DeepFinance: Can AI diligence a $100M deal?

Julius Research introduces DeepFinance, an ultra long-horizon benchmark of 21 tasks built from real, anonymized Private Equity and Investment Banking workflows that take professionals 20–30 hours each, averaging 19 files per data room and 84 rubric criteria per task. Grading covers output, workflow and judgment, and on the leaderboard GPT-6 Astra (max) leads at 57.6%, followed by Claude Opus 5.5 (54.8%), Claude Sonnet 5.5 (54.6%) and GPT-6.1 Sol (54.3%), which are not statistically distinguishable, while the bottom group scores 10.9% to 14.5%. The authors report that agents often find decision-relevant evidence without acting on it and execute calculations correctly on wrong inputs, and argue that auditability of the trajectory matters as much as final numbers.