AI models can solve formidable math problems, trounce humans at programming competitions, and outperform doctors. But can they help people make sound decisions when the evidence is ambiguous and the stakes are high? How do we know they are helping us reason better, not just faster? To find out, we built BioDecisionBench, a collection of 40 task variants derived from 26 cases of complex reasoning failures in life sciences. The tasks span the drug-development process, from target selection to clinical trial design to choosing new indications for a successful drug. High-stakes decision-making under ambiguity and limited information is an acute problem in drug development. A company only gets to pick a small set of targets to pursue, a limited number of assays to run, and a single clinical trial design to conduct. It’s not possible to explore all avenues, so the only way to do better is to reason better about how things might have gone differently if they had chosen differently. Existing benchmarks don’t meet this challenge: they tend to ask narrow questions, and they only evaluate final answers, rather than evaluating the reasoning that led to the answer.