AI & SCRAPING
The model matters less than you'd think: this web scraping API explains why the infrastructure layer decides most of it.
There's no single "best" AI model for scraping, because the model isn't what determines whether a request succeeds. Proxy handling, anti-bot bypass, and JavaScript rendering, the infrastructure layer, decide whether you get the page at all. Which AI model processes the result afterward is a separate, swappable choice.
"Scraping" actually covers two distinct steps: getting the page (fetching it reliably past whatever blocks the site puts up), and understanding the page (turning raw content into the specific data you want). AI models are genuinely useful for the second step, pulling structured fields out of messy or inconsistent content. They contribute almost nothing to the first step, which is a networking and anti-bot problem, not a language problem.
If a request never gets past a site's anti-bot system, no AI model, however capable, has anything to work with. That's why the practical answer to "which AI is best" is usually the wrong question; the better question is which scraping infrastructure reliably gets you the page, after which almost any modern model can handle extraction reasonably well.
Once you have a page, an AI model can help normalize inconsistent formatting, extract fields that don't map to a fixed schema, or summarize long content. Connecting an AI agent directly to a scraping API via MCP, so the agent can call the tool, get structured data, and reason over it in one flow, is one practical way to combine both pieces without manually gluing them together.