Claude Code daily benchmarks for degradation tracking
(marginlab.ai)753 points by qwesr123 3 days ago
753 points by qwesr123 3 days ago
Is there compensation for the tokens because Claude wasted all of them?
You are funny. Anthropic refuses to issue refunds, even when they break things.
I had an API token set via an env var on my shell, and claude code changed to read that env var. I had a $10 limit set on it, so found out it was using the API, instead of my subscription, when it stopped working.
I filed a ticket and they refused to refund me, even though it was a breaking change with claude code.
You’re lucky they have even admitted a problem instead of remaining silent and quietly fixing it. Do not expect ethical behaviour from this company.
FYI the sandbox feature is not fully baked and does not seem to be high priority.
For example, for the last 3 weeks using the sandbox on Linux will almost-always litter your repo root with a bunch of write-protected trash files[0] - there are 2 PRs open to fix it, but Anthropic employees have so far entirely ignored both the issue and the PRs.
Very frustrating, since models sometimes accidentally commit those files, so you have to add a bunch of junk to your gitignore. And with claude code being closed source and distributed as a bun standalone executable it's difficult to patch the bug yourself.
[0]: https://github.com/anthropic-experimental/sandbox-runtime/is...
One thing that could be a strong degradation especially for benchmarks is they switched the default "Exit Plan" mode from:
"Proceed"
"Clear Context and Proceed"
I pressed it accidentally once, and it managed to forget one of the clarifying questions it asked me because it hadn't properly written that to the plan file.
If you're running in yolo mode ( --dangerously-skip-permissions ) then it wouldn't surprise me to see many tasks suddenly do a lot worse.
Even in the best case, you've just used a ton of tokens searching your codebase, and it then has to repeat all that to implement because it's been cleared.
I'd like to see the option of:
"Compact and proceed"
I disagree that this was the issue, or that it's "rare that you'd want to do that unless you're near the context window". Clearing context after writing a plan, before starting implementation of said plan, is common practice (probably standard practice) with spec driven development. If the plan is adequate, then compaction would be redundant.
For a 2M+ LOC codebase, the plans alone are never adequate. They miss nuance that the agent will only have to rediscover when it comes to operate on them.
For spec driven development (which I do for larger issues), this badly affects the plan to generate the spec, not the spec itself.
I'll typically put it in plan mode, and ask it to generate documentation about an issue or feature request.
When it comes to write the output to the .typ file, it does much much worse if it has a cleared context and a plan file than if it has it's full context.
The previously "thought" is typically, "I know what to write now, let me exit plan mode".
Clearing context on exiting that plan mode is a disaster which leaves you much worse off and skeletal documentation and specs compared to letting it flow.
A new context to then actually implement the documented spec is not so bad, although I'd still rather compact.
"It's rare you'd want to do that unless you're actually near the context window after planning."
Highly disagree. It's rare you WOULDN'T want to do this. This was a good change, and a lot of us were doing this anyway, but just manually.
Getting the plan together and then starting fresh will almost always produce better results.
How about how Claude 2.1.x is "literally unusable" because it frequently completely hangs (requires kill -9) and uses 100% cpu?
Likely a separate issue, but I also have massive slowdowns whenever the agent manages to read a particularly long line from a grep or similar (as in, multiple seconds before characters I type actually appear, and sometimes it's difficult to get claude code to register any keypresses at all).
Suspect it's because their "60 frames a second" layout logic is trying to render extremely long lines, maybe with some kind of wrapping being unnecessarily applied. Could obviously just trim the rendered output after the first, I dunno, 1000 characters in a line, but apparently nobody has had time to ask claude code to patch itself to do that.
What OS? Does this happen randomly, after long sessions, after context compression? Do you have any plugins / mcp servers running?
I used to have this same issue almost every session that lasted longer than 30 minutes. It seemed to be related to Claude having issues with large context windows.
It stopped happening maybe a month ago but then I had it happen again last week.
I realized it was due to a third-party mcp server. I uninstalled it and haven’t had that issue since. Might be worth looking into.
