AIMultiple/aimultiple-decision-models-browser

AIM Decision Model Browser Benchmark: 10-task sample

This is 10 of the 50 tasks from AIMultiple's decision model browser benchmark. The benchmark compares decision models (also called System One models) with general LLMs on browser tasks: Jev 1.13, Kev-9B, Laya typed-decisions, Gemini 3.8 Flash and GPT-6 Astra. The other 40 tasks are withheld so they can be reused for later runs.

Article: https://aimultiple.com/decision-models

Files

• tasks.jsonl has one task per line: start URL, instruction, accepted final URLs and pass conditions.
• results.csv has one row per task and model: pass or fail, how the attempt ended, seconds and number of browser actions.
• media/ has two demo clips of the models working the same task at real speed.

How tasks are scored

An attempt passes only if the model declares the task done within the limits and an independent check of the final page confirms every condition. The URL must be one of the accepted final URLs, every listed selector condition must hold, and the page must be fully loaded. A model's own claim of success does not count.

Conditions use CSS selectors. text is the element's whitespace-normalized text, texts is the list over all matching elements, value is a form value, count is the number of matches and visible means rendered and not hidden by CSS.

Limits per attempt were 900 seconds, 60 browser actions and 120 decision calls. Every attempt started in a fresh browser session and ran once.

Sample

ID	Site	Type
B04	Books to Scrape	Category switch to product page
W01	Wikipedia	Article lookup
Q06	Quotes to Scrape	Dependent filters
Q08	Quotes to Scrape	Infinite scroll
V07	Web Scraper	Native dropdown
H05	Scrape This Site	AJAX table
C06	ScrapingCourse	Product options
M02	ScrapeMe	Sort
D04	web-scraping.dev	Load more
T02	TestPages	Form submit

The tasks were chosen to cover every site in the benchmark and a mix of outcomes, from tasks most models passed to T02, which no model passed. Pass rates in this sample do not match the full benchmark: Jev and Kev pass 5 of these 10 tasks but completed 17 and 20 of all 50.

Runtime

All models used the same open-source runtime, browser-use/jev-ultrafast. At each step it turns the page into text plus a numbered list of visible controls, and the model picks an operation and a target. Values typed into form fields come from a separate text model, Mercury 2.5, which was available to every participant.

Practice sites change over time. A task that fails today because a site changed is a site problem, not a model result.

Demo clips

Five models on W01 (top row Jev, Kev-9B, Laya; bottom row Gemini 3.8 Flash, GPT-6 Astra and a results card):

Four models on Q06, filtering quotes by author and tag:

The clips come from separate demo runs, not from the scored attempts in results.csv.