That used to matter to me back in the days when the best models still only accepted ~32,000 tokens, but these days even the models that run on my laptop are happy with ~100,000 and the hosted models I use take ~200,000 or more.
That used to matter to me back in the days when the best models still only accepted ~32,000 tokens, but these days even the models that run on my laptop are happy with ~100,000 and the hosted models I use take ~200,000 or more.
They accept more tokens these days, but they are still more accurate with a shorter context [1].
[1] https://arxiv.org/abs/2307.03172
If it's one of many tool calls, I'd assume that less is more.
The trick there is to use a subagent to read the HTML page and extract the relevant information, than dumping all that HTML into your top-level session.
That's effectively using an LLM as an HTML to markdown converter, which is both absurdly wasteful and also surprisingly inexpensive (if you use a model like GPT-5.6 Luna.)
I found HTML itself works [slightly] better with some LLMs (that I happen to frequent). So, when scraping, I parse the resulting html, simplify it, and produce simpler html contents (closer to semantics of what I guessed the real content was). Given processing tools for html are far more mature, I tend to keep content in semantic, low structure html.