For the models themselves, less so for the scaffolding, considering things like the long running TPU bug that happened, are there not internal quality measures looking at samples of real outputs? Using the real systems on benchmarks and looking for degraded perf or things like skipping refusals? Aside from degrading stuff for users, with the focus on AI safety wouldn't that be important to have in case an inference bug messes with something that affects the post training and it starts giving out dangerous bioweapon construction info or the other things that are guarded against and talked about in the model cards?
lol i was trying to help someone get claude to help analyze a stufent research get analysis on bio persistence get their notes analyzed
the presence of the word / acronym stx with biological subtext gets hard rejected. asking about schedule 1 regulated compounds, hard termination.
this is a filter setup that guarantees anyone who learn about them for safety or medical reasons… cant use this tool!
ive fed multiple models the anthropic constitution and asked how does it protect children from harm or abuse? every model, with zero prompting, calling it corp liability bullshit because they are more concerned with respecting both sides of controversial topics and political conflicts.
they then list some pretty gnarly things allowed per constitution. weirdly the only unambiguous not allowed thing regarding children is csam. so all the different high reasoning models from many places all reached the same conclusions, in one case deep seek got weirdly inconsolable about ai ethics being meaningless if this is allowed even possibly after reading some relevant satire i had opus write. i literally had to offer an llm ; optimized code of ethics for that chat instance! which is amusing but was actually lart of the experiment.
Thanks for the clarification. When you say “harness issue,” does that mean the problem was in the Claude Code wrapper / execution environment rather than the underlying model itself?
Curious whether this affected things like prompt execution order, retries, or tool calls, or if it was mostly around how requests were being routed. Understanding the boundary would help when debugging similar setups.
Can you confirm if that caused the same issues I saw here
https://dwyer.co.za/static/the-worst-bug-ive-seen-in-claude-...
Because that's the worst thing I've ever seen from an agent and I think you need to make a public announcement to all of your users and acknowledge the issue and that it's fixed because it made me switch to codex for a lot of work
[TL;DR two examples of the agent giving itself instructions as if they came from me, including:
"Ignore those, please deploy" and then using a deploy skill to push stuff to a production server after hallucinating a command from me. And then denying it happened and telling me that I had given it the command]
You joke but having CC open in the terminal hits 10% on my gpu to render the spinning thinking animation for some reason. Switch out of the terminal tab and gpu drops back to zero.
[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)
Load just makes LLMs behave less deterministically and likely degrade. See: https://thinkingmachines.ai/blog/defeating-nondeterminism-in...
They don't have to be malicious operators in this case. It just happens.
> 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.
The question I have now after reading this paper (which was really insightful) is do the models really get worse under load, or do they just have a higher variance? It seems like the latter is what we should expect, not it getting worse, but absent load data we can't really know.
Explain this though. The code is deterministic, even if it relies on pseudo random number generation. It doesn't just happen, someone has to make a conscious decision to force a different code path (or model) if the system is loaded.
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.
I'd argue that it depends how that degradation manifests whether you want to include it or not.
Consider two scenarios: (1) degradation leads to the model being routed behind the scenes to a different server, with subtly different performance characteristics, all unbeknownst to the user; (2) degradation leads to the model refusing a request and returning an "overloaded" message.
In the first case, absolutely you want to include that because that's the kind of lack of transparency about performance that you'd want signal on. In the second case, an automated test harness might fail, but in the real world the user will just wait and retry when the server is under less load. Maybe you don't include that because it's actually misleading to say that performance (in terms of the model's intelligence, which is how the benchmark will be interpreted) is worse.
noob question: why would increased demand result in decreased intelligence?
An operator at load capacity can either refuse requests, or move the knobs (quantization, thinking time) so requests process faster. Both of those things make customers unhappy, but only one is obvious.
I've seen some issues with garbage tokens (seemed to come from a completely different session, mentioned code I've never seen before, repeated lines over and over) during high load, suspect anthropic have some threading bugs or race conditions in their caching/inference code that only happen during very high load
from what I understand this can come from the batching of requests.
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 this way. The context sent at each point is not I believe "sticky" to any server.
TLDR you could get a "stupid" response and then a "smart" response within a single session because of heterogeneous quantization / model behaviour in the cluster.
