Another (IMO fatal) error is they don’t attempt to measure within-model variance.
The thing you find when you actually wire up a rigorous eval is that with tool calls like web search you are wide open to infra issues, flakes, and all sorts of non-determinism.
They really should be breaking out the numbers for the 3 without search (kinda meaningless for recent factual claims after knowledge cutoff) vs search agents. Lack of a “I don’t know” option completely invalidates results for the non-search models; they are basically guessing what seems like a probable answer, since they don’t know and aren’t allowed to say that.
I do agree the forced choice and “weak / strong” variants inflate the headline stat. To make that distinction you need a much more rigorous prompt, likely including ICL examples to illustrate what you mean by “mostly” instead of leaving this to the model to define.
The thing you find when you actually wire up a rigorous eval is that with tool calls like web search you are wide open to infra issues, flakes, and all sorts of non-determinism.
They really should be breaking out the numbers for the 3 without search (kinda meaningless for recent factual claims after knowledge cutoff) vs search agents. Lack of a “I don’t know” option completely invalidates results for the non-search models; they are basically guessing what seems like a probable answer, since they don’t know and aren’t allowed to say that.
I do agree the forced choice and “weak / strong” variants inflate the headline stat. To make that distinction you need a much more rigorous prompt, likely including ICL examples to illustrate what you mean by “mostly” instead of leaving this to the model to define.