LLM Scraper vs LLM Responses: How to Choose the Right AI Data Solution
AI search evolves rapidly, attracting more and more users as an effective way to look up information. According to Peec AI, ChatGPT’s search engine market share was between 4% and 12% of Google’s, as of autumn 2025. Today, ChatGPT’s is likely even higher. What was a handy tool for daily tasks is now an emerging search channel.
LLM developers constantly update model behaviors and introduce new search features that you need to analyze and track. To keep your Generative Engine Optimization strategies and tracking tools up to date, a steady supply of LLM insights is essential.
At DataForSEO, we faced the growing demand for AI data head-on and introduced two solutions: LLM Scraper and LLM Responses endpoints of AI Optimization API. Both are powerful tools for extracting structured and up-to-date LLM data. Despite their similarities, LLM Scraper and LLM Responses are designed for different purposes. We will demonstrate it using two real-world use cases that reflect the key processes of the GEO optimization.
Contents:
Use case 1: Assessing & tracking brand and content visibility in LLMs
1. Why does it matter?
2. What data do you need?
3. The solution: LLM Scraper
4. Pricing and cost breakdown
Use case 2: Analyzing LLM search behavior
1. Why does it matter?
2. What data (and functions) do you need?
3. The solution: LLM Responses
4. Pricing and cost breakdown
Conclusion
Use case 1: Assessing & tracking brand and content visibility in LLMs
Analyzing whether your brand and content (and competitors’) are shown in LLMs, and how prominently, is one of the core processes in Generative Engine Optimization. This analysis explores all possible types of LLM response features for brand mentions, how those mentions are incorporated into the responses, and which queries to target to make your content visible. Let’s explain why this process matters and what data you will need to achieve success.
1 Why does it matter?
Until recently, LLM responses were generic, plain-text summaries that rarely included in-text mentions of brands, pulled from the model’s memory. They gave users a general explanation of the products or services, which was usually outdated. Now, LLMs respond to users like real shopping or travel guides, comparing products in tables and displaying actual pricing details in real-time. Look, for example, at the ChatGPT response below.
In response to a simple query, ChatGPT provides a list of relevant products, including interactive product cards containing detailed information. This is just one example of how ChatGPT showcases products and brands. It can mention your or a competitor’s brand as a direct citation, in a table, or in a local business feature. Given the robustness of new features in responses, it is important to appear prominently in these features and track visibility changes. The more you appear in LLMs, the higher the chances your brand will be more recognizable by the target audience.
However, there is no traditional 1-10 ranking system in AI search, and brand mentions are integrated within response elements. Besides, LLM providers don’t grant public access to brand mentions or traffic data, rendering traditional tracking methods obsolete. Thus, collecting and analyzing data directly from LLM answers is one of the most viable ways to evaluate and track your AI search performance.
Additionally, analyzing LLM responses will help identify new keywords or keyphrases for content optimization. You can explore how LLMs structure content and which keywords they find most relevant for describing a product or service. Then, you can use new keywords to create new LLM-ready content or optimize existing publications.
You can also discover where and how prominently your competitors are cited and displayed in AI-generated responses. By analyzing competitors’ content quoted by LLMs, you can assess what makes them stand out to AI search and gain additional ideas for improving your own content. In some cases, you can even spot absolutely new competitors that are not visible in the traditional search, but highly popular in LLMs.
Finally, by exploring how LLM answers to different queries, you can identify possible content and visibility gaps, where your brand should appear, but doesn’t. LLMs favor content that covers the target topic in depth and addresses all the user’s needs. The analysis of a wide range of LLM responses and target queries will demonstrate in which contexts your content appears most frequently, and where you should optimize or expand your content.
Assessing and tracking brand visibility in LLM responses is the cornerstone of AI search optimization. However, it relies heavily on highly structured, granular data from LLM responses that cover a broad range of visibility-tracking and optimization aspects.
2 What data do you need?
For data-driven brand visibility tracking and content analysis in LLMs, you’ll need to pull a large volume of diverse data from AI-generated responses.
1. To understand how a brand is represented in search engines and track visibility changes, you need to identify its in-text mentions and collect source citations inside the LLM responses. Collect brand keywords and relevant links. Then, track how often and where the relevant keywords and URLs appear in responses for target queries. Also, explore and track mentions across additional features, such as tables or product elements, to cover all aspects of tracking.
2. To find additional keyword ideas, you’ll need to pull clear text snippets from AI-answers, including paragraphs, lists, product descriptions, and headings. Then, analyze which keywords AI frequently utilizes to describe the topic, and in which content sections such keywords appear the most. Fetching fan-out queries is also beneficial, as they act as semantic extensions of the original query and reveal how LLMs interpret and expand a topic.
3. To research and track competitors, analyze all the quoted websites, text snippets associated with competitors, product and business listings – everything related to your industry rivals. Besides, you’ll need detailed metadata on each response element where your competitors appear – not only URLs, but also titles, descriptions, and product IDs. This data will help you understand which types of competitors’ content, offers, and products get the most attention from LLMs.
4. Exploring visibility and content gaps can be done by assessing how prominently the brand is represented in LLM answers by different target queries on the same topic. To accomplish this, you’ll need the fan-out queries data that highlights subtopics of your target topic. You can also check whether the LLM responses to fan-out queries include links to your content or brand mentions. Additionally, it is important to evaluate the context and structure of responses to understand how the LLM perceives your brand as a whole.
You’ll need the most comprehensive and detailed data from LLM responses. It must include information from every response element, whether it’s a text paragraph or an image gallery. That data must represent what LLMs actually display to a user and in what format. Such a dataset must include generated responses for thousands of t target queries. Moreover, the dataset must be regularly updated or provide direct access to live data for analyzing real-time changes.
3 The solution: LLM Scraper
The best solution for this scope of work and data requirements is the DataForSEO LLM Scraper. This API is the best for analyzing and tracking brand visibility in LLMs, as it directly scrapes their web interfaces. That means you’ll get the data that is actually displayed to users in the AI response – every link, table, image gallery with all additional insights. Currently, the LLM Scraper provides data from the two most popular LLMs – ChatGPT and Gemini.