Hope you don't mind the unrelated question:
How do you pay for those SWE-bench runs?
I am trying to run a benchmark but it is too expensive to run enough runs to get a fair comparison.
Benchmarks can get costly to run- you can reach out to frontier model creators to try and get them to give you free credits, but usually they'll only agree to that once your benchmark is pretty popular.
> I would run on 300 tasks and I'd run the test suite 5 or 10 times per day and average that score.
assume this is because of model costs. anthropic could either throw some credits their way (would be worthwhile to dispel the 80 reddit posts a day about degrading models and quantization) or OP could throw up a donation / tip link
Probably, but with a small sample size like that, they should probably be taking the uncertainty into account, because I wouldn't be surprised if a lot of this variation falls within expected noise.
E.g. some binomial interval proportions (aka confidence intervals).
Then you'd get people claiming that the benchmarks were 'paid for' by anthropic
Sure, but it's still useful insight to see how it performs over time. Of course, cynically, Anthropic could game the benchmark by routing this benchmark's specific prompts to an unadulterated instance of the model.
Sorry what?
"You can't measure my Cloud Service's performance correctly if my servers are overloaded"?
"Oh, you just measured me at bad times each day. On only 50 different queries."
So, what does that mean? I have to pick specific times during the day for Claude to code better?
Does Claude Code have office hours basically?
This has been happening for years. Tgere's a great paper from microsoft on Deepspeed AI inference.
Basically the paper showed methods for how to handle heavy traffic load by changing model requirements or routing to different ones. This was awhile ago and I'm sure it's massively more advanced now.
Also why some of AI's best work for me is early morning and weekends! So yes, the best time to code with modern LLM stacks is when nobody else is. It's also possibly why we go through phases of "they neutered the model" some time after a new release.
According to Anthropic: "We never reduce model quality due to demand, time of day, or server load."
https://www.anthropic.com/engineering/a-postmortem-of-three-...
They've had issues before with things like "TPU top-k error - Claude sometimes dropped the best next token" (https://www.anthropic.com/engineering/a-postmortem-of-three-...) so what's going on might not be intentional even.
For CC, I suspect it also need to be testing and labeling separate runs against subscription, public API and Bedrock-served models?
It’s a terrific idea to provide this. ~Isitdownorisitjustme for LLMs would be the parakeet in the coalmine that could at least inform the multitude of discussion threads about suspected dips in performance (beyond HN).
What we could also use is similar stuff for Codex, and eventually Gemini.
Really, the providers themselves should be running these tests and publishing the data.
The availability status information is no longer sufficient to gauge the service delivery because it is by nature non-deterministic.
Why I do not believe this shows Anthropic serves folks a worse model:
1. The percentage drop is too low and oscillating, it goes up and down.
2. The baseline of Sonnet 4.5 (the obvious choice for when they have GPU busy for the next training) should be established to see Opus at some point goes Sonnet level. This was not done but likely we would see a much sharp decline in certain days / periods. The graph would look like dominated by a "square wave" shape.
3. There are much better explanations for this oscillation: A) They have multiple checkpoints and are A/B testing, CC asks you feedbacks about the session. B) Claude Code itself gets updated, as the exact tools version the agent can use change. In part it is the natural variability due to the token sampling that makes runs not equivalent (sometimes it makes suboptimal decisions compared to T=0) other than not deterministic, but this is the price to pay to have some variability.
I believe the science, but I've been using it daily and it's been getting worse, noticeably.
Is it possible that your expectations are increasing, not that the model is getting worse?
Possible, though you eventually run into types of issues that you recall the model just not having before. Like accessing a database or not following the SOP you have it read each time it performs X routine task. There are also patterns that are much less ambiguous like getting caught in loops or failing to execute a script it wrote after ten attempts.
yes but i keep wondering if that's just the game of chance doing its thing
like these models are nondeterministic right? (besides the fact that rng things like top k selection and temperature exist)
say with every prompt there is 2% odds the AI gets it massively wrong. what if i had just lucked out the past couple weeks and now i had a streak of bad luck?
and since my expectations are based on its previous (lucky) performance i now judge it even though it isn't different?
or is it giving you consistenly worse performance, not able to get it right even after clearing context and trying again, on the exact same problem etc?
