Introducing DataBench

Hex introduces DataBench, a 100-task benchmark designed to evaluate AI agents on realistic analytics work rather than narrowly specified SQL questions, using a synthetic but intentionally messy $129 million business environment called Shorelane Commerce. The benchmark includes direct Q&A, open-ended recommendation tasks, and “trap” cases where plausible evidence supports an incorrect conclusion, with evaluations judged against detailed rubrics and ground-truth data where applicable. Results suggest that frontier models are often capable of navigating complicated warehouses, reconciling definitions, and conducting detailed forensic analysis when user intent is clear, while GPT-5.6 Luna showed strong cost-performance efficiency and Claude Fable 5 was the only tested model whose higher effort levels consistently improved outcomes. However, models performed less reliably when tasks required judgment, causal reasoning, uncertainty acknowledgement, or detection of incomplete data, and some models became less accurate at high effort by overcomplicating otherwise correct answers. The authors argue that human oversight, careful prompting, and follow-up remain important for consequential analytical decisions, and plan to expand DataBench while eventually open-sourcing the Shorelane environment.

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