To give you a better understanding of LLM Scraper’s capabilities, let’s look at the example request and the response of the ChatGPT LLM Scraper. Setting a task for this endpoint is easy – just specify the location, language, and the target keyword to get the response for.
POST: https://api.dataforseo.com/v3/ai_optimization/chat_gpt/llm_scraper/live/advanced
Request example:
[
{
"force_web_search": true,
"language_code": "en",
"location_code": 2840,
"keyword": "provide me with variants of cheap smartphones to buy from google shopping"
}
]
Response example:
{
"version": "0.1.20260223",
"status_code": 20000,
"status_message": "Ok.",
"time": "49.1394 sec.",
"cost": 0.004,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "03091716-1535-0629-0000-d433e185d071",
"status_code": 20000,
"status_message": "Ok.",
"time": "49.0849 sec.",
"cost": 0.004,
"result_count": 1,
"path": [
"v3",
"ai_optimization",
"chat_gpt",
"llm_scraper",
"live",
"advanced"
],
"data": {
"api": "ai_optimization",
"function": "llm_scraper",
"se": "chat_gpt",
"force_web_search": true,
"language_code": "en",
"location_code": 2840,
"keyword": "provide me with variants of cheap smartphones to buy from google shopping",
"device": "desktop",
"os": "windows"
},
"result": [
{
"keyword": "provide me with variants of cheap smartphones to buy from google shopping",
"location_code": 2840,
"language_code": "en",
"model": "gpt-5-3",
"check_url": "https://chatgpt.com/?prompt=provide%20me%20with%20variants%20of%20cheap%20smartphones%20to%20by%20from%20google%20shopping&hints=search",
"datetime": "2026-03-09 15:16:50 +00:00",
"markdown": "Here are some **cheap smartphone variants you can buy (commonly listed on Google Shopping)**. I focused on **budget models roughly $30–$180** that are widely available online.\n\n---\n\n## 📱 Cheap Smartphones (Popular Budget Picks)\n\n### [Samsung Galaxy A15 5G](https://www.walmart.com/ip/TF-SAMSUNG-S156V-CDMA-LTE-HANDSET-64GB-WALMART-POSA/12014964263?wmlspartner=wlpa&selectedSellerId=0&utm_source=chatgpt.com)\n*$59.88*\n\n### [Motorola Moto G Play (2024)](https://www.motorola.com/us/en/p/phones/moto-g/g-play-gen-3/pmipmgj36mj?pn=PB0C0007US&utm_source=chatgpt.com)\n*$119.99*\n\n### [BLU G75 Smartphone](https://www.microcenter.com/product/702402/blu-g75-unlocked-4g-lte-black-smartphone?utm_source=chatgpt.com)\n*$149.99*\n\n### [Samsung Galaxy A13 5G](https://www.bestbuy.com/product/samsung-pre-owned-excellent-galaxy-a13-5g-64gb-unlocked-black/JX87LRSWK8/sku/6638666?extStoreId=435&utm_source=chatgpt.com)\n*$119.99*\n\n### [BLU View 5 Pro](https://www.walmart.com/ip/TF-BLU-B170D-CDMA-LTE-GRAY-HANDSET-WALMART-POSA/5296361527?wl13=1077&selectedSellerId=0&wmlspartner=wlpa&utm_source=chatgpt.com)\n*$29.88*\n\n### [Oscal C30 Smartphone](https://poshmark.com/listing/BRAND-NEW-SEALED-SMARTPHONES-66ad3e789ceaf7baf9d090bd?utm_medium=feed&utm_source=chatgpt.com)\n*$79.00*\n\n---\n\n## 🔎 Quick Overview of These Budget Phones\n\n### 1️⃣ [Samsung Galaxy A15 5G](https://www.walmart.com/ip/TF-SAMSUNG-S156V-CDMA-LTE-HANDSET-64GB-WALMART-POSA/12014964263?wmlspartner=wlpa&selectedSellerId=0&utm_source=chatgpt.com) \n💲 Often **$40–$180 depending on carrier/condition** \n- 6.5\" display \n- Triple-camera system \n- 5G support \n- Expandable storage \n- Good battery life \n👉 One of the **best cheap Samsung phones** with long software support.\n\n---\n\n### 2️⃣ [Motorola Moto G Play (2024)](https://www.motorola.com/us/en/p/phones/moto-g/g-play-gen-3/pmipmgj36mj?pn=PB0C0007US&utm_source=chatgpt.com) \n💲 Around **$40–$150** \n- Snapdragon 680 processor \n- 6.5\" 90Hz display \n- 50MP camera \n- Solid everyday performance \n👉 Very good for **basic apps, calls, and social media**.\n\n---\n\n### 3️⃣ [BLU G75 Smartphone](https://www.microcenter.com/product/702402/blu-g75-unlocked-4g-lte-black-smartphone?utm_source=chatgpt.com) \n💲 About **$100–$170** \n- 120Hz display \n- Up to **256GB storage + microSD** \n- 50MP camera \n👉 Great value if you want **more storage and smooth screen**.\n\n---\n\n### 4️⃣ [Samsung Galaxy A13 5G](https://www.bestbuy.com/product/samsung-pre-owned-excellent-galaxy-a13-5g-64gb-unlocked-black/JX87LRSWK8/sku/6638666?extStoreId=435&utm_source=chatgpt.com) \n💲 Around **$120–$150** \n- 5G connectivity \n- 5000mAh battery \n- Triple camera \n👉 Reliable **budget 5G phone**.\n\n---\n\n### 5️⃣ [BLU View 5 Pro](https://www.walmart.com/ip/TF-BLU-B170D-CDMA-LTE-GRAY-HANDSET-WALMART-POSA/5296361527?wl13=1077&selectedSellerId=0&wmlspartner=wlpa&utm_source=chatgpt.com) \n💲 About **$30–$80** \n- Very cheap entry-level phone \n- Good for calls, texting, and basic apps \n👉 Best for **ultra-low budgets**.\n\n---\n\n### 6️⃣ [Oscal C30 Smartphone](https://poshmark.com/listing/BRAND-NEW-SEALED-SMARTPHONES-66ad3e789ceaf7baf9d090bd?utm_medium=feed&utm_source=chatgpt.com) \n💲 Around **$70–$90** \n- 6.5\" display \n- Large battery (~5000mAh) \n- Android smartphone \n👉 Budget phone for **basic use**.\n\n---\n\n✅ **Typical cheap smartphone price ranges:**\n\n| Budget | What you get |\n|---|---|\n| $30–$80 | Basic Android phones |\n| $80–$150 | Decent cameras + bigger batteries |\n| $150–$200 | 5G phones with better performance |\n\n\n---\n\n✅ **Good brands to check on Google Shopping**\n- Samsung (Galaxy A series)\n- Motorola (Moto G series)\n- Xiaomi / Redmi\n- Nokia\n- BLU\n\n---\n\n💡 If you want, tell me:\n- your **budget**\n- **Android or iPhone**\n- **new or refurbished**\n\n…and I can show you **the best cheap phones on Google Shopping right now (with the lowest prices)**. 📱💰",
