[bgirard]

> malicious

It doesn't have to be malicious. If my workflow is to send a prompt once and hopefully accept the result, then degradation matters a lot. If degradation is causing me to silently get worse code output on some of my commits it matters to me.

I care about -expected- performance when picking which model to use, not optimal benchmark performance.

[Aurornis]

Non-determinism isn’t the same as degradation.

The non-determinism means that even with a temperature of 0.0, you can’t expect the outputs to be the same across API calls.

In practice people tend to index to the best results they’ve experienced and view anything else as degradation. In practice it may just be randomness in either direction from the prompts. When you’re getting good results you assume it’s normal. When things feel off you think something abnormal is happening. Rerun the exact same prompts and context with temperature 0 and you might get a different result.

[bonoboTP]

This has nothing to do with overloading. The suspicion is that when there is too much demand (or they just want to save costs), Anthropic sometimes uses a less capable (quantized, distilled, etc) version of the model. People want to measure this so there is concrete evidence instead of hunches and feelings.

To say that this measurement is bad because the server might just be overloaded completely misses the point. The point is to see if the model sometimes silently performs worse. If I get a response from "Opus", I want a response from Opus. Or at least want to be told that I'm getting slightly-dumber-Opus this hour because the server load is too much.

[F7F7F7]

“Just drink the water, it’s all water.”

[novaleaf]

this is about variance of daily statistics, so I think the suggestions are entirely appropriate in this context.