We dropped DiligenceBench, a new frontier, rubric-based eval for public-equity research.

A few observations:

1/ Meta Muse Spark 1.1 tops the finance harness at 57.4%, followed by GLM 5.2, Sonnet 4.6, and GPT-5.6 Sol.

2/ We found that strong models benefit primarily from generic tools that unlock execution, while weaker models require more opinionated, domain-specific scaffolding.

3/ Inkling appears to be domain-competence bottlenecked. The generic sandbox barely improved its performance, from 20.9% to 22.5%, suggesting that tool access alone was not enough. The finance harness then lifted it to 32.8%, with the largest gain coming from factual accuracy. This makes the value of a harness model-dependent.

4/ The finance harness shifts the price–performance frontier: it makes most models simultaneously better and cheaper, with GLM 5.2 leading on absolute performance and MiniMax M3 offering the strongest overall efficiency.