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],
"product_ids": [
{
"type": "chat_gpt_google_shopping_product",
"ei": "UuSuadCKGZayqtsPjrC_4Q0",
"product_id": null,
"catalog_id": "7596833668883100772",
"gpcid": "16807554110425304326",
"headline_offer_docid": "9108951588644876925",
"image_docid": "2307298460091488768",
"rds": "PC_16807554110425304326|PROD_PC_16807554110425304326",
"query": "cheap+smartphones+under+200+unlocked+android",
"mid": "576462823761771837",
"pvt": "hg",
"uule": "w+CAIQICIhVGFsbGFoYXNzZWUsRmxvcmlkYSxVbml0ZWQgU3RhdGVz",
"gl": "us",
"hl": "en"
}
]
},
{
"type": "chat_gpt_products_element",
"product_id": "15274220373654874926",
"merchants": "Walmart + others",
"id_to_token_map": "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",
"title": "BLU View 5 Pro",
"rating": {
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"value": 2.8,
"votes_count": 406,
"rating_max": 5
},
"price": 29.88,
"currency": "USD",
"tag": null,
"url": "https://www.walmart.com/ip/TF-BLU-B170D-CDMA-LTE-GRAY-HANDSET-WALMART-POSA/5296361527?wl13=1077&selectedSellerId=0&wmlspartner=wlpa&utm_source=chatgpt.com",
"domain": "www.walmart.com",
"images": [
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],
"product_ids": [
{
"type": "chat_gpt_google_shopping_product",
"ei": "UuSuadCKGZayqtsPjrC_4Q0",
"product_id": null,
"catalog_id": "15274220373654874926",
"gpcid": "1792693132998821836",
"headline_offer_docid": "11488513671895083625",
"image_docid": "2336648526022893748",
"rds": "PC_1792693132998821836|PROD_PC_1792693132998821836",
"query": "cheap+smartphones+under+200+unlocked+android",
"mid": "576462842264946612",
"pvt": "hg",
"uule": "w+CAIQICIhVGFsbGFoYXNzZWUsRmxvcmlkYSxVbml0ZWQgU3RhdGVz",
"gl": "us",
"hl": "en"
}
]
},
{
"type": "chat_gpt_products_element",
"product_id": "66ad3e789ceaf7baf9d090bd",
"merchants": "mmposh18",
"id_to_token_map": null,
"title": "Oscal C30 Smartphone",
"rating": null,
"price": 79,
"currency": "USD",
"tag": null,
"url": "https://poshmark.com/listing/BRAND-NEW-SEALED-SMARTPHONES-66ad3e789ceaf7baf9d090bd?utm_medium=feed&utm_source=chatgpt.com",
"domain": "poshmark.com",
"images": [
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],
"product_ids": null
}
]
},
{
"type": "chat_gpt_text",
"rank_group": 2,
"rank_absolute": 3,
"markdown": "\n\n---\n\n## 🔎 Quick Overview of These Budget Phones\n\n### 1️⃣ [Samsung Galaxy A15 5G](https://www.walmart.com/ip/TF-SAMSUNG-S156V-CDMA-LTE-HANDSET-64GB-WALMART-POSA/12014964263?wmlspartner=wlpa&selectedSellerId=0&utm_source=chatgpt.com) \n💲 Often **$40–$180 depending on carrier/condition** \n- 6.5\" display \n- Triple-camera system \n- 5G support \n- Expandable storage \n- Good battery life \n👉 One of the **best cheap Samsung phones** with long software support.\n\n---\n\n### 2️⃣ [Motorola Moto G Play (2024)](https://www.motorola.com/us/en/p/phones/moto-g/g-play-gen-3/pmipmgj36mj?pn=PB0C0007US&utm_source=chatgpt.com) \n💲 Around **$40–$150** \n- Snapdragon 680 processor \n- 6.5\" 90Hz display \n- 50MP camera \n- Solid everyday performance \n👉 Very good for **basic apps, calls, and social media**.\n\n---\n\n### 3️⃣ [BLU G75 Smartphone](https://www.microcenter.com/product/702402/blu-g75-unlocked-4g-lte-black-smartphone?utm_source=chatgpt.com) \n💲 About **$100–$170** \n- 120Hz display \n- Up to **256GB storage + microSD** \n- 50MP camera \n👉 Great value if you want **more storage and smooth screen**.\n\n---\n\n### 4️⃣ [Samsung Galaxy A13 5G](https://www.bestbuy.com/product/samsung-pre-owned-excellent-galaxy-a13-5g-64gb-unlocked-black/JX87LRSWK8/sku/6638666?extStoreId=435&utm_source=chatgpt.com) \n💲 Around **$120–$150** \n- 5G connectivity \n- 5000mAh battery \n- Triple camera \n👉 Reliable **budget 5G phone**.\n\n---\n\n### 5️⃣ [BLU View 5 Pro](https://www.walmart.com/ip/TF-BLU-B170D-CDMA-LTE-GRAY-HANDSET-WALMART-POSA/5296361527?wl13=1077&selectedSellerId=0&wmlspartner=wlpa&utm_source=chatgpt.com) \n💲 About **$30–$80** \n- Very cheap entry-level phone \n- Good for calls, texting, and basic apps \n👉 Best for **ultra-low budgets**.\n\n---\n\n### 6️⃣ [Oscal C30 Smartphone](https://poshmark.com/listing/BRAND-NEW-SEALED-SMARTPHONES-66ad3e789ceaf7baf9d090bd?utm_medium=feed&utm_source=chatgpt.com) \n💲 Around **$70–$90** \n- 6.5\" display \n- Large battery (~5000mAh) \n- Android smartphone \n👉 Budget phone for **basic use**.\n\n---\n\n✅ **Typical cheap smartphone price ranges:**\n\n",
