Plans the shortest route, with zero model tokens.
A small world of rooms, with the goal two to five moves away and a loop and a dead end on the way. Each task shows which move leads where, then asks for a route. Duplex, a reactive agent and GPT-5.5 got the same map.
Internal benchmarks. Every figure on this page comes from tests Vareon ran itself and has not been independently verified. Read the disclaimer
- 100%*Plan success, on every runA reactive agent: 0% · Duplex used no model tokens
- 0%*A reactive greedy agent
- ~204*Tokens per decision a frontier model spends to match it
* Figures from Vareon’s internal benchmarks, not independently verified. Read the disclaimer
Finding the shortest route.
Step 1 of 5: The world
The same answer, without the bill.
What GPT-5.5 and Duplex each paid for the same shortest routes.
Step 1 of 3: Tokens
Duplex spent no model tokens on any decision. GPT-5.5 spent about 204 on each.*
The move that looks closest can lead nowhere.
Where each approach’s moves led, over ten runs.
Step 1 of 2: Reactive
A reactive agent’s first moves: about half went into the loop or the dead end, and none reached the goal.*
Four checks, passed on every plan.
What was checked on every plan Duplex made.
Step 1 of 4: Shortest
Every plan was the exact shortest route.*
How it was run.
- The work
- Find a route through a randomly generated world of rooms, two to five moves to the goal, with a loop that goes nowhere and a dead end.
- Scale
- One hosted run of 20 tasks, and ten independent runs of 12 plans each.
- Compared with
- A reactive agent that looks one move ahead, and GPT-5.5 given the same map in its prompt.
- Kept equal
- The same map and the same task for every approach.
What it means for your work.
Next: Agent tasks keep succeeding, with fewer model calls.Read it →
See it on your own work.
From your own assistant to every agent your company runs, a pilot puts Duplex on your own work, with the measures agreed before it begins.