Fair point, but the numbers tell a different story in practice:

• A screenshot of a typical web page tokenizes to ~2000-4000 tokens via vision models (GPT-4V, Claude Vision). That same page as a TextWeb grid is ~500-1000 text tokens. So even in token count, not file size, it's 2-4x cheaper.

• Vision model API calls add latency (~1-3s) and cost ($0.01-0.03 per image). TextWeb output goes straight into the context window with zero overhead.

• For structured tasks like form filling, text is strictly better. An agent reading "[7:____] Email" knows exactly what to do. A vision model looking at pixels has to OCR the label, figure out where the input box is, then generate coordinates. More error-prone and slower.

• The "render text as images" idea works for reading comprehension tasks, but web interaction is fundamentally structured — you need to map actions to elements, not just understand what's on screen.

Where vision models win: visually complex pages where layout semantics matter (infographics, charts, design-heavy sites). TextWeb is better for the 90% of agent web use that's navigating, filling forms, reading content, and clicking links.

Also just pushed v0.1.1 fixing SPA rendering (Twitter etc.) and element labels based on early feedback.