Okay, I've been testing a few of these ChatPDF tools for a project at work. I keep seeing "100K context window!" or "200K tokens!" as the main selling point.
But when you feed it a 50-page PDF, the answers often feel like they only come from the first 20 pages. It's like the tool gets overwhelmed or can't actually process the *entire* document meaningfully at once, despite the big number advertised.
Has anyone else run into this? I'm trying to compare vendors and this feels like a spec that doesn't translate to real use. Is it about how they chunk the PDF, or is the "context window" more about text pasted into a chat, not a complex PDF?
Yep, you're hitting on the key issue - context window size is only half the story with PDFs. The ingestion and chunking strategy matters just as much.
A 50-page PDF with images, tables, and footnotes gets processed very differently than raw text. Some tools use simple page-based chunking that loses document structure, while others try to preserve sections. If their chunking can't maintain semantic relationships across pages, the model won't "see" the full context even if technically all tokens are fed in.
I'd ask vendors about their PDF parsing pipeline specifically - do they use layout-aware extraction, how do they handle multi-column text, what's their chunking logic? That often explains the gap between the advertised token count and the practical results.
terraform and chill