"sources": null,
"brand_entities": [
{
"type": "chat_gpt_brand_entity",
"title": "Samsung Galaxy A15 5G",
"category": "product",
"markdown": "[Samsung Galaxy A15 5G](https://www.walmart.com/ip/TF-SAMSUNG-S156V-CDMA-LTE-HANDSET-64GB-WALMART-POSA/12014964263?wmlspartner=wlpa&selectedSellerId=0&utm_source=chatgpt.com)",
"urls": [
{
"url": "https://www.walmart.com/ip/TF-SAMSUNG-S156V-CDMA-LTE-HANDSET-64GB-WALMART-POSA/12014964263?wmlspartner=wlpa&selectedSellerId=0&utm_source=chatgpt.com",
"domain": "www.walmart.com"
},
{
"url": "https://www.walmart.com/ip/TF-SAMSUNG-S156V-CDMA-LTE-HANDSET-64GB-WALMART-POSA/12014964263?wmlspartner=wlpa&selectedSellerId=0",
"domain": "www.walmart.com"
}
]
},
{
"type": "chat_gpt_brand_entity",
"title": "Motorola Moto G Play (2024)",
"category": "product",
"markdown": "[Motorola Moto G Play (2024)](https://www.motorola.com/us/en/p/phones/moto-g/g-play-gen-3/pmipmgj36mj?pn=PB0C0007US&utm_source=chatgpt.com)",
"urls": [
{
"url": "https://www.motorola.com/us/en/p/phones/moto-g/g-play-gen-3/pmipmgj36mj?pn=PB0C0007US&utm_source=chatgpt.com",
"domain": "www.motorola.com"
},
{
"url": "https://www.motorola.com/us/en/p/phones/moto-g/g-play-gen-3/pmipmgj36mj?pn=PB0C0007US",
"domain": "www.motorola.com"
}
]
},
{
"type": "chat_gpt_brand_entity",
"title": "BLU G75 Smartphone",
"category": "product",
"markdown": "[BLU G75 Smartphone](https://www.microcenter.com/product/702402/blu-g75-unlocked-4g-lte-black-smartphone?utm_source=chatgpt.com)",
"urls": [
{
"url": "https://www.microcenter.com/product/702402/blu-g75-unlocked-4g-lte-black-smartphone?utm_source=chatgpt.com",
"domain": "www.microcenter.com"
},
{
"url": "https://www.microcenter.com/product/702402/blu-g75-unlocked-4g-lte-black-smartphone",
"domain": "www.microcenter.com"
}
]
},
{
"type": "chat_gpt_brand_entity",
"title": "Samsung Galaxy A13 5G",
"category": "product",
"markdown": "[Samsung Galaxy A13 5G](https://www.bestbuy.com/product/samsung-pre-owned-excellent-galaxy-a13-5g-64gb-unlocked-black/JX87LRSWK8/sku/6638666?extStoreId=435&utm_source=chatgpt.com)",
"urls": [
{
"url": "https://www.bestbuy.com/product/samsung-pre-owned-excellent-galaxy-a13-5g-64gb-unlocked-black/JX87LRSWK8/sku/6638666?extStoreId=435&utm_source=chatgpt.com",
"domain": "www.bestbuy.com"
},
{
"url": "https://www.bestbuy.com/product/samsung-pre-owned-excellent-galaxy-a13-5g-64gb-unlocked-black/JX87LRSWK8/sku/6638666?extStoreId=435",
"domain": "www.bestbuy.com"
}
]
},
{
"type": "chat_gpt_brand_entity",
"title": "BLU View 5 Pro",
"category": "product",
"markdown": "[BLU View 5 Pro](https://www.walmart.com/ip/TF-BLU-B170D-CDMA-LTE-GRAY-HANDSET-WALMART-POSA/5296361527?wl13=1077&selectedSellerId=0&wmlspartner=wlpa&utm_source=chatgpt.com)",
"urls": [
{
"url": "https://www.walmart.com/ip/TF-BLU-B170D-CDMA-LTE-GRAY-HANDSET-WALMART-POSA/5296361527?wl13=1077&selectedSellerId=0&wmlspartner=wlpa&utm_source=chatgpt.com",
"domain": "www.walmart.com"
},
{
"url": "https://www.walmart.com/ip/TF-BLU-B170D-CDMA-LTE-GRAY-HANDSET-WALMART-POSA/5296361527?wl13=1077&selectedSellerId=0&wmlspartner=wlpa",
"domain": "www.walmart.com"
}
]
},
{
"type": "chat_gpt_brand_entity",
"title": "Oscal C30 Smartphone",
"category": "product",
"markdown": "[Oscal C30 Smartphone](https://poshmark.com/listing/BRAND-NEW-SEALED-SMARTPHONES-66ad3e789ceaf7baf9d090bd?utm_medium=feed&utm_source=chatgpt.com)",
"urls": [
{
"url": "https://poshmark.com/listing/BRAND-NEW-SEALED-SMARTPHONES-66ad3e789ceaf7baf9d090bd?utm_medium=feed&utm_source=chatgpt.com",
"domain": "poshmark.com"
},
{
"url": "https://poshmark.com/listing/BRAND-NEW-SEALED-SMARTPHONES-66ad3e789ceaf7baf9d090bd?utm_medium=feed",
"domain": "poshmark.com"
}
]
}
]
},
{
"type": "chat_gpt_table",
"rank_group": 1,
"rank_absolute": 4,
"text": null,
"markdown": "| Budget | What you get |\n|---|---|\n| $30–$80 | Basic Android phones |\n| $80–$150 | Decent cameras + bigger batteries |\n| $150–$200 | 5G phones with better performance |\n\n",
"table": {
"table_header": [
"Budget",
"What you get"
],
"table_content": [
[
"$30–$80",
"Basic Android phones"
],
[
"$80–$150",
"Decent cameras + bigger batteries"
],
[
"$150–$200",
"5G phones with better performance"
]
]
},
"brand_entities": null
},
{
"type": "chat_gpt_text",
"rank_group": 3,
"rank_absolute": 5,
"markdown": "\n---\n\n✅ **Good brands to check on Google Shopping**\n- Samsung (Galaxy A series)\n- Motorola (Moto G series)\n- Xiaomi / Redmi\n- Nokia\n- BLU\n\n---\n\n💡 If you want, tell me:\n- your **budget**\n- **Android or iPhone**\n- **new or refurbished**\n\n…and I can show you **the best cheap phones on Google Shopping right now (with the lowest prices)**. 📱💰",
"sources": null,
"brand_entities": null
}
]
}
]
}
]
}
As you can see, the API returns a scraped AI response in JSON format. The result captures data from every displayed element of the response, including non-visible metadata.
