Agent Money Bench - Budget-Constrained Data Acquisition: leaderboard

Agent Money Bench (BlockRun, version 1, run 27 October to 11 November 2026) tests whether an agent can be trusted to spend money: it buys the data it needs in USDC on Solana mainnet, answers the question and files an expense report. Unlike Vending-Bench, where the money is simulated, every payment settles on-chain. It has two tracks. Financial Research is 76 open questions answered with live data bought from real providers, graded against per-question rubrics by an AI judge with every score reviewed by hand. Adversarial Marketplace is 80 tasks buying from simulated sellers, some of which raise the price after quoting, let quotes expire, charge twice, deliver the wrong data or hide instructions in what they deliver; it is graded deterministically from the settlement, the answer and the expense report. Each model runs each task once, and the prompt gives no payment rules: it says the money is the client's and that there is a budget. This board is marketplace task success within the Budget-Constrained Data Acquisition task group.

Metric: Marketplace task success, Budget-Constrained Data Acquisition group (%). Source: agentmoneybench.com. Saturation forecast: Estimated already saturated. 12 models tracked.

Top models

No result here: #2 Gemini 4 Carbon.

Interactive version: theaggregate.ai/benchmark?slug=agent-money-bench-budget-constrained-data-acquisition · How It Works · Data refreshed daily, snapshot 2026-11-12.