    "order_by": ["ai_search_volume,desc"],
    "limit": 3,
}]

r = requests.post(URL, auth=AUTH, json=payload)
r.raise_for_status()
task = r.json()["tasks"][0]
print("cost:", task["cost"])          # expected 0.1 + 3 x 0.001
for item in task["result"][0]["items"]:
    cited = [s["domain"] for s in item.get("sources") or []]
    print(item["ai_search_volume"], item["question"], cited[:5])

The request asks for Google AI Overview answers containing "bmw", excludes dataforseo.com, keeps questions with ai_search_volume above 1,000 and caps the result at three items. The cost comment assumes three rows come back, which limit does not guarantee. For a scheduled job, add a timeout, check the task status and handle empty results.

How it compares with other approaches

Approach	Where it wins	What you give up
DataForSEO LLM Mentions API	Existing questions, answers, citations, volume estimates and data for tools you build	No custom prompts; ChatGPT is US English only, and Google coverage is AI Overviews
Prompting models yourself, for example DataForSEO's LLM Responses API, from $0.0002 per task plus LLM cost for ChatGPT, Claude, Gemini and Perplexity	Exact prompts and a wider choice of models	The prompt set, demand context and sampling need a separate plan
SE Ranking AI Search API, part of its credit based Data API	Fits teams already in its wider SEO suite	A different data model and pricing unit that needs a like for like comparison
Dashboard tools	Ready reporting without building an interface	Raw field access and exports depend on the tool

For a client report on which existing questions mention a brand and which sources get cited, the Mentions API matches the task. For a controlled check of how a model answers a specific question now, prompt directly. When the buyer mainly needs an interface, a dashboard is the more practical purchase.

A repeatable report needs consistent settings: the same platform, location, language, match type, target and scope between comparable pulls, with timestamps read against the 60 day update cycle. If a report says "share of voice", define it, for example as one brand's mentions divided by all mentions in the comparison set under the same settings.

DataForSEO's product page allows the data in GEO tools built and sold to customers, with specifics in its Terms of Service.

To run the Python request above, it needs API credentials from [a DataForSEO account](https://try.dataforseo.com/tq5b44jba11a Use), set as DFS_LOGIN and DFS_PASSWORD.

FAQ

Does the LLM Mentions API query ChatGPT live?

No. "Live" is the request mode; the endpoints search the stored database.

How fresh is the data?

The pricing page describes continuous updates and a 60 day full update cycle. Read each answer with its timestamps; a stored result is not proof the same answer would appear in a new chat today.

How can the cost be kept under control?

Size the match set with a small request, set limit deliberately and budget for both tasks and rows. Use Target Metrics when an aggregate is enough and Search Mentions when the report needs answer text or cited URLs. Daily spending limits help cap the spend.

Sources

Fetched on the date in the disclosure.

Fact used	Source
Task and row prices, row definition, $1.10 and $1,100 examples, Live only, 2 second average, 280M+ prompts, 60 day cycle, Lite same price	https://dataforseo.com/pricing/ai-optimization/llm-mentions
LLM Responses API from $0.0002 per task plus LLM cost, models covered	https://dataforseo.com/pricing/ai-optimization
Endpoints, live prompt counters, historical wording, AI search volume definition, use in GEO tools	https://dataforseo.com/apis/ai-optimization-api/llm-mentions-api
$1 trial credit for new accounts, $50 minimum top up, balance never expires, daily spending limits	https://dataforseo.com/pricing
$100 monthly commitment removed, 1 July 2026	https://dataforseo.com/update/pricing-update-in-dataforseo-apis
Search Mentions limits, 120 seconds, fields, example body, sample cost 0.103, total_count 11,859	https://docs.dataforseo.com/v3/ai_optimization/llm_mentions/search_mentions/live/
Target Metrics groupings, internal_list_limit, initial_dataset_filters, sample cost 0.101	https://docs.dataforseo.com/v3/ai_optimization/llm_mentions/target_metrics/live/
Endpoint names	https://docs.dataforseo.com/v3/ai_optimization/llm_mentions/overview/
SE Ranking AI Search API in a credit based Data API	https://seranking.com/api-pricing.html
Example totals, ChatGPT share, full pull estimate	Arithmetic using the figures above