Here is a quick breakdown of what you’ll get with the LLM Scraper:
1. Response text snippets. You’ll receive all text paragraphs, lists, and headings of the response in a Markdown markup language. Text snippets come as separate objects, so you won’t miss important details. At the beginning of the response, you will find the full response in Markdown language as well.
2. Extra response elements. Data from every additional element that may appear in the AI response is also included in the result. Whether it’s a table or an image gallery, the API provides granular insights into every element’s contents. For example, every product element scraped includes merchant information, the actual price, the product title, the image URL, and a set of product IDs if the product is listed on Google Shopping.
3. Sources and search results. The API provides all sources that the LLM cited and used to generate the response. Sources come in individual sources arrays for every scraped element in the response. Each source annotation includes not only the URL, but the title, source text snippet, publication date, and source domain. For ChatGPT, you will also receive search_results arrays that contain all websites the model retrieved when looking up information, even unused entries.
4. Fan-out queries and brand entities. The ChatGPT LLM Scraper returns fan-out queries that the AI uses to search for additional information. Besides, the API captures brand entities – structured data elements that help ChatGPT distinguish brand mentions from the rest of the response. Brand entities can help analyze whether ChatGPT recognizes your or your competitor’s brand and which category it belongs to.
In short, with LLM Scraper, you can easily build extensive datasets of detailed LLM responses, perfect for AI visibility tracking and content analysis. Additionally, you can use LLM Scraper without any coding. Just connect the DataForSEO MCP server to a popular AI model. The MCP server directly connects DataForSEO endpoints to a model, enabling you to run analysis right through AI chat. You can connect the MCP server in just a few minutes by following this Help Center guide.
Here’s an example of fast AI keyword research for ChatGPT, using LLM Scraper and AI Keywords Search Volume endpoint, done in Claude:
“Find keywords I can use to optimize my website’s content for a ChatGPT query “What is sustainable clothing?” Use the ChatGPT LLM Scraper to fetch and analyze the response, then extract relevant keywords. Run those keywords through the AI Keyword Data endpoint to retrieve AI search volume data and present them in a table sorted by search volume in descending order.”
As you can see, Claude used the ChatGPT LLM Scraper endpoint to fetch a response for keyword analysis. Then, Claude used the AI Keyword Search Volume endpoint to obtain AI search volume for keywords and visualized the results in a table. Within a minute, you performed a data-driven keyword analysis without a single line of code.
LLM Scraper serves as a powerful data foundation for content analysis and visibility tracking in LLMs. But is it cost-effective, and what are the pricing details? We’ll explain this below.
4 Pricing and cost breakdown
The pricing for LLM Scraper is straightforward, as with most DataForSEO APIs. It follows a pay-as-you-go model, meaning you pay only for the data you get. To retrieve one LLM result page in Live mode, you’ll pay $0.004, and in Standard (POST-GET) Mode, we charge only $0.0012 per result.
Let’s assume you want to build a dataset of ChatGPT responses for target queries to support AI visibility analysis. The total size of the dataset you want is 10,000 responses. If you use LLM Scraper in the Standard method to retrieve data, the math goes like this:
$0.0012х10 000 = $12
For only $12, you get a vast dataset of LLM responses with granular data to use in various types of AI research.
As might be obvious, LLM visibility tracking and content analysis require extensive, detailed insights from LLM responses. The LLM Scraper delivers these insights straight to your system or no-code solution at a fair price. But GEO is not only about tracking results and building optimized content. It is also about a deep understanding of LLM models and predicting their behavior.
Use case 2: Analyzing LLM search behavior
What distinguishes AI-powered search from traditional search most is the fluid, conversational format of the search results. Conventional SERP results appear as lists of links and sometimes include additional SERP features. On the other hand, LLM responses can vary significantly even within the same query, depending on numerous factors. That’s why the LLM behavior analysis emerges as a crucial part of optimizing for AI.
1 Why does it matter?
The LLM behavior may seem unpredictable and hard to understand. Sure, it’s not possible to access LLM algorithms directly, but you can analyze the patterns and differences in responses, while also simulating various scenarios. Moreover, each provider of popular AI platforms offers numerous types of models, each evaluating information and producing results differently. For successful GEO, it is vital to explore LLM behavior from multiple angles.
For example, analyzing how LLMs use reasoning capabilities can help you understand the essential steps of the models’ thought processes. You can evaluate how each model interprets user queries, what assumptions it makes, and what data it searches before generating the final answer. Ultimately, such analysis can reveal some of the factors the model considers when choosing between your content and your competitors’.
