alt Hacker NewsIs Opus 5.5 nerfed? Thinking patterns changed on November 3
Comments
I'm surprised why folks are either all in on frontier subs or self hosting on their own hardware.
To me it seems there should be a very viable middle ground of paying for open model hosting by a trusted provider - unless I'm only hearing from 24/7 vibe coders
I noticed Fable decline in quality a couple weeks ago. And I've noticed Opus drop over the last week, even after bumping to a default of Extra High reasoning for almost everything.
Tonight has been maddening with the kinds of absurd mistakes it's repeatedly making. It's so frustrating.
I'm the kind of person that wants something to be consistent and reliable. And Anthropic has been great, except for in this way. Which is enough of a recurring frustration (about every couple months this year) that I genuinely can't wait for other options to catch up. Then I'll be leaving as soon as I can.
I just don't know how long that will be.
People, refuse to be A/B tested!
If you paid for X but are now receiving Y, you should be entitled to get your money back.
Every time I consider going back to Claude, I remember that mysterious week in February when Claude was failing to do even the simplest things. Things were so bad that BigPickle was correcting errors Sonnet was making.
And let's not forget the king of A/B testing here. Google has no issues breaking products for months just to test some ridiculous idea.These people should be publicly fired.
I noticed the same, and posted about it three days ago.
Opus is suddenly fumbling basic tool calls and failing to finish tasks even at high, where I had been permanently using it on low effort with success since it came out. It is very frustrating.
My guess is they quantized it and/or reallocated DC capacity to train a new model, this has happened before.
It blows my mind how people still build their workflows around depending on services like these, with how easy it is to just self host and forget about all of this. You can buy a GPU that can run Qwen 3.8 flash next for about 9 months worth of the highest tier Claude Code subscription. You'll never have to worry about token limits, the model changing out from under you, getting banned, etc.
For me I've noticed the pattern of decline happening on Tuesday and Wednesdays. I find myself having to have Opus redo many steps and making a much larger number of errors on those days. I am able to be more productive on Thursdays just as my weekly reset approaches on Friday at 1 pm local time.
I don't have measured data aside from myself noticing my frustration level with Claude jumping on these days frequently over the past several months.
Token use exploded a week or two ago. I worked for a few hours and hit my 5 hour limit. Did only some simple bug fix. Somethings definitely broken.
This is the #1 thing i HATE with LLMs. They are not predictable like a compiler is.
As one user pointed out:
> I noticed this too as did many others. Had to bump my default reasoning but it’s not the same. Seems to often happen before new model drops
I heard that Qwen4 was dropping some time soon, I wonder if Anthropic is trying to announce a new model immediately after Qwen4 - both for their IPO, and to kill the competition.
Definitely noticed some anecdotal declines this past week or so. Missing steps it didn't previously, needing reminders of tool calls and skills that it should auto apply.
Lots of "this is a potential issue, I didn't check into it though" which is super frustrating to see. The model just points out major flaws in logic or barriers but goes no further than a comment on it.
I've got a Qwen coder model and a flash next model locally now, so it's not the end of the world. But it does need to be remedied - are we seeing them prep for a new model deploy when this happens? Or are they experimenting with leaner hardware to drive down costs ahead of IPO?
Some teams I know started moving all linting and validation into LLMs and markdown files, the reasoning was that they asked agent to review changes that were later found to be buggy and agents flagged these bugs. And that’s great but my immediate worry was that: it finds it now, but will you check every new model for all these bugs? And what if you need to switch providers? Or your provider silently tweaks the model to improve their margins? The answer was “well also build deterministic linters for all these problems”, but you know that’s just wishful thinking - if you can get 99.9% done with just a little bit of fuzzy text and the next 0.1% is a lot of work it won’t happen. So you have model behaviour lock-in that will bite you suddenly when you least expect it. Or maybe LLMs will just keep getting better and cheaper? It’s all a casino right now.
Definitely a significant change. I noticed it in just daily work. Quality of work down, mistakes up, repetitive errors in scripts - rare earlier, now a few every session.
We use Azure Foundry hosted Opus and it seems as capable as ever.
