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Claude Code daily benchmarks for degradation tracking

marginlab.ai

51–60 of 372 posts

Re: Claude Code daily benchmarks for degradation tracking

#51
post #12

Simply search user prompts for curse words and then measure hostility sentiment. User hostility rises as agents fail to meet expectations.

Maybe im overlooking something obvious but how do you 'simply' scan the content of Claude users their prompts?

Re: Claude Code daily benchmarks for degradation tracking

#52

[SWE-bench co-author here] It seems like they run this test on a subset of 50 tasks, and that they only run the test once per day. So a lot of the movement in accuracy could be attributed to that. I would run on 300 tasks and I'd run the test suite 5 or 10 times per day and average that score. Lots of variance in the score can come from random stuff like even Anthropic's servers being overloaded.

but degradation from servers being overloaded would be the type of degradation this SHOULD measure no? Unless it's only intended for measuring their quietly distilling models (which they claim not to do? idk for certain)

noob question: why would increased demand result in decreased intelligence?

Re: Claude Code daily benchmarks for degradation tracking

#53

[SWE-bench co-author here] It seems like they run this test on a subset of 50 tasks, and that they only run the test once per day. So a lot of the movement in accuracy could be attributed to that. I would run on 300 tasks and I'd run the test suite 5 or 10 times per day and average that score. Lots of variance in the score can come from random stuff like even Anthropic's servers being overloaded.

"Lots of variance in the score can come from random stuff like even Anthropic's servers being overloaded"

Aha, so the models do degrade under load.

Re: Claude Code daily benchmarks for degradation tracking

#54
Does it benchmark the underlying code (Opus 4.5) or Claude Code harness? If the second, I would love to see CC versions involved.

I would be curious to see on how it fares against a constant harness.

There were thread claiming that Claude Code got worse with 2.0.76, with some people going back to 2.0.62. https://github.com/anthropics/claude-code/issues/16157

So it would be wonderful to measure these.

Re: Claude Code daily benchmarks for degradation tracking

#55

Wouldn't be surprised if they slowly start quantizing their models over time. Makes it easier to scale and reduce operational cost. Also makes a new release have more impact as it will be more notably "better" than what you've been using the past couple of days/weeks.

Oooff yes I think that is exactly the kind of shenanigans they might pull.

Ultimately I can understand if a new model is coming in without as much optimization then it'll add pressure to the older models achieving the same result.

Nice plausible deniability for a convenient double effect.

Re: Claude Code daily benchmarks for degradation tracking

#56

Earlier quoted context omitted.

but degradation from servers being overloaded would be the type of degradation this SHOULD measure no? Unless it's only intended for measuring their quietly distilling models (which they claim not to do? idk for certain)

I've personally witnessed large variability in behaviour even within a given session -- which makes sense as there's nothing stopping Anthropic from shuttling your context/session around load balanced through many different servers, some of which might be quantized heavily to manage load and others not at all. I don't know if they do this or not, but the nature of the API is such you could absolutely load balance thi…

I've defended opus in the last weeks but the degradation is tangible. It feels like it degraded by a generation tbh.

Re: Claude Code daily benchmarks for degradation tracking

#57
I am using API mode, and it's clear that there are times when the Claude model just gives up. And it is very noticeable because the model just does the most dumb things possible.

"You have a bug in line 23." "Oh yes, this solution is bugged, let me delete the whole feature." That one-line fix I could make even with ChatGPT 3.5 can't just happen. Workflows that I use and are very reproducible start to flake and then fail.

After a certain number of tokens per day, it becomes unusable. I like Claude, but I don't understand why they would do this.

Re: Claude Code daily benchmarks for degradation tracking

#58

Why is this happening?

They're "optimizing" costs wherever possible - reducing compute allocations, quantizing models, doing whatever they can to reduce the cost per token, but vehemently insisting that no such things are occurring, that it's all in the users' heads, and using the weaseliest of corporate weasel speak to explain what's happening. They insist it's not happening, then they say something like "oh, it happened but it was an accident", then they say "yes, it's happening, but it's actually good!" and "we serve the same model day by day, and we've always been at war with Eastasia."

They should be transparent and tell customers that they're trying to not lose money, but that'd entail telling people why they're paying for service they're not getting. I suspect it's probably not legal to do a bait and switch like that, but this is pretty novel legal territory.

Re: Claude Code daily benchmarks for degradation tracking

#59
First off, this is a cool project, look forward to some interesting insights.

I would suggest adding some clarification to note that longer measure like 30 pass rate is raw data only while the statistically significant labels apply only to change.

Maybe something like Includes all trials, significance labels apply only to confidence in change vs baseline.

Re: Claude Code daily benchmarks for degradation tracking

#60

Earlier quoted context omitted.

but degradation from servers being overloaded would be the type of degradation this SHOULD measure no? Unless it's only intended for measuring their quietly distilling models (which they claim not to do? idk for certain)

noob question: why would increased demand result in decreased intelligence?

It would happen if they quietly decide to serve up more aggressively distilled / quantised / smaller models when under load.
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