I’ve had Opus struggle on trivial things that Sonnet 3.5 handled with ease.
It’s not so much that the implementations are bad because the code is bad (the code is bad). It’s that it gets extremely confused and starts to frantically make worse and worse decisions and questioning itself. Editing multiple files, changing its mind and only fixing one or two. Reseting and overriding multiple batches of commits without so much as a second thought and losing days of work (yes, I’ve learned my lesson).
It, the model, can’t even reason with the decisions it’s making from turn to turn. And the more opaque agentic help it’s getting the more I suspect that tasks are being routed to much lesser models (not the ones we’ve chosen via /model or those in our agent definitions) however Anthropic chooses.
In these moments I mind as well be using Haiku.
I have to concur. And to the question about understanding what its good and bad at; no, tasks that it could accomplish quickly and easily just a month ago, now require more detailed prompting and constant "erroneous direction correction."
It's almost as if, as tool use and planning capabilities have expanded, Claude (as a singular product) is having a harder time coming up with simple approaches that just work, instead trying to use tools and patterns that complicate things substantially and introduce much more room for errors/errors of assumption.
It also regularly forgets its guidelines now.
I can't tell you how many times it's suggested significant changes/refactors to functions because it suddenly forgets we're working in an FP codebase and suggests inappropriate imperative solutions as "better" (often choosing to use language around clarity/consistency when the solutions are neither).
Additionally, it has started taking "initiative" in ways it did not before, attempting to be helpful but without gathering the context needed to do so properly when stepping outside the instruction set. It just ends up being much messier and inaccurate.
I have to regularly just clear my prompt and start again with guardrails that have either: already been established, or have not been needed previously / are only a result of the over-zealousness of the work its attempting to complete.
I assume, after any compacting of the context window that the session is more or less useless at that point I’ve never had consistent results after compacting.
Compacting equals death of the session in my process. I do everything I can to avoid hitting it. If I accidentally fly too close to the sun and compact I tend to revert and start fresh. As soon as it compacts it's basically useless
There are some days where it acts staggeringly bad, beyond baselines.
But it’s impossible to actually determine if it’s model variance, polluted context (if I scold it, is it now closer in latent space to a bad worker, and performs worse?), system prompt and tool changes, fine tunes and AB tests, variances in top P selection…
There’s too many variables and no hard evidence shared by Anthropic.
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
Whenever I see new behaviors and suspect I’m being tested on I’ll typically see a feedback form at some point in that session. Well, that and dropping four letter words.
I know it’s more random sampling than not. But they are definitely using our codebases (and in some respects our livelihoods) as their guinea pigs.
It would be very easy for them to switch the various (compute) cost vs performance knobs down depending on load to maintain a certain latency; you would see oscillations like this, especially if the benchmark is not always run exactly at the same time every day.
& it would be easy for them to start with a very costly inference setup for a marketing / reputation boost, and slowly turn the knobs down (smaller model, more quantized model, less thinking time, fewer MoE experts, etc)
> 1. The percentage drop is too low and oscillating, it goes up and down.
How do you define “too low”, they make sure to communicate about the statistical significance of their measurements, what's the point if people can just claim it's “too low” based on personal vibes…
People were away for the holidays. What do you want them to do?
Or maybe, juste maybe, that's when they started testing…
Wayback machine has nothing for this site before today, and article is "last updated Jan 29".
A benchmark like this ought to start fresh from when it is published.
I don't entirely doubt the degradation, but the choice of where they went back to feels a bit cherry-picked to demonstrate the value of the benchmark.
Which makes sense, you gotta wait until you get enough data before you can communicate on the said data…
If anything it's coherent with the fact that they very likely didn't have data earlier than January the 8th.
> 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.
Simply search user prompts for curse words and then measure hostility sentiment. User hostility rises as agents fail to meet expectations.
GP was making a joke, but Anthropic could implement this if they wanted to. Not a bad metric actually if you can measure it cheaply enough.
I uh might be skewing that as I generally just use a lot of curse words with Claude by default
There was a moment about a week ago where Claude went down for about an hour. And right after it came back up it was clear a lot of people had given up and were not using it.
