Claude Code daily benchmarks for degradation tracking

I wonder when I experience noticeably degraded model quality, ie opus, is it because my usage falls in the highest buckets and I’m being shadow limited or served worse versions of opus or is it because of actual server load/burden?

It wouldn’t be the first time companies have secret shadow algorithms running to optimize things and wouldn’t it be obvious to limit power users as matter of cost/profit and not tell them. (See history of “Shadow ban” though that’s for different reasons)

The primary (non malicious, non stupid) explanation given here is batching. But I think you would find looking at large-scale inference the batch sizes being ran on any given rig are fairly static - there is a sweet spot for any given model part ran individually between memory consumption and GPU utilization, and generally GPUs do badly at job parallelism.

I think the more likely explanation is again with the extremely heterogeneous compute platforms they run on.

> We model tests as Bernoulli random variables and compute 95% confidence intervals around daily, weekly, and monthly pass rates. Statistically significant differences in any of those time horizons are reported.

They're going to need to provide a lot more detail on their methodology, because that doesn't make a lot of sense. From their graphs, they seem to be calculating the confidence interval around the previous value, then determining whether the new value falls outside of it. But that's not valid for establishing the statistical significance of a difference. You need to calculate the confidence interval of the difference itself, and then see if all the values within that confidence interval remain positive (if it excludes 0). This is because both the old and new measurement have uncertainty. Their approach seems to be only considering uncertainty for one of them.

They should also really be more specific about the time periods. E.g. their graphs only show performance over the past 30 days, but presumably the monthly change is comparing the data from 60 to 31 days ago, to the data from 30 days ago until yesterday? In which case the weekly graph really ought to be displaying the past two months, not one month.

I too suspect the A/B testing is the prime suspect: context window limits, system prompts, MAYBE some other questionable things that should be disclosed.

Either way, if true, given the cost I wish I could opt-out or it were more transparent.

Put out variants you can select and see which one people flock to. I and many others would probably test constantly and provide detailed feedback.

All speculation though

Benchmark tracking of cloud AI performance is going to be crucial going forward. Vendors are selling a service that by its nature is very difficult for customers to gauge day to day. How will I know if a code revision is ~2.5% less good today than it would have been yesterday? Or if queries during peak load hours use one less 'expert' in their MoE?

Yet vendor's costs to deliver these services are skyrocketing, competition is intense and their ability to subsidize with investor capital is going away. The pressure on vendors to reduce costs by dialing back performance a few percent or under-resourcing peak loads will be overwhelming. And I'm just a hobbyist now. If I was an org with dozens or hundreds of devs I'd want credible ways to verify the QoS and minimum service levels I'm paying for are being fulfilled long after a vendor has won the contract.