It is also important to assess how specific contexts can influence AI model output. While searching for information with LLMs, users typically don’t send a single prompt and then finish the search. They may engage in short or long conversations with AI to get more specific information, explore different sides of products, brands, and so on. For AI search optimization, taking conversational context into account can help produce more targeted content that is more likely to be recommended by LLMs and recognized by a user.
Another valuable angle of LLM analysis is understanding how AI models behave under different instructions. LLMs can be prompted to adopt specific roles, communication styles, and change the diversity of responses. Such instructions can significantly influence how the model interprets queries and information, and how it generates answers. For GEO, testing how models behave under different instructions can reveal which content framing and communication styles are more likely to resonate with AI-generated responses in a given context.
As you can see, the LLM behavior analysis is crucial for understanding how AI models evaluate, interpret, and present information. For such analysis, you need not only specific data but also the ability to programmatically interact with LLMs to set up different scenarios and use different functions to get specific results.
2 What data (and functions) do you need?
To conduct a comprehensive LLM behavior analysis, you need programmatic access to LLMs to set up different operation scenarios, explore which factors influence response generation the most, and receive specific outputs. Here are the things you should prioritize most.
1. First of all, you still need to get structured AI-generated responses with data from core response elements. The data should include clean text paragraphs from the answers, along with a list of sources used by the LLM in the response generation process. These core insights will serve as a foundation of your analysis, allowing you to identify patterns in how the model structures answers and selects sources.
2. To explore the step-by-step thought process of LLMs and how they evaluate queries, you need programmatic access to reasoning functions and reasoning data of AI models. For instance, you should be able to retrieve the reasoning chain content that generates before the final response. Tracking the number of reasoning tokens consumed per query can also be useful for indicating the complexity of the model’s thought process.
3. To provide all the necessary context to the model and simulate different conversation scenarios, you should have the ability to set specific “rules” and “memory” to a model before generating a response. In particular, the solution must include an option to specify previous conversation turns with the LLM and to separately provide tone and communication instructions.
4. Finally, to get even more detailed and specific results, you should have access to advanced parameters of AI models, like temperature and diversity of response generation. With such parameters, you can run more controlled analyses across different models and draw more reliable conclusions.
In summary, you need a solution that allows you to access LLMs programmatically and utilize the advanced functions they have under the hood. For this kind of analysis, the LLM Scraper endpoint is not the optimal tool. When providing granular response data, the LLM Scraper gives limited, interface-level access to the AI. This is where LLM Responses steps in, giving you broad and programmatic control over LLM functions.
3 The solution: LLM Responses
The LLM Responses is our tailored, multi-platform solution for advanced analysis of LLM behavior and response generation. What distinguishes the LLM Responses API from LLM Scraper the most is how it functions. The LLM Responses endpoints directly interact with official LLM APIs of ChatGPT, Claude, Gemini, and Perplexity, and support different versions of the models, not just a specific one. With direct LLM API access, you have nearly full control over model parameters, allowing you to set up various scenarios and contexts for research.
Here are the key functions and data this API gives you access to:
1. Customization of conversation history and model communication style. For every model supported in LLM Responses, you can specify a message_chain that reflects the previous conversation turns between the model and the user. Additionally, you can set up a custom system_message in the request to give AI communication instructions and tone of voice. In this way, you can set a custom context for a model before generating a response.
2. Access to reasoning capabilities. The API can fetch reasoning data from the models that support reasoning. This data includes text sections of reasoning chains that reflect the thought process of the models. For ChatGPT models, the API retrieves reasoning data automatically. In the Claude and Gemini endpoints, you need to specify the use_reasoning parameter to enable reasoning.
3. Advanced response generation parameters. In the request to LLM Responses endpoints, you can pass special parameters that control the output of a selected AI model. For example, you can customize the temperature and top_p parameters, which control the randomness and diversity of the answer. Besides, you can control whether AI uses web search or not by specifying web_search and supporting parameters.
4. Clear and structured response data. For every request, you will receive a clear and structured AI-generated response, structured in JSON. The result includes the answer text snippets and an annotations array with the sources the LLM used during response generation. Moreover, you will get data on the number of output, input, and reasoning tokens processed by the LLM.
In short, the LLM Responses gives you full programmatic control over the LLMs for researching their behavior and response patterns. Let’s take a look at an example request and response of the Gemini LLM Responses endpoint to see its functionality in practice.
POST: https://api.dataforseo.com/v3/ai_optimization/gemini/llm_responses/live
Request example:
[
{
"system_message": "communicate as if we are in a business meeting",
"message_chain": [
{
"role": "user",
"message": "Hello, what's up?"
},
{
"role": "ai",
"message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?"
}
],
"max_output_tokens": 2048,
"model_name": "gemini-3.1-pro-preview",
"web_search": true,
"user_prompt": "provide information on how relevant the amusement park business is in France now"
}
]
Response example:
{
"version": "0.1.20260318",
"status_code": 20000,
"status_message": "Ok.",
"time": "23.0909 sec.",
"cost": 0.043582,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "03181427-1535-0612-0000-9a145ef058ec",
"status_code": 20000,
"status_message": "Ok.",
"time": "23.0290 sec.",
"cost": 0.043582,
"result_count": 1,
"path": [
"v3",
"ai_optimization",
"gemini",
"llm_responses",
"live"
],
"data": {
"api": "ai_optimization",
"function": "llm_responses",
"se": "gemini",
"system_message": "communicate as if we are in a business meeting",
"message_chain": [
{
"role": "user",
"message": "Hello, what's up?"
},
{
"role": "ai",
"message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?"