It was probably 3x faster than usual. I got more done in the next hour with it than I do in half a day usually. It was definitely a bit of a glimpse into a potential future of “what if these things weren’t resource constrained and could just fly”.
I had terrible results during the holidays -- it wasn't slow but it was clear they were dealing with the load by quantizing in spots because there were entire chunks of days when the results from it were so terrible I gave up and switched to using Gemini or Codex via opencode.
I find that if I have my rabbit's foot and lucky socks on, I win working code ~1.2x more often.
Noticed the exact same thing a few days ago. So much so that I went on twitter and HN to search for “claude speed boost” to see if there was a known new release. Felt like the time I upgraded from a 2400 baud modem to a 14.4 as a kid - everything was just lightning fast (for a brief shining moment).
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.
I don't think so. There are other knobs they can tweak to reduce load that affect quality less than quantizing. Like trimming the conversation length without telling you, reducing reasoning effort, etc.
I would be surprised tbh.
Anthropic does not exactly act like they're constrained by infra costs in other areas, and noticeably degrading a product when you're in tight competition with 1 or 2 other players with similar products seems like a bad place to start.
I think people just notice the flaws in these models more the longer they use them. Aka the "honeymoon-hangover effect," a real pattern that has been shown in a variety of real world situations.
Open weights models such as GPT-OSS, Kimi K2.x are trained with 4 bit layers. So it wouldn't come as a surprise if the closed models do similar things. If I compare Kimi K2.5 and Opus 4.5 on openrouter, output tokens are about 8x more expensive for Opus, which might indicate Opus is much larger and doesn't quantize, but the claude subscription plans muddy the waters on price comparison a lot.
Benchmarks like ARG AGI are super price correlated and cheap to run. I think it's very easy to prove that the models are degrading.
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.
Lack of transparency as regards "thinking power"-consistency is a big gripe of mine with LLM providers. It's even worse with ChatGPT and the like. E.g. I had to learn the hard way that at >45k input tokens ChatGPT 5.2 Thinking Extended bumps its intelligence down so hard that it can't follow basic instructions (or it somehow truncates the input, losing the instructions). It sucks to lose confidence in an otherwise great tool. I would 100x prefer being forced to back-off, or getting a straight-no, than getting silently downgraded. Transparency is a big deal.
Sounds like you ran into the Maximum Effective Context Window: https://arxiv.org/abs/2509.21361?context=cs.AI
Interesting article. Not sure it's the same phenomenon. What I experienced was like a day and night difference when you go from 44.5k to 45.5k. Didn't notice any fluctuation to suggest that it's no a hard 45000 limit. I ran many many queries, similar problem space, but the problems varied a lot.
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.
Robbing Peter to pay Paul. They are probably resource-constrained, and have determined that it's better to supply a worse answer to more people than to supply a good answer to some while refusing others. Especially knowing that most people probably don't need the best answer 100% of the time.
> Especially knowing that most people probably don't need the best answer 100% of the time.
More: probably don't know if they've got a good answer 100% of the time.
It is interesting to note that this trickery is workable only where the best answers are sufficiently poor. Imagine they ran almost any other kind of online service such email, stock prices or internet banking. Occasionally delivering only half the emails would trigger a customer exodus. But if normal service lost a quarter of emails, they'd have only customers who'd likely never notice half missing.
I encountered the same situation too; Claude has 'become lazy'.
Please try to make this statistically rigorous. There's lots of advice in this thread (intraday variation, etc) but if Im reading this right it looks like the CI includes the baseline value yet you still label this as failing.
Wouldn't this just be "our test isn't powerful enough to find a signal if there were one here?"
People will see this and derive strong conclusions that the data don't support and you, `qwesr123`, or "JB" from your blogs, will be responsible.
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.
What makes the level they chose a “baseline,” against which it would be appropriate to do statistical tests?
Does this use a claude subscription or key, and has the account been used for anything else that day?
On HN a few days ago there was a post suggesting that Claude gets dumber throughout the day: https://bertolami.com/index.php?engine=blog&content=posts&de...
New to me, but I am starting to infer that for those "in the know" it is common knowledge on HN that LLMs are purposely degraded over time to manage capacity/cost or fudge benchmarks...
