Top 6 LLM Scrapers: ChatGPT, Perplexity & Gemini
We benchmarked how the top LLM scraper providers, including Bright Data, Oxylabs, and Apify, perform at extracting outputs from LLM platforms such as ChatGPT, Gemini, Perplexity, and Google AI Mode.
To ensure reliable results, we ran 1,000 tests per provider, repeating each prompt 10 times for consistency. The top-performing provider is detailed below.
Multi-model support across LLM scraper providers
LLM web scraping benchmark results
Providers missing from specific charts (e.g., Oxylabs in ChatGPT mode or Apify in Google AI mode) were omitted because their success rates did not meet the 90% minimum reliability threshold required for this benchmark.
What counts as an LLM scraper?
The term is used in two different ways, and they need different tools:
1. Scraping LLM platforms: extracting answers, citations, and metadata directly from ChatGPT, Perplexity, Gemini, and Google AI Mode. This is what our benchmark covers.
2. LLM-powered scraping: open-source libraries that use an LLM to pull structured data from any website via natural-language prompts instead of CSS selectors. If that’s what you’re after, see our guide to open-source web crawlers for LLM & AI.
Best LLM web scraping providers
Bright Data demonstrated the most robust performance across all tested models, consistently maintaining a success rate near 100%. It significantly outperformed competitors in metadata richness, capturing up to 25 fields in ChatGPT mode.
Bright Data was the only provider to successfully meet the 90% success threshold for the Gemini model, establishing it as the most versatile option for multi-LLM prompt-based scraping.
Bright Data offers a variety of pre-built templates for AI platforms.
- ChatGPT scraper: Submits prompts to the ChatGPT interface and collects responses.
- Perplexity search (by prompt): Gathers citations and source lists from Perplexity, an AI-powered search engine.
- Google Gemini and Claude (collect by URL): Bright Data’s Scraping Browser automates access to these platforms, which feature strong anti-bot protections.
- AI training datasets: Bright Data provides ready-made datasets of AI-generated content, enabling companies to fine-tune their models without scraping data.
Oxylabs demonstrated strong reliability in Google AI and Perplexity modes, achieving success rates above 94% across a wide range of available metadata fields. However, it was excluded from the ChatGPT mode analysis as its performance fell below the mandatory 90% success threshold. Its strength lies in structured data extraction through search-centric AI models.
Oxylabs offers web scrapers for Perplexity, ChatGPT, and Google AI Mode (SGE). The ChatGPT Scraper allows you to send prompts to ChatGPT, automatically collect responses and structured metadata, and select the country of origin for each prompt. JavaScript rendering is always enabled for ChatGPT.
The ChatGPT Scraper supports prompts up to 4,000 characters. For longer inputs, divide your text into smaller sections and submit them as separate requests. The Perplexity Scraper uses JavaScript rendering for all requests by default. Batch requests are not supported for either Perplexity or ChatGPT.
Decodo offers scrapers for ChatGPT, Perplexity, and Google AI Mode, with particular emphasis on extracting Google’s AI-generated search answers. The ChatGPT scraper includes a “Web Search” toggle that lets users gather real-time browsing data directly in the interface.
The API supports multiple response formats in a single request, including Raw HTML, Parsed JSON, Markdown, XHR, and PNG screenshots, providing developers with greater flexibility.
Decodo offers competitive pricing, with the “23K req” plan available at $29 per month, which comes to approximately $1.25 per 1,000 requests. In addition to its affordability relative to larger providers, the service includes features such as JavaScript rendering and geo-location targeting.
SerpApi offers a Google AI Mode API that allows users to extract results from the Google AI Mode page and supports contextual follow-up queries. By using the subsequent_request_token in each response, users can initiate new requests and compare AI content and layout across desktop, tablet, and mobile devices.
The provider offers a free plan to test their scraper, including 250 searches per month.
Apify’s LLM scraper maintained a high success rate (approx. 99%) within ChatGPT mode, though it captured a more limited range of metadata fields (averaging 4) compared to its peers.
Due to success rates falling below the 90% benchmark, Apify was excluded from the performance charts for Google AI and Perplexity modes, suggesting a more specialized focus on standard ChatGPT-driven tasks.
You provide a standard JSON Schema or a similar format, such as Pydantic. The Actor ensures the LLM processes raw HTML and maps it to your specified fields. Apify’s LLM scraper offers a technical advantage over self-hosted libraries through its integrated Apify Proxy system, which includes services like Bright Data and Oxylabs.
To reduce LLM costs, Apify removes unnecessary tags such as <script>, <style>, <svg>, and <iframe>, along with navigation elements and hidden metadata.