I run a small local GPU cluster for Qwen 3.8 Flash and while it's "free" (you should check electricity prices in EU...) unfortunately it doesn't even compare to Opus 5.5 or Astra in capabilities. It's like GPT 6 vs GPT ~4, but imagine GPT 4 is also 100x slower and sounds like an airplane taking off.
In the end it's totally possible that I would pay more for electricity running Qwen for 24 hours churning on some complex problem vs. Astra one-shotting it in 15 minutes.
I hope we get there, but it's not there yet, and I'm sure never will be with consumer GPUs. Maybe if you spend $20k+ on a local AI rig. There's just no market force to give access to AI to everyone at fair price.
I moved from Claude to Codex and am happier. The prose is less grating and results comparable. As much as I don't like Altman I am tired of Anthropic's antics.
I've seen big drops in Opus 5.5 (on high/x-high) & Fable. Both in terms of following instructions / workflows execution and overall code quality. Also the token burn-rate increased a lot, I've reached limits of 3 Ultra subscriptions during last week & had to spend 1.5k on API usage during the weekend just to be frustrated with the stupidity of the output.
Weirdly enough, Sonnet quality seems unchanged.
I guess it's just capacity problems. They probably have a fixed amount of compute and they need to make space for new models that will be released. So they quantize existing models e.g. in half so that can use 50% of current capacity for new models. They have such massive scale that you just cannot dynamically get that amount of new compute online so they need to work with what they have. No provider has thousands of GPUs idle.
Yes. That was the weekend my token usage blew up and used up my weekly allowance in a matter of hours. It was so stark.
After I figured out, like this chap on X, that it wasn't ME, I diversified my model usage and now a lot more work has shifted to Astra/Sol and I'm learning to make my workflows more resistant to the whims of each model.
This is not real model's reasoning chain, they might have just replaced or tuned the model that summarizes it. If you want to track actual degradation you need to run evals. Trackers like this do exist, e.g. https://aistupidlevel.info/
The reverse happen with Deepseek. Getting smarter and faster especially if you dont mind the China API. Gemini Argon getting dumber compare to older 3.8 and significantly slower too.
I am using Opus 5.5 high almost exclusively. I definitely felt this and looked for nerfing watch sites. It felt like downgrading to the old version of deepseek. Things have improved a bit now.
A official statement is much required
We need a consistent way to tell level of service for LLM services. Without they can play with us and steal money by providing different/quantized models than we paid for. I believe this may be a good next AI company if they will be reliable.
How can anyone build on this quicksand where you never know whether the same model will still be available tomorrow? The whole industry has become a casino.
We are on the cusp of an intellectual obesity epidemic.
From what I understand they are picking and choosing who is running the latest models. So nerfed yes but for you not for everyone
this is the reason i cancelled claude after 4.6, non-determinism aside, anthropic just doesn't seem care about maintaining sane baseline for their products
Hm, I’ve seen a degradation of 5.5 yesterday. I’ve had a session running for around 50 days and it just started to generate garbage and ignored all rules. A fresh one feels the same as always
If Anthropic indeed nerfed Opus 5.5 on November 3, it could be because they wanted to be prepared for inference cost going up due to the data center moratoriums and the election results. I really hope this is false, but I don't see how inference cost can go down in the near future.
Yeah I realized some decrease performance on htmls it generates
Anecdotal, but I tend to get by with medium thinking on Opus and I noticed that as of sometime last week it really did feel like it had become noticeably slower to think about things.
I wouldn't say I've especially noticed its effectiveness having been reduced, it's just slower.
I'm also noticing the model seems to be a lot more sloppy now and less reliable like compared to a week ago or so?
not as bad as sol 6.1. im getting better results out of glm 5.3
> "One Bug. Two effort levels."
Sounds like analysis fully driven by a model, which can be fine, if you know what you are getting into and can verify the output. Just last week had someone ask Claude Opus 5.5 on High about watermarking after I informed him and he'd rather believe a models output over me, do a basic Google search or spend the effort reading Anthropics communications on the matter.
