I wrote that section and made the graphs, so you can blame me. We no doubt highlight the evals that make us look good, but in this particular case I think the emphasis on telecom isn't unprincipled cherry picking.
Telecom was made after retail & airline, and fixes some of their problems. In retail and airline, the model is graded against a ground truth reference solution. But in reality, there can be multiple solutions that solve the problem, and perfectly good answers can receive scores of 0 by the automatic grading. This, along with some user model issues, is partly why airline and retail scores haven't climbed with the latest generations of models and are stuck around 60% / 80%. Even a literal superintelligence would probably plateau here.
In telecom, the authors (Barres et al.) made the grading less brittle by grading against outcome states, which may be achieved via multiple solutions, rather than by matching against a single specific solution. They also improved the user modeling and some other things too. So telecom is the much better eval, with a much cleaner signal, which is partly why models can score as high as 97% instead of getting mired at 60%/80% due to brittle grading and other issues.
Even if I had never seen GPT-5's numbers, I like to think I would have said ahead of time that telecom is much better than airline/retail for measuring tool use.
Incidentally, another thing to keep in mind when critically looking at OpenAI and others reporting their scores on these evals is that the evals give no partial credit - so sometimes you can have very good models that do all but one thing perfectly, which results in very poor scores. If you tried generalizing to tasks that don't trigger that quirk, you might get much better performance than the eval scores suggest (or vice versa, if they trigger a quirk not present in the eval).
Telecom was made after retail & airline, and fixes some of their problems. In retail and airline, the model is graded against a ground truth reference solution. But in reality, there can be multiple solutions that solve the problem, and perfectly good answers can receive scores of 0 by the automatic grading. This, along with some user model issues, is partly why airline and retail scores haven't climbed with the latest generations of models and are stuck around 60% / 80%. Even a literal superintelligence would probably plateau here.
In telecom, the authors (Barres et al.) made the grading less brittle by grading against outcome states, which may be achieved via multiple solutions, rather than by matching against a single specific solution. They also improved the user modeling and some other things too. So telecom is the much better eval, with a much cleaner signal, which is partly why models can score as high as 97% instead of getting mired at 60%/80% due to brittle grading and other issues.
Even if I had never seen GPT-5's numbers, I like to think I would have said ahead of time that telecom is much better than airline/retail for measuring tool use.
Incidentally, another thing to keep in mind when critically looking at OpenAI and others reporting their scores on these evals is that the evals give no partial credit - so sometimes you can have very good models that do all but one thing perfectly, which results in very poor scores. If you tried generalizing to tasks that don't trigger that quirk, you might get much better performance than the eval scores suggest (or vice versa, if they trigger a quirk not present in the eval).
Here's the tau2-bench paper if anyone wants to read more: https://arxiv.org/abs/2506.07982