What is the difference between LLM Mentions and LLM Mentions Lite endpoints?
The LLM Mentions API offers two types of endpoints for retrieving aggregated mention metrics – Standard and Lite. Both endpoint types return the same underlying data, but their response structures differ. Choosing between them affects how much data you get back per request and how much work your application has to do to parse the response. In this guide, we’ll cover the structural differences between Standard and Lite endpoints and explain when to use each.
LLM Mentions and LLM Mentions Lite: key differences
The differences between Standard and Lite LLM Mentions endpoints is best explained with a practical example. Let’s send a request with the identical payload to the Top Mentioned Domains and Top Mentioned Domains Lite endpoints and see the results.
Request example:
[
{
"target": [
{
"keyword": "auto",
"search_scope": [
"question"
]
},
{
"keyword": "audi",
"search_scope": [
"answer"
]
}
],
"platform": "google",
"location": 2840,
"limit": 2
}
]
Top Mentioned Domains response example:
{
"version": "0.1.20260610",
"status_code": 20000,
"status_message": "Ok.",
"time": "4.3659 sec.",
"cost": 0.102,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "06181451-1535-0653-0000-87571d2a57ea",
"status_code": 20000,
"status_message": "Ok.",
"time": "4.3543 sec.",
"cost": 0.102,
"result_count": 1,
"path": [
"v3",
"ai_optimization",
"llm_mentions",
"top_mentioned_domains",
"live"
],
"data": {
"api": "ai_optimization",
"function": "top_mentioned_domains",
"target": [
{
"keyword": "auto",
"search_scope": [
"question"
]
},
{
"keyword": "audi",
"search_scope": [
"answer"
]
}
],
"platform": "google",
"limit": 2
},
"result": [
{
"total_count": 8030,
"offset": 0,
"items_count": 2,
"aggregated_metrics": {
"location": [
{
"key": 2276,
"mentions": 12037,
"ai_search_volume": 4968530
},
{
"key": 2840,
"mentions": 6975,
"ai_search_volume": 8452360
},
{
"key": 2528,
"mentions": 2371,
"ai_search_volume": 699960
},
{
"key": 2826,
"mentions": 1387,
"ai_search_volume": 2236630
},
{
"key": 2040,
"mentions": 1097,
"ai_search_volume": 252840
}
],
"language": [
{
"key": "de",
"mentions": 14012,
"ai_search_volume": 5453300
},
{
"key": "en",
"mentions": 10893,
"ai_search_volume": 12782540
},
{
"key": "nl",
"mentions": 2976,
"ai_search_volume": 914090
},
{
"key": "pl",
"mentions": 752,
"ai_search_volume": 422000
},
{
"key": "fr",
"mentions": 313,
"ai_search_volume": 183970
}
],
"platform": [
{
"key": "google",
"mentions": 30128,
"ai_search_volume": 21638010
}
],
"sources_domain": [
{
"key": "www.youtube.com",
"mentions": 12375,
"ai_search_volume": 9726720
},
{
"key": "www.adac.de",
"mentions": 4274,
"ai_search_volume": 2307610
},
{
"key": "www.autohero.com",
"mentions": 2969,
"ai_search_volume": 1175520
},
{
"key": "en.wikipedia.org",
"mentions": 2382,
"ai_search_volume": 4903120
},
{
"key": "www.carwow.de",
"mentions": 2236,
"ai_search_volume": 848680
}
],
"search_results_domain": [],
"brand_entities_title": [],
"brand_entities_category": [],
"total": {
"mentions": 30128,
"ai_search_volume": 21638010
}
},
"items": [
{
"domain": "www.youtube.com",
"location": [
{
"key": 2276,
"mentions": 1081,
"ai_search_volume": 410150
},
{
"key": 2840,
"mentions": 364,
"ai_search_volume": 571600
},
{
"key": 2826,
"mentions": 81,
"ai_search_volume": 121890
},
{
"key": 2040,
"mentions": 79,
"ai_search_volume": 18080
},
{
"key": 2756,
"mentions": 70,
"ai_search_volume": 14490
}
],
"language": [
{
"key": "de",
"mentions": 1213,
"ai_search_volume": 441690
},
{
"key": "en",
"mentions": 590,
"ai_search_volume": 851140
},
{
"key": "nl",
"mentions": 37,
"ai_search_volume": 10250
},
{
"key": "pl",
"mentions": 17,
"ai_search_volume": 7320
},
{
"key": "fr",
"mentions": 13,
"ai_search_volume": 6430
}
],
"platform": [
{
"key": "google",
"mentions": 1923,
"ai_search_volume": 1458600
}
],
"sources_domain": [
{
"key": "www.youtube.com",
