Originally published on AI Tech Connect.
What you need to know Reasoning models — the ones that spend test-time compute thinking before they answer, emitting long chains of thought along the way — have changed what a good evaluation looks like. For years the default question was simple: was the final answer right? For a model that writes two thousand tokens of working before it commits to a number, that question is no longer sufficient. A model can land on the correct answer through reasoning that is riddled with errors, and it can produce mostly-valid reasoning that stumbles at the last step into a wrong answer. If your eval only reads the final token, both of those cases look identical to their opposites, and you learn nothing about whether the model can be trusted when the answer is not independently checkable. This guide…
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