Expand launches Expand Fetch, a web reading API for AI agents that it says beats the next best tool 74% to 55%
Introducing Expand Fetch, a new way to scrape and crawl the web. Your agent has never seen the web. No wonder it gets basic questions wrong. Traditional Markdown APIs lose critical details. So we trained two System one models to change that: Iris, a vision model that sees pages like you do, preserving tables, icons, and layouts. X-Ray, a JSON model that uncovers the hidden data behind every page. Every fetch also comes with a Wayback Machine. Every line links back to its source, so you can verify exactly what your agent saw. Three products. One API. On our benchmark of 1,094 questions across 326 challenging web pages, agents using Expand got 74% right. The next best API got 55%. Available today at $1 per 1,000 fetches. Try it at
Context
Agents are only as good as what they can read, and Expand is pitching trained vision and data models as a fix for scrapers that drop tables, icons and hidden page data.
Tim Suchanek, who posted the launch, is with Expand, so this is a first hand but promotional announcement. The 74% versus 55% result comes from Expand's own benchmark of 1,094 questions across 326 web pages and has not been independently reproduced. The post reached the tracked feed through a quote by kimmonismus.
Deep dive
On October 14, Tim Suchanek introduced Expand Fetch as a new way to scrape and crawl the web for AI agents. His argument is that agents have never really seen the web, and that traditional APIs that convert pages to Markdown lose critical details.
According to the post, Expand trained two models for the job. Iris is a vision model that sees pages the way a person does and preserves tables, icons and layouts. A second model returns the hidden data behind each page as JSON. Suchanek writes that every fetch also comes with a Wayback Machine, which suggests a saved copy of each page, and that every line links back to its source so developers can verify exactly what an agent saw. He describes this as three products in one API.
On Expand's benchmark of 1,094 questions across 326 challenging web pages, Suchanek says agents using Expand answered 74% correctly, against 55% for the next best API. The post does not name the competing APIs or say how the questions were chosen, so the comparison should be read as a vendor claim.
Expand Fetch is available now at $1 per 1,000 fetches, according to the post.
The launch lands in a crowded market for agent web access, where many developers already use scraping and search APIs that return Markdown. Expand's pitch is that keeping visual layout and structured data, plus a verifiable record of each page, will make agents answer basic questions more reliably.
What to expect next
Watch for independent comparisons of Expand Fetch against established scraping and search APIs, and whether Expand publishes its benchmark questions so others can reproduce the 74% result.
Also watch pricing pressure in the agent web access market, since $1 per 1,000 fetches sets a clear price point, and whether model makers build similar page vision features directly into their own browsing tools.