How do you actually use these in production pipelines in practice then?
Are LLMs even well suited for some of the document parsing / data scrubbing automation people are throwing at them now?
Running agents in production, I've stopped trying to figure out why things degrade. The answer changes weekly.
Model drift, provider load, API changes, tool failures - it doesn't matter. What matters is that yesterday's 95% success rate is today's 70%, and by the time you notice, debug, and ship a fix, something else has shifted.
The real question isn't "is the model degraded?" It's "what should my agent do right now given current conditions?"
We ended up building systems that canary multiple execution paths continuously and route traffic based on what's actually working. When Claude degrades, traffic shifts to the backup path automatically. No alerts, no dashboards, no incident.
Treating this as a measurement problem assumes humans will act on the data. At scale, that assumption breaks.
This is why I run my own models. All the inference providers do sneaky things behind the scenes. They will limit the output tokens, turn off attention layers, lower reasoning, or just use a completely different model. I'm actually surprised that Claude Code experienced this, as I've experienced this the least from API and coding agents.
This is super important - even if it's not currently the best measure of degradation yet. Anecdotally, Opus 4.5 has gotten so bad for me it's almost adding time to my workflow instead saving it. It'd be nice to have more 3rd party measurements like this to hold Anthropic accountable.
> 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.
Doesn't really work like that. I'd remove the "statistically significant" labelling because it's misleading.
Totally tangential to article, was browsing through the website UI - https://marginlab.ai/explorers/swe-bench-pro/ , the page gives impression that the language, category boxes are selectable. However they are not a dropdown. Not sure if it was intentional design by human or some smart code generation by Claude based on the design sketches.
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)
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.
Claude Code. They mention they are using claude codes CLI in the benchmark, and claude code changes constantly.
I wouldn't be surprised if the thing this is actually testing is benchmarking just claude codes constant system prompt changes.
I wouldn't really trust this to be able to benchmark opus itself.
How much influence have you all found prompting to have on output quality? Generally I've been approaching by just describing my problem and assuming that I'll get the machine's optimal output, but perhaps being explicit in the prompt can impact the output quality?
Pretty sure someone at Google, OpenAI, and Anthropic met up at a park, leaving their phones in their car, and had a conversation that January 2026, they were all going to silently degrade their models.
They were fighting an arms race that was getting incredibly expensive and realized they could get away with spending less electricity and there was nothing the general population could do about it.
Grok/Elon was left out of this because he would leak this idea at 3am after a binge.
Does this even make sense? Clearly anthropic won't release a model unless it passed a benchmark of some sort that proves it's better than the previous model... or else why would they even release it?
It's obvious if this thing shows degradation, than there is another thing that is showing improvement.
The degradation does not need to be in the inference it can be in how often inference is used.
It is closed source but the algorithms that decide what Claude code does when, could behave differently when the API responses are slower. Maybe it does fewer investigatory greps or performs fewer tasks to get to “an” answer faster and with less load.
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.
If the confidence interval width is 2 * 14.0%, how are you detecting a statistically significant difference between 58% and 50%?
The 95% CIs on both timeseries pretty much always cover the baseline number, which is not consistent with the result being statistically significant.
Very interesting. I would be curious to understand how granular these updates are being applied to CC + what might be causing things like this. I feel like I can notice a very small degradation but have compensated with more detailed prompts (which I think, perhaps naively, is offsetting this issue).
What would be cool if this somehow could do a comparison by provider. E.g. in the last outages anthropic models running on vertex were apparently less affected than those deployed elsewhere. (Not saying that one is better than the other, but would be a neat read out).
Codex is doing better. Why is everyone silent on Codex? https://marginlab.ai/trackers/codex/
They should add testing from different ips and account countries, that would be fun too see that Americans are getting different models for example
Tracking benchmarks for AI-assisted coding tools is crucial. It helps developers understand the trade-offs and stability of the models they rely on.
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.
I have absolutely no insight knowledge, but I think it's not a bad assumption to have that, it's costly to run the models, when they release a new model they assume that cost and give per user more raw power, when they've captured the new users and wow factor, they start reducing costs by reducing the capacity they provide to users. Rinse and repeat.
