Tested 7 Best LLM Scrapers: Benchmarks for Cost, Speed, and Data Accuracy
Large Language Models (LLMs) have transformed web data extraction. As the field advances, the gap between capabilities and real-world performance has become increasingly important.
We conducted a comprehensive benchmark comparing leading providers, including Bright Data, Oxylabs, and Apify, across AI environments such as ChatGPT, Gemini, Perplexity, and Google AI Mode.
Multi-model support across LLM scraper providers
LLM web scraping benchmark results
- Bright Data emerged as the clear market leader, consistently occupying the “Most Attractive” quadrant across every tested mode. It delivered the deepest metadata (up to 25 fields) and was the only provider to sustain high-reliability performance using Gemini.
- Oxylabs and Apify demonstrated specialized strengths but lacked universal consistency. At the same time, Apify showed high success in ChatGPT mode, but it struggled with metadata depth, and both providers fell below the 90% success threshold in specific search-centric AI environments.
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.
The 7 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.
Notably, 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.
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.
Features:
- To reduce LLM costs, Apify removes unnecessary tags such as <script>, <style>, <svg>, and <iframe>, along with navigation elements and hidden metadata.
Firecrawl, unlike Crawl4AI, a flexible Python library for custom pipelines, handles the data extraction infrastructure. This lets developers focus on building LLM application logic rather than on proxies, retries, or headless browsers.
You can use Firecrawl as open source (AGPL-3.0) or as a hosted service. If you run Firecrawl with Docker on your own servers, the Open Source Orchestration Layer manages scraping tasks using Playwright, Redis, and BullMQ, and follows standard scraping protocols. Keep in mind that scraping sites protected by Cloudflare, Akamai, or DataDome will probably get your server’s IP blocked.
Fire-Engine is a proprietary backend that you can only access through Firecrawl’s cloud service. It is designed to get around advanced anti-bot systems. Fire-Engine manages TLS fingerprinting, header rotation, and Canvas fingerprinting, and, unlike the self-hosted version, comes with a managed proxy network.
Features:
- Firecrawl automatically uses an intelligent filter to remove headers, footers, and sidebars. This means you get only the main content, which helps you save LLM tokens.
- The API can also provide content in a “summary” format if requested.
- You can set up a list of actions, such as click, scroll, wait, type, and take screenshots.
ScrapeGraphAI is primarily a Python library for local use, unlike Firecrawl or Jina Reader, which are managed API services. To prevent getting blocked when data collection at scale, you need to set up third-party proxy servers yourself. It does not include a built-in managed proxy network, such as Fire-Engine from Firecrawl.
Traditional scrapers rely on CSS selectors or XPath expressions, which can break easily. ScrapeGraphAI uses Semantic Mapping instead. It relies on natural-language prompts to find web data, so it can adapt when web pages change their class names or layout. The LLM can still identify items like “Price” or “Product Title” by understanding the context, not just fixed paths.
ScrapeGraphAI works directly with Ollama, so you can extract data locally, privately, and for free. You can use models like llama3 or mistral on your own machine to process HTML.
Crawl4AI
Crawl4AI is an open-source Python library that uses Playwright’s asynchronous API to handle hundreds of URLs simultaneously. Instead of working like a standard scraper, it acts as a Data Preparation Engine for the transform step in ETL. This makes sure the output is fully token-optimized for Retrieval-Augmented Generation (RAG) pipelines.
Features:
- The library includes a markdown generation engine that cleans up pages by removing navigation links, footers, and ads.
- It keeps link and image metadata in the Markdown, so you can cite sources or analyze visual content.
- It brings together execution logic like caching, image handling, and page interaction in one place.
- It manages browser settings, including headless mode, user-agent spoofing, and proxy configuration.
Jina Reader
Jina AI’s Reader is a Small Language Model (SLM) that converts unstructured HTML into clean, structured data. Like Firecrawl, it is available as a plug-and-play cloud service and as an open-source version.
The open-source version uses Puppeteer and Chromium to render pages, accessing sites through your own IP address. The cloud service retrieves URLs using its built-in proxy layer.
Features:
- It automatically captions images on each page using a vision-language model and replaces <img> tags with descriptive alt-text.
- It also offers a specialized SERP (Search Engine Results Page) endpoint. Rather than returning only links, it performs the search, collects the top 5 results, scrapes them, and returns the cleaned text in a single call.
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, only 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.
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