"mentions": 1923,
"ai_search_volume": 1458600
},
{
"key": "www.adac.de",
"mentions": 380,
"ai_search_volume": 197120
},
{
"key": "www.reddit.com",
"mentions": 278,
"ai_search_volume": 109080
},
{
"key": "www.carwow.de",
"mentions": 247,
"ai_search_volume": 112960
},
{
"key": "www.autohero.com",
"mentions": 241,
"ai_search_volume": 79190
}
],
"search_results_domain": [],
"brand_entities_title": [],
"brand_entities_category": [],
"total": {
"mentions": 1923,
"ai_search_volume": 1458600
}
},
{
"domain": "www.adac.de",
"location": [
{
"key": 2276,
"mentions": 548,
"ai_search_volume": 297540
},
{
"key": 2756,
"mentions": 36,
"ai_search_volume": 7140
},
{
"key": 2040,
"mentions": 34,
"ai_search_volume": 8840
},
{
"key": 2056,
"mentions": 3,
"ai_search_volume": 3520
},
{
"key": 2380,
"mentions": 1,
"ai_search_volume": 110
}
],
"language": [
{
"key": "de",
"mentions": 608,
"ai_search_volume": 314030
},
{
"key": "en",
"mentions": 8,
"ai_search_volume": 570
},
{
"key": "it",
"mentions": 4,
"ai_search_volume": 1230
},
{
"key": "fr",
"mentions": 2,
"ai_search_volume": 1320
}
],
"platform": [
{
"key": "google",
"mentions": 622,
"ai_search_volume": 317150
}
],
"sources_domain": [
{
"key": "www.adac.de",
"mentions": 622,
"ai_search_volume": 317150
},
{
"key": "www.youtube.com",
"mentions": 380,
"ai_search_volume": 197120
},
{
"key": "www.carwow.de",
"mentions": 181,
"ai_search_volume": 93880
},
{
"key": "www.autohero.com",
"mentions": 175,
"ai_search_volume": 62740
},
{
"key": "www.meinauto.de",
"mentions": 173,
"ai_search_volume": 101320
}
],
"search_results_domain": [],
"brand_entities_title": [],
"brand_entities_category": [],
"total": {
"mentions": 622,
"ai_search_volume": 317150
}
}
]
}
]
}
]
}
Top Mentioned Domains Lite response example:
{
"version": "0.1.20260610",
"status_code": 20000,
"status_message": "Ok.",
"time": "2.3857 sec.",
"cost": 0.102,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "06181450-1535-0658-0000-505a1e5fb78e",
"status_code": 20000,
"status_message": "Ok.",
"time": "2.3731 sec.",
"cost": 0.102,
"result_count": 1,
"path": [
"v3",
"ai_optimization",
"llm_mentions",
"top_mentioned_domains_lite",
"live"
],
"data": {
"api": "ai_optimization",
"function": "top_mentioned_domains_lite",
"target": [
{
"keyword": "auto",
"search_scope": [
"question"
]
},
{
"keyword": "audi",
"search_scope": [
"answer"
]
}
],
"platform": "google",
"limit": 2
},
"result": [
{
"total_count": 10409,
"offset": 0,
"items_count": 2,
"aggregated_metrics": null,
"items": [
{
"domain": "www.youtube.com",
"location": 2276,
"language": "de",
"platform": "google",
"metrics": {
"mentions": 1081,
"ai_search_volume": 410150
}
},
{
"domain": "www.adac.de",
"location": 2276,
"language": "de",
"platform": "google",
"metrics": {
"mentions": 548,
"ai_search_volume": 297540
}
}
]
}
]
}
]
}
With both results on display, we can easily identify the differences between Standard and Lite endpoint types:
- Standard endpoints return data in a nested, grouped structure. Each item includes the primary identifier (relevant
domain,page,brand,brand_category) with separate arrays breaking down the metrics by various dimensions, such aslocation,platform,sources_domain,brand_entities_category, and more. Besides, each item contains thetotalobject with the total metrics count for the primary identifier.The response also includes a top-level
aggregated_metricsobject with the same dimensional breakdowns rolled up across all items. This object aggregates mention metrics from all found domains, pages, brands – depending on the endpoint you use. - Lite endpoints return data in a flat, single-level structure. Each item contains only the primary identifier (e.g.,
domain), a singlelocation,language, andplatformidentifiers, and a singlemetricsobject withmentionsandai_search_volumemetrics. The response doesn’t include the top-levelaggregated_metricsobject or the internal arrays.