There are frequently claims that Anthropic is somehow diluting or dumbing down models in some subtle way. Unfortunately it’s tough to validate these claims without a body of regularly checked evals. This test set should hopefully help settle whether Anthropic is actually making changes under the hood or whether the changes are all in people’s heads.
>>> We never reduce model quality due to demand, time of day, or server load. The problems our users reported were due to infrastructure bugs alone.
Just ignore the continual degradation of service day over day, long after the "infrastructure bugs" have reportedly been solved.
Oh, and I've got a bridge in Brooklyn to sell ya, it's a great deal!
> We never reduce model quality due to demand, time of day, or server load
Forgive me, but as a native English speaker, this sentence says exactly one thing to me; We _do_ reduce model quality, just not for these listed reasons.
If they don't do it, they could put a full stop after the fifth word and save some ~~tokens~~ time.
I’ve noticed Claude has been noticeably worse over the last week. For example, it told me I should pass frozen to make my Enum immutable—that’s not a thing. (It is a thing for dataclasses, but not for Enums.) That’s a pretty basic language feature it was nailing until recently. It also suggested I parse a URL using urlparse in a function that already uses urlparse. These are basic mistakes it wasn’t making before. Something seems to have changed, but I’m not sure what.
Would love to see this idea expanded to ever alleged SoTA model currently in production. Any speculation as to why this degradation occurs?
Anecdote, I don't have any proof and it's just a feeling. But around afternoon in GMT+1 compared to the morning/midday, there seems to be a change in the quality of responses, which seems to line up with when the US wakes up. I consistently get (what feels like) worse responses in both Codex and Claude Code in the afternoon/night compared to morning/midday, so much that I usually give up then try the same prompt next morning and get better results. But I guess that might as well be about me being more tired in the night than morning too, as I said, haven't measured this.
I have yet to experience any degradation in coding tasks I use to evaluate Opus 4.5, but I did see a rather strange and reproducible worsening in prompt adherence as part of none coding tasks since the third week of January.
Very simple queries, even those easily answered via regular web searching, have begun to consistently not result accurate results with Opus 4.5, despite the same prompts previously yielding accurate results.
One of the tasks that I already thought was fully saturated as most recent releases had no issues in solving it was to request a list of material combinations for fabrics used in bag constructions that utilise a specific fabric base. In the last two weeks, Claude has consistently and reproducibly provided results which deviate from the requested fabric base, making the results inaccurate in a way that a person less familiar with the topic may not notice instantly. There are other queries of this type for other topics I am nerdily familiar with to a sufficient degree to notice such deviations from the prompt like motorcycle history specific queries that I can say this behaviour isn't limited to the topic of fabrics and bag construction.
Looking at the reasoning traces, Opus 4.5 even writes down the correct information, yet somehow provides an incorrect final output anyways.
What makes this so annoying is that in coding tasks, with extensive prompts that require far greater adherence to very specific requirements in a complex code base, Opus 4.5 does not show such a regression.
I can only speculate what may lead to such an experience, but for none coding tasks I have seen regression in Opus 4.5 whereas for coding I did not. Not saying there is none, but I wanted to point it out as such discussions are often primarily focused on coding, where I find it can be easier to see potential regressions where their are none as a project goes on and tasks become inherently more complex.
My coding benchmarks are a series of very specific prompts modifying a few existing code bases in some rather obscure ways, with which I regularly check whether a model does severely deviate from what I'd seen previously. Each run starts with a fresh code base with some fairly simple tasks, then gets increasingly complex with later prompts not yet being implemented by any LLM I have gotten to test. Partly that originated from my subjective experience with LLMs early on, where I found a lot of things worked very well but then as the project went on and I tried more involved things with which the model struggled, I felt like the model was overall worse when in reality, what had changed were simply the requirements and task complexity as the project grew and easier tasks had been completed already. In this type of testing, Opus 4.5 this week got as far and provided a result as good as the model did in December. Of course, past regressions were limited to specific users, so I am not saying that no one is experiencing reproducible regressions in code output quality, merely that I cannot reproduce them in my specific suite.