ScrapingBee’s ChatGPT API enables users to obtain AI-generated responses by integrating GPT-4 with real-time web search in a single API call. If a request fails, the service automatically retries for up to 30 seconds. Each successful request consumes 15 credits.
The API provides structured data outputs in either Markdown or JSON formats and incorporates source citations within results_markdown or designated HTML tags. This integration allows users to access web content and language model capabilities simultaneously, eliminating the need for separate scraping and AI tools.
Add as preferred sourceHow to scrape each LLM platform
How to scrape ChatGPT
ChatGPT scrapers submit a prompt to the ChatGPT interface and return the response plus structured metadata (citations, model version, timestamps). In our benchmark, Bright Data led on metadata depth (~25 fields at ~98% success), and Apify was highly reliable (~99%) but returned fewer fields (~4). Oxylabs fell below the 90% threshold in this mode.
JavaScript rendering is required; Oxylabs caps prompts at 4,000 characters and does not support batch requests.
How to scrape Perplexity
Perplexity scrapers capture the answer text along with the citations and source list. In our benchmark, Bright Data (~100% · 18 fields) and Oxylabs (~94% · 13 fields) landed in the most attractive quadrant; Decodo was close behind (~95% · 9 fields). Apify fell below the threshold here.
JavaScript rendering is on by default; batch requests are not supported.
How to scrape Google AI Mode
Scraping Google AI Mode (SGE) means extracting the AI-generated answer that appears above traditional results, ideally with its contextual follow-up queries. Bright Data (~100% · 11 fields) and Oxylabs (~98% · 12 fields) performed best; SerpApi exposes a dedicated Google AI Mode API with a subsequent_request_token for follow-ups and device-level (desktop/tablet/mobile) comparison. Apify fell below the threshold.
How to scrape Gemini
Gemini is the hardest target in this benchmark: only Bright Data cleared the 90% reliability threshold (~100% · 14 fields), using its Scraping Browser to handle Gemini’s anti-bot protections.
LLM scraper benchmark methodology
Each provider was tested with 100 unique prompts, each executed 10 times, yielding 1,000 total tests per provider. All prompts were open-ended technical questions in the AI and machine learning domain requiring paragraph-length responses.
Each provider was assigned a ten-minute timeout per prompt. If a request encountered a rate limit (HTTP 429), we waited ten minutes before retrying. A two-second pause between requests helped prevent rate limits and ensured efficient benchmarking.
Validation success:
Each prompt included 5 selector keywords representing core concepts expected in relevant responses. For example, the prompt “What are the key differences between traditional RAG and agentic RAG systems?” used the keywords: RAG, difference, agentic, retrieval, and traditional.
These keywords formed the basis of our data validation. We checked for their presence in the answer text to assess accuracy. If no keywords appeared, the response was marked as incorrectly extracted. For non-empty citations, we verified that at least one valid URL with proper HTTP or HTTPS formatting was present. Responses were classified as valid if they passed all checks, as warnings if they failed due to empty content or missing citations, and as errors if they encountered technical issues such as parsing failures.
Submission success:
We measured the percentage of API requests accepted by the scraping provider. A request was successful if it returned an HTTP 200 or 201 status code and included a valid job identifier or immediate response. This metric reflected provider infrastructure reliability before scraping began.
Execution success:
We measured the proportion of accepted requests that completed the scraping job and returned data.
We tracked these three success rates throughout the pipeline to identify failure points at each stage. For the final analysis, we report the validation success rate, as it measures end-to-end performance from API call to semantically relevant, citation-verified content. While a provider may achieve 100% submission and execution success, Validation Success determines whether the scraped data is usable in production applications.
Execution time:
The duration required to receive a complete response. For asynchronous providers such as Bright Data and Apify, this included the polling period from job submission to completion. For synchronous providers like Oxylabs, it was the total elapsed time for the request.
To maintain a high standard of data quality, providers with a success rate above 90% were represented in the comparative charts. As a result, Oxylabs (ChatGPT mode) and Apify (Google AI mode) were excluded because their performance fell below this benchmark. It is also worth noting that Bright Data was the sole provider to employ Gemini for prompt-based scraping in this test.
Available metadata:
We counted the number of structured data fields returned alongside the raw text, including citations, links, response text, location, model version, and others.
Cite this research
Pick the format that matches where you're publishing. Pasting the link version into your CMS preserves the backlink.
@misc{karatas2026,
author = {Karatas, Gulbahar and Şipi, Nazlı},
title = {{Top 6 LLM Scrapers: ChatGPT, Perplexity & Gemini}},
year = {2026},
month = jun,
howpublished = {\url{https://aimultiple.com/llm-scrapers}},
note = {AIMultiple. Retrieved June 29, 2026}
}
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