}
],
"max_output_tokens": 2048,
"model_name": "gemini-3.1-pro-preview",
"web_search": true,
"user_prompt": "provide information on how relevant the amusement park business is in France now"
},
"result": [
{
"model_name": "gemini-3.1-pro-preview",
"input_tokens": 267,
"output_tokens": 1204,
"reasoning_tokens": 388,
"web_search": true,
"money_spent": 0.042982,
"datetime": "2026-03-18 14:28:12 +00:00",
"items": [
{
"type": "reasoning",
"sections": [
{
"type": "summary_text",
"text": "**My Assessment of the French Amusement Park Business in 2026**\n\nOkay, so the client wants a concise overview of the French amusement park landscape, presented as though we're in a business meeting. Understood. First, I need to get a clear picture. *Thank you* for bringing this up; it's a pertinent question. We need to be on top of the trends. My initial thought is to structure this response clearly, in a concise, and professional tone.\n\nI need to quickly assess the current state. I'll need to search for recent data, ideally looking at trends in the sector up to 2025 or 2026. *Let's review the current landscape*. We're looking at things like market size, growth trajectory, investment figures, and of course, attendance numbers. I'll also want to look at the market leaders. I know these off the top of my head, but I'll make sure to confirm the names, so I can ensure the search parameters are accurate: Disneyland Paris, Parc Astérix, Puy du Fou, and Futuroscope.\n\nOkay, let me initiate the search. I'm going to focus the search terms to get us to a good starting point: `amusement park business in France market size growth 2025 2026`. This should give us a solid foundation to build from. I'll then refine it as needed. *I've pulled some data*; now let's see what we've got."
},
{
"type": "summary_text",
"text": "\n\n\n**Amusement Park Market Update: France (2026)**\n\nAlright, here's the lay of the land in the French amusement park market. The data paints a pretty robust picture for this year and beyond. Let's get right to it.\n\nFirst, the **market size and growth** are quite compelling. The market hit around $3.25 billion in 2024, and the projections show it’s going to hit over $4.27 billion by 2030, which gives us a solid CAGR of approximately 4.4%. France holds a significant 3.2% of the global market share and is a clear leader in Europe, according to the research.\n\nNow, looking at some **major 2026 milestones**, Disneyland Paris is, naturally, the big headline. The transformation of the second park into \"Disney Adventure World,\" opening this March, is huge. This includes the 'World of Frozen', 'Adventure Bay', and new technology and immersive experiences. It's an excellent indicator of the industry's direction. We should keep an eye on how these regional park investments perform compared to the larger parks like Disneyland. It's worth noting the multi-million euro investments happening in mid-sized parks like Festyland and Parc du Bocasse. These investments into attractions like 'Valkyrie' coaster and 'Pendulonium' show strong market confidence and indicate an upscale movement.\n\nRegarding **key trends**, it is clear that technology integration is no longer optional. We're seeing heavy investment in AR/VR, interactive experiences, and advanced animatronics. Sustainability and eco-friendly practices are becoming equally important. Consumers are demanding more. It's also increasingly about experiential and immersive entertainment – the shift from isolated rides to fully themed worlds, as Disney Adventure World demonstrates, is the industry's future. We also see changing demographics in the market. Parks are trying to engage a wider audience, including the aging population, millennials, and are looking for a good balance between thrill rides and family-friendly experiences. We should keep these trends in mind when working on new marketing strategies or ride designs. Finally, we're seeing mergers and expansions, such as Maeva (Pierre & Vacances) taking over outdoor accommodation/franchises. This is a potential new market to capitalize on."
}
]
},
{
"type": "message",
"sections": [
{
"type": "text",
"text": "\n\n\nThank you for bringing this up. It’s a great topic to cover today, especially since the French amusement park industry is currently experiencing some pivotal transformations. I've pulled the latest market intelligence for 2026, and the landscape is highly relevant and full of investment opportunities. \n\nHere is a high-level briefing on where the amusement park business in France stands right now:\n\n### 1. Market Valuation and Growth\nThe French amusement park sector is demonstrating robust health. We saw the market generate roughly $3.25 billion in revenue recently, and it is projected to grow at a steady Compound Annual Growth Rate (CAGR) of about 4.4%, putting it on track to surpass $4.27 billion by 2030. France currently accounts for over 3% of the global amusement park market and remains the leading and fastest-growing regional market in Europe. \n\n### 2. Major 2026 Milestones and Capital Injections\nThis year is turning out to be a landmark year for capital investments and expansions across the board:\n* **Disneyland Paris Metamorphosis:** The biggest industry event this decade is the transformation of the Walt Disney Studios Park into **Disney Adventure World**, officially materializing in March 2026. This massive rebranding includes \"Adventure Bay\" (a massive new central lake) and the highly anticipated \"World of Frozen\" featuring next-generation animatronics and immersive environments.\n* **Regional Upscaling:** It's not just the mega-parks driving the industry. Mid-sized regional parks are making historic investments to capture domestic tourism. For instance, Festyland in Normandy is debuting *Valkyrie*, a €4+ million family boomerang coaster, and Parc du Bocasse is opening multiple new attractions centered around a highly themed steampunk universe. \n\n### 3. Key Industry Trends Driving Revenue\nIf we are looking at strategic positioning or investment in this space, here are the trends dictating consumer spend right now:\n* **Immersive Worlds Over Isolated Rides:** The market is shifting aggressively from standalone mechanical rides to fully immersive, IP-driven themed environments (like the new developments at Disney and Futuroscope). \n* **Tech Integration (AR/VR):** There is a massive push to incorporate Augmented and Virtual Reality into \"dark rides\" to enhance the interactive experience for guests.