LemonadeBench: Evaluating the Economic Intuition of Large Language Models in Simple Markets

Aidan Vyas
1/14/2026
q-fin.GNcs.AI

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

We introduce LemonadeBench v0.5, a minimal benchmark for evaluating economic intuition, long-term planning, and decision-making under uncertainty in large language models (LLMs) through a simulated lemonade stand business. Models must manage inventory with expiring goods, set prices, choose operating hours, and maximize profit over a 30-day period-tasks that any small business owner faces daily. All models demonstrate meaningful economic agency by achieving profitability, with performance scaling dramatically by sophistication-from basic models earning minimal profits to frontier models capturing 70% of theoretical optimal, a greater than 10x improvement. Yet our decomposition of business efficiency across six dimensions reveals a consistent pattern: models achieve local rather than global optimization, excelling in select areas while exhibiting surprising blind spots elsewhere.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{vyas2026lemonadebench,
  title  = {LemonadeBench: Evaluating the Economic Intuition of Large Language Models in Simple Markets},
  author = {Aidan Vyas},
  year   = {2026},
  eprint = {2602.13209},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.13209v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2602.13209.svg)](https://codeofpaper.com/paper/2602.13209)

Discussion