WindTunnel. An open benchmark for WebMCP.

Name: WindTunnel: WebMCP benchmark runs
Creator: nekuda
License: https://github.com/nekuda-ai/WindTunnel

WindTunnel compares WebMCP with other ways browser agents interact with websites.

github.com/nekuda-ai/WindTunnel Star us

Tests completed

94–98%

Tests completed by agents that used WebMCP

(vs. 78–90% with screenshot + DOM agents)

Faster

3–5×

task completion

on the same tasks

Less cost

5–10×

per task

up to ~12× on longer journeys

Less token usage

5–11×

per task

Leaderboard

Rank	Configuration	Interface	Final score	Tasks solved	Attempt success	Median costper task	Median tokensper task	Median timeper task
1	Sonnet 4.6 · Stagehand v4	WebMCP	91.9	48/49	144/147 (98.0%)	$0.008	3,596	7.3s
2	Gemini 3.6 Flash	WebMCP	89.9	47/49	142/147 (96.6%)	$0.004	4,460	7.8s
3	Sonnet 4.6 · native	WebMCP	88.3	48/49	142/147 (96.6%)	$0.007	3,626	7.2s
4	GPT-5.6 Luna	WebMCP	85.5	46/49	137/147 (93.2%)	$0.002	2,596	5.7s
5	Claude Opus 5	WebMCP	81.9	48/49	142/147 (96.6%)	$0.014	4,871	9.9s
6	GPT-5.5	WebMCP	76.2	47/49	137/147 (93.2%)	$0.015	2,546	5.8s
7	GPT-5.6 SOL	WebMCP	74.7	46/49	138/147 (93.9%)	$0.013	2,573	9.4s
8	GPT-5.5	Computer use	47.3	44/49	131/145 (90.3%)	$0.090	18,659	20.4s
9	Sonnet 4.6	Accessibility tree	35.8	42/49	128/147 (87.1%)	$0.043	11,784	35.6s
10	Sonnet 4.6	DOM + vision	34.7	43/49	130/147 (88.4%)	$0.112	33,365	37.8s
11	GPT-5.6 Luna	Computer use	33.9	41/49	122/147 (83.0%)	$0.017	20,914	18.3s
12	GPT-5.6 SOL	Computer use	26.8	42/49	123/147 (83.7%)	$0.063	16,312	25.1s
13	Claude Opus 5	Computer use	24.8	43/49	127/147 (86.4%)	$0.139	47,141	50.4s
14	Gemini 3.6 Flash	Computer use	15.2	38/49	116/147 (78.9%)	$0.020	23,658	33.7s
15	Sonnet 4.6	Computer use	9.7	39/49	115/147 (78.2%)	$0.047	38,856	31.1s

Final score: The final score combines attempt success rate (60%), median cost per task (20%), and median agent time per task (20%). Cost and time are log-normalized (large differences are compressed so extreme values do not dominate); higher is better. Tokens are shown separately and are not scored twice.

Methodology

We operate eight real websites across a wide range of tasks — 49 tasks, three attempts each. There are three ways to operate the website:

WEBMCP

Tool calling

The website exposes direct actions for the agent to call.

SCREENSHOTS

Computer use

The agent reads rendered images of the page and acts by coordinate.

PAGE STRUCTURE

DOM

The agent reads the page's DOM and accessibility tree.

Everything is fully reproducible — the methodology, code, task definitions, and full run transcripts are on github.com/nekuda-ai/WindTunnel.