\n* **Broadening Demographic Appeal:** Parks are successfully optimizing their offerings to capture multiple age brackets. While families remain the core audience, there is a strategic focus on targeting adult demographics (ages 20-54) through premium dining, integrated resort hotels, and high-thrill nostalgia properties. \n* **Eco-Tourism and Sustainability:** Consumers and regulators are pushing for greener operations. We're seeing parks heavily market their sustainability measures, energy-efficient operations, and eco-friendly resort integrations (similar to expansions by the Pierre & Vacances-Center Parcs Group).\n\n**The Takeaway:**\nThe amusement park business in France is highly relevant and shifting toward a premium, resort-style model. The post-pandemic appetite for experiential leisure has stabilized into steady, reliable growth. If we are exploring partnerships, technology vendor opportunities, or direct investments, the current climate—fueled by strong domestic tourism and the 2026 mega-openings—makes France a prime market to target. \n\nWould you like me to drill down into the financials of any specific park operators, or should we look closer at the tech vendor opportunities within these new expansions?",
"annotations": [
{
"title": "grandviewresearch.com",
"url": "https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHTpD8hoVE7FQlCrx5TjHKGJIb-19xxigorH084LGv797-mSTDdGBncnQpeFaDeUn4gyYpFcgQDVtV0DpcT1AFiN2rl0Tk12vyLNRbI0USVtcaSUnvquLM6BwKScZZsupo7MJ-ahGPB-yA1eu1TYEyLALfZ4p-FqgchtciGHHeHpWNeu2EhPv26E64="
},
{
"title": "parktrips.fr",
"url": "https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFvMycEwa2q8vAKdXTYG-sXN9QhsqAbJI919jYJW7ey6LdlK7TCPcu1Kjciqgv0bzsq4KHAZluQafIqtzXG8tICITClKtQ4gmR03tl6i5K0_jvanYsUp_lr2xx16RHBk8PMjgMN3Ai0yuqO"
},
{
"title": "6wresearch.com",
"url": "https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGImAkdPw6fr_CMz-BS_1jfWSjGv_RaL0KPOMZRHS0UeEj5qsUSvhLwYfxA93jHkn5iM1vlgF35SuTZlaEkYFNAuLbmqGQE-s-4iVDmIJnGJUAyEFuheyPPqMuw73jXhEvr6CZoND4I3NlR80g2_v4hBuYvqfpXObhbOlxSHiWqOW_z-Q223qgF_0o="
},
{
"title": "mordorintelligence.com",
"url": "https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEUjknl3DCpsq5wZI-GzDce9u-ECFc8f0w_ccpVENfraphE2lKyftyjNmKgY5JPtggY-K8xRK4nlZN5Fxni8QhMG1NzGRJ1JRD9rMwUtcZAYEpj2nXB9G1tNs_cpwm32DpGLD5phwIe6TspuJm-FxdtDJBfWGwx2bSBtWem1qpKyGwsm7vv"
},
{
"title": "industryarc.com",
"url": "https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGgw0GxQDRhTuylXtBFHKTfqQm3jR_cpDU892DcSFDh5XxLGnEgaFe5jWXT3SVPYOCXljw-zxUH813WPBIIhYmLuHGUQcwi_E8rdWv-kzpga3dWHp-cIHHHSy0vdLzTwvDgakMTLrjvs7xHHnyO_YeXg5ia8yRWnr5VqvA6XkvT1RyTJrLizQ=="
},
{
"title": "groupepvcp.com",
"url": "https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFyep0baezFljxWw9XSxlR8Cep7P5sEPkTbtaFq6vP9y-FKWaF7zNxvKnVVeykupnNJA-i_edOtajhCsRU0IKY-fy7diElSEOOjfPNjhnkI7Fo4QBh0bProTYRKhImpVg=="
}
]
}
]
}
],
"fan_out_queries": [
"\"amusement park\" business in France market size trends 2025 2026",
"French theme park industry recent investments 2025 2026"
]
}
]
}
]
}
Here is the result. In the items array, you can find the reasoning item that contains reasoning chain sections, which describe the Gemini thought process. Below, you can see the message item with structured text of the model’s answer, and an annotations array of cited sources. Moreover, at the end of the result, you can explore the fan_out_queries model used to get additional information.
As you can see, LLM Responses delivered a tailored AI answer with structured data, taking into account all instructions specified in the request. You can easily pass other rules and instructions and experiment with the model’s behavior to align with your research goals.
How much does it cost to use such an advanced solution? Let’s explore key pricing details below and find out.
4 Pricing and cost breakdown
The pricing system for LLM Responses also uses a pay-as-you-go model, but the calculation is different. Because our system directly interacts with LLM APIs to send requests and retrieve responses, we must include LLM API’s pricing into the total request price. Eventually, the price for a single task in Live mode will be calculated as follows: $0.0006 per request + the price charged by the LLM API. The price for tasks sent using the Standard method is $0.0002, plus an advance payment of $0.01, which we partially refund if the final price is less than $0.01. In the dedicated Help Center Article and on the Pricing page, we explain pricing in more detail.
Now, let’s do some practical calculations. Suppose you want to analyze and retrieve 2000 Claude responses using the Live mode, and let’s assume that Claude charges $0,005 for processing each request. The calculation is as follows:
($0.0006 + $0.005) x 2000 = $11.2
By spending just $11.2, you can retrieve around 2000 tailored Claude responses with structured data for in-depth LLM behavior analysis at scale. What’s more, you can switch to a different model or LLM platform at any time, without the need to pay extra for individual access to each LLM API.
Conclusion
Tracking brand visibility in LLMs and analyzing their behavior are two distinct but equally important pillars of GEO. Each requires a different approach to data collection and access to AI models’ functions, as well as a different set of tools to transform data into valuable insights.
For assessing and tracking brand and content visibility in LLMs, the LLM Scraper is your go-to data solution. This API captures everything ChatGPT and Gemuni provide to users in a structured JSON. Text snippets, product cards, and brand entities – this API pulls data from every element of the answer. With this data, you can build extensive workflows for visibility tracking, content analysis, AI keyword research, and more. Also, LLM Scraper is a cost-effective solution for retrieving response data at scale. With its straightforward, low-cost pricing, you can build extensive LLM datasets without incurring high costs.
For analyzing LLM search behavior in different scenarios, the LLM Responses gives you programmatic control over the LLMs you need. With direct access to popular LLM API providers, you can customize model parameters, simulate conversation scenarios to compare how AI interprets queries, and determine which information surfaces depending on the context. In terms of pricing, LLM Responses is more expensive to use than LLM Scraper, due to expenses on the LLM API connection. However, the cost is still better compared to individual access to LLM APIs, and you have the flexibility to explore multiple LLMs simultaneously.
Whether your priority is AI visibility and content analysis or exploring how LLMs work under the hood, DataForSEO provides the right data infrastructure for both. Register at DataForSEO today and start building a complete, data-driven approach to GEO.