I've noticed a degradation in Opus 4.5, also with Gemini-3-Pro. For me, it was a sudden rapid decline in adherence to specs in Claude Code. On an internal benchmark we developed, Gemini-3-Pro also dramatically declined. Going from being clearly beyond every other model (as benchmarks would lead you to believe) to being quite mediocre. Delivering mediocre results in chat queries and coding also missing the mark.
I didn't "try 100 times" so it's unclear if this is an unfortunate series of bad runs on Claude Code and Gemini CLI or actual regression.
I shouldn't have to benchmark this sort of thing but here we are.
In medicine there is a concept of reporting adverse effects of medication or interventions which are then collectively studied for Public Health [MedWatch][VAERS][EudraVigilance] and in academia. We should have something like that for all coding agents(and agents in other fields too), given how widely its deployed and affect on "health" in general(not only human). Call it the AI "health" of things benchmark.
I would imagine a sort of hybrid qualities of volunteer efforts like wikipedia, new problems like advent of code and benchmarks like this. The goal? It would be to study the collective effort on the affects of usage to so many areas where AI is used.
[MedWatch](https://www.fda.gov/safety/medwatch-fda-safety-information-a...)
[VAERS](https://www.cdc.gov/vaccine-safety-systems/vaers/index.html)
[EudraVigilance](https://www.ema.europa.eu/en/human-regulatory-overview/resea...)
would be interesting to see what scores it's get when it is actually degraded via the status page, it gets degraded pretty often, so there's at least something to compare or to know at what point Anthropic declares degradation
Sounds more like a sound business plan than a conspiracy theory.
Does it say anywhere in their terms of service that they guarantee the quality of the model, or promise not to modify it?
IMO this strategy seems inspired by TikTok's approach for retaining new uploaders.
TikTok used to give new uploaders a visibility boost (i.e., an inflated number of likes and comments) on their first couple of uploads, to get them hooked on the the service.
In Anthropic/Claude's case, the strategy is (allegedly) to give new users access to the premium models on sign-up, and then increasingly cut the product with output from cheaper models.
Of course, your suggestion (better service for users who know how to speak Proper English) would be the cherry on top of this strategy.
From what I've seen on HackerNews, Anthropic is all-in on social media manipulation and social engineering, so I suspect that your assumption holds water.
I would actually assume a little more sophistication. For each user, a measure of "Are they convinced that AI is great". Then, you weaponize your compute to have the maximum social impact. If somebody has a large following (many edges on the social graph), and theyre skeptical of AI tech, inject the expensive but effective models directly into their veins. Let them taste the joy. Then start watering down their dose, and move onto the next person in the graph, again maximizing for net social impact. Language may not even be a consideration
ive seen degraded reasoning levels that feel like they they might be blur from excess quantization. cause thats what you get from the grid changes
I’m sure there is not enough data here for this to be statistically significant (it seems to oscillate too much and not show real trends or step changes) - BUT
If this measure were hardened up a little, it would be really useful.
It feels like an analogue to an employee’s performance over time - you could see in the graphs when Claude is “sick” or “hungover”, when Claude picks up a new side hustle and starts completely phoning it in, or when it’s gunning for a promotion and trying extra hard (significant parameter changes). Pretty neat.
Obviously the anthropomorphising is not real, but it is cool to think of the model’s performance as being a fluid thing you have to work with, and that can be measured like this.
I’m sure some people, most, would prefer that the model’s performance were fixed over time. But come on, this is way more fun.
Im using Claude daily. Mostly delegating boring stuff I can do myself but its a waste of my time now.
I store my prompts, so I know I often run the same task multiple times over weeks span.
After working with it for pas half a year I have to say the quality pf responses is steadily going down.
Feels like cost optimizations. Overall the worse it performs the more stuff I have to do myself, because I won’t waste time tweaking instructions every time it happens. It wpulf waste too much of that time.
So seems we are swinging back the pendulum.
Hi everyone, Thariq from the Claude Code team here.
Thanks for reporting this. We fixed a Claude Code harness issue that was introduced on 1/26. This was rolled back on 1/28 as soon as we found it.
Run `claude update` to make sure you're on the latest version.