Astra and Fable 5.1 can ace a graduate-level science exam, but no public benchmark will tell you whether a model knows where you’d put a comma or how many ideas belong on a slide. Today, Every CEO Dan Shipper explains why we’re building a personal benchmark for every employee, head of evals Mike Taylor shares how a run on his own benchmark convinced him a smaller model could handle much of his daily work, and we offer a five-step workflow for turning the corrections you already give AI into checks for grading any model.

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Inside Every

Astra and Fable 5 scored 96 percent and 93 percent, respectively, on a test of graduate-level science questions. Impressive, clearly! What’s less clear from public benchmarks is how those scores translate to what most of us care about: how well these models help us do our jobs.

The solution, says Dan, is to create a personal benchmark—a set of custom evals that tests how well a model does specific parts of your work, graded against your own standard for what good looks like.