Research

Benchmarks and papers.

How agents and models behave inside simulated companies. Every result is graded from state, with its method and limits.

Report No. 001

Six models, 600 graded tries.

The ranking changed completely between easier and harder tasks.

Every model did worse on harder tasks, but not by the same amount.

Share of tries that passed, out of 50 per model on each set of tasks.

Easier tasks
Harder tasks
Gemini 3.8 Flash
40%
98%
−58 pts
Gemini 3.7 Flash
42%
96%
−54 pts
GPT 5.6 Luna
4%
64%
−60 pts
Biggest fall: from 64% to 4%.
GPT 5.6 Terra
26%
62%
−36 pts
GPT 5.6 Sol
26%
60%
−34 pts
Gemini 3.5 Flash
34%
54%
−20 pts
Smallest fall. It ended up ahead of all three GPT models.
0%
50%
100%
drop

Benchmarks

No. 001: Cross-App Agent Benchmark
Sept 2026
Mobile Assistant Bench: Personal Use
Oct 2026
No. 002: Multi-app benchmark on DrizzLabs twins
In progress

Papers

Grading what changed
In preparation
One run isn’t a result
In preparation
Why agents fail at the third-party boundary
In preparation

Guides

Run the benchmark on your own agent.