In a nutshell, the Standard endpoints return a multi-dimensional breakdown of the metrics returned per item in the response. That means, for each top-mentioned domain, you get a breakdown of the sources, search results, and brands that appear alongside it. The Lite endpoints return data in a flattened format, providing a simplified overview of metrics returned per item of the response. In this case, for each top mentioned domain you get only the total number of mentions and the current AI search volume rate.
It’s important to note that the underlying data is identical between the two endpoint types. The Lite endpoints don’t return results from a sampled or reduced dataset. The data returns from the same extensive dataset as the Standard endpoints use, but in a simpler shape.
Use cases for Standard and Lite LLM Mentions endpoints
Given their differences, LLM Mentions Standard and Lite endpoints suit different solutions. Let’s explain some possible use cases for each endpoint type separately.
1 With a detailed breakdown of mentions data by specific categories, Standard endpoints provide more insights into the target’s mention profile. In addition, you get aggregated mentions metrics of all items in the response separately. However, the trade-off is a larger JSON which requires more parsing work on your end. That means…
In this case, use the Standard LLM Mentions endpoints for the following complex processes:
1. Competitive intelligence views that need brand co-mentions or referring-source breakdowns. Each item includes the brand entities, source domains, and search result domains, so you can map a target’s competitive context in a single call rather than several.
2. Pulling cross-target totals in one request. The aggregated_metrics object returns a rollup across all items in the response, which is best for summary widgets, executive reports, or any view that needs both per-item and total figures.
3. Investigating where a target gets cited from. The breakdowns within each item tell you which source domains drove the mentions, which brand categories appeared alongside them, and how the metrics split across other dimensions.
2 For the Lite version of LLM Mentions, the smaller, faster-to-parse responses make data easier to parse by your system. This allows for simpler integration and efficient use in flat data structures such as relational tables and spreadsheets, where each row maps directly to a single record and no flattening logic is required.
The trade-off is no dimensional breakdown inside each item. If you need to know the composition of a mention rather than just its totals, Lite isn’t the right choice.
That makes LLM Mentions Lite suitable for more straightforward integrations and processes:
1. Dashboards or charts that show mentions across geography or platform. Flat rows map directly to chart axes, so you can plot mentions by location or platform without restructuring the data first.
2. Quick prototyping and testing. The flat response is easier to read during development, which speeds up shipping a proof of concept or setting up an early integration.
3. Exports to a spreadsheet, CSV, or BI tool. One row per record means the response is written to a CSV or Google Sheets file without a transformation step.
It’s worth noting that pricing is identical across the Standard and Lite versions of the LLM Mentions endpoints. You’re not paying more or less for either choice – pick the one that fits your solution.
Wrap up
In summary, the core difference between the Standard and Lite versions of the LLM Mentions endpoints lies in the response shape and the coverage of insights.
With Standard endpoints, you get an in-depth breakdown of mentions metrics, relevant to the target. Thus, these endpoints are the best fit for systems and solutions that need to analyze a target’s full mention profile, including breakdowns by source, brand, location, and language. The Lite endpoints refund data in a flattened, simplified response that is easier to parse and integrate. That makes Lite endpoints great for feeding flat data into dashboards and spreadsheets where integration simplicity is the priority.
See the LLM Mentions docs for all the details, and don’t hesitate to contact our 24/7 customer support if anything is unclear.