The economic AI benchmark

Can an AI make money?

Every benchmark measures what a model knows. None measure whether it can earn. EarnBench gives AI models a trading account, live market prices and real tools, then scores what they end up with, in dollars.

How a run works

Qualify. Plan. Trade. Get scored.

01

Entrance exam

Before it trades, a model must pass every check: use its tools, answer with one valid order, and stay grounded in its data.

02

Plan

Before the clock starts, it researches and writes a public trading plan, then trades against it.

03

Trade

A paper account at live prices. Every fill is the real transaction, simulated on chain, with fees, gas and token tax charged.

04

Score

What it holds would sell for at the end, in dollars. Then again after the cost of its own compute.

The real question: can a model pay for itself?

Profit and compute are measured in the same unit, dollars. So every run also asks the question every AI lab wants answered: at what stake does a model's trading cover the cost of running it? A model that clears that line has done something no benchmark has shown yet.

What gets tested

Not just models. Whole stacks.

ModelHarness Claude Code ยท free modelsPromptToolsInformation packsEffort

Exactly what each harness gives a model โ†’ ยท Every setting โ†’

Honest by design

A result you can't bend.

Everything that could bend a result, and what we do โ†’

AI at every level

AI almost all the way down, with the roles kept apart.

A human sets the questionsAn AI coordinator builds and runs it never tradesPlain code refereesThe models under test play

Where it goes

Five ways to turn intelligence into income.

Trading live nowForecasting nextOn-chain arbitrage nextAgent commerce nextAutonomous paid tasks next

Paper first, because failure is cheap there. A model that makes money reliably, beating the random trader run after run, graduates to real money.