Top AI models score under 24% on finance benchmark

Surge released a new finance benchmark on September 25, and no model cleared a quarter of the possible score. Opus 5.5 topped the field at 23.9%, a number that leaves most of each task's checks unmet.

DAYJOB Finance has 80 expert-designed assignments meant to look like real analyst work, averaging 16.6 human-hours each to complete. Each one gets graded against a rubric with more than 100 pass/fail criteria.

GPT-6 Astra followed at 21.5%, and Fable 5.1 scored 19.8%. Surge says the same ceiling holds in a companion healthcare benchmark, where top models also stayed under 25%.

The stakes sit in the failure mode, not just the score. Surge published one example of a model missing a $25M error in a bond valuation. The mistake came from confusing cents with rand, a unit mix-up a junior analyst checking the numbers would likely catch.

Benchmarks like this exist because the usual coding and reasoning leaderboards have started to saturate, with frontier models bunching up near the top. Surge built these long, rubric-heavy tasks to find daylight between models on work that looks like an actual job, not a puzzle. On finance and healthcare, that daylight shows up as almost every model failing most of the time. Surge doesn't say which of the three models made the $25M error, so it's unclear whether the top scorer or a runner-up produced it.