The model told him with no uncertainty that watermarking text was not implemented in any Anthropic models, whereupon he showed me and I then asked him to just ask the model to check again, ideally online. These models do still "hallucinate", they still have major gaps in their output not easily caught if you don't know the topic and interestingly, anything connected to LLMs, model providers, etc. tends to be where they do worst.
The annoying thing is, they still sound immensely authoritative across most any topic, but especially LLM stuff, which is such an easy way to get tricked. I have been for sure in the past and will be in the future, it's why I recommend having some incredibly niche hobby (like sewing with less known materials, obscure motorcycles, etc.) to check how quickly the model makes a mistake which no one in the know would make. Just to keep yourself disillusioned and inoculated.
In this case, looks like the LLM did not consider that these are (and have been for months now) just summaries and the actual reasoning can not be analyzed for encryption reasons.
If in doubt, please do some research independent of the model output. LLMs not having any internal "understanding" that'd give them anything akin to an internalized "feel" for what they "know" accurately enough is one of their major flaws.
Funnily enough, once did the opposite, made a manual analysis of a Github outage, then had the model just write an artifact to quickly share. Was lazy, could have just put something on Pages, but taking summary changes and extrapolating that to a "nerf" is less ideal.
On the regression front, I run my own, private evals and have only once encountered consistently reproducible, over run-to-run deviation, degraded performance, which was purely present when using Claude Opus 4.8 in April after some system prompt changes in Claude Code specifically which were addressed soon thereafter. These were not present with OpenCode via API and any other time the public on Reddit and co. reported regressions, I could not reproduce it, even when using Claude Code as the harness and a subscription.
What I have encountered and been able to reliably reproduce is that certain labs do very clear teach to the public benchmarks and underperform them commonly, whereas others do not. And that certain, especially cheaper and higher throughput, neoclouds very clearly take some quality affecting, tool call misformatting shortcuts.
For what it's worth, currently measuring, but haven't seen anything so far with Opus 5.5, especially nothing connected to reasoning that'd make the assessments some of the commenters draw from this apparent.
It's just a new summarizer, the model and performance itself is unchanged.
as a user I believe it: they are probably reducing assigned compute to prepare to offer new models.
That would be the less bad reason.
LLMs are such a great business, they are non-deterministic, hence you can do whatever you want, because customers can't do anything about it:
* nerf them based on supply and demand, when you have a high demand, nerf it, but keep charging same price.
* do you want to show revenue growth to investors? increase thinking tokens, customers will keep paying, because again, this is non-deterministic system, sometimes it thinks more
* do you want to increase profits? start serving 4-bit, 5-bit quantizations and keep charging same price
* want even more? publish a paper "Advanced Auto Super Smart Prompt Router - AASSPiRation", and start routing requests to Qwen3.8-27B or GemmaE2B from time to time on the edge
* Not enough? introduce "Complex Chained Workflows - CCW" in your coding harness, which spawns multiple deterministic workflows (which you can't stop or control) and they will use Haiku to write the code, then use Opus 5.5 high to verify the code (which is obviously wrong) and Opus 5.5 produces recommendations on how to fix the code, delegates to Haiku, which again fails writing proper code, and keep doing it 5-6 times, you made lots and lots of money (don't forget, all should run in parallel, to pick one best result among 10 outputs).
Eagerly awaiting Artificial Analysis to redo their Anthropic benchmarks and kick it down 15-20 index points, in the interests of accuracy. Those guys love accuracy. They moved Heaven and Earth to kick Kimi off the #2 spot, out of their deep, abiding passion for having the most accurate benchmarks in the industry.
I have no doubt they will ensure their users get an accurate picture of Opus 5.5 within hours.
It’s A/B experiments at scale to see how many people put up with endless quantisation, a defence mechanism so they can say “hey all these wildly unjustifiable salaries for developers and management are totally unwild and justifiable, why look at how much we’ve slashed our inference costs”
Model releases remind me of https://commons.wikimedia.org/wiki/File:Shepard_Tone_spectru...
This'll likely be the reality moving forward - they allocate more compute at launch to get good test scores, reviews, and news coverge, they get people excited and used to it, then they cut costs.
I'm reminded of the video game industry where they would launch a game without any in-game purchases at launch, get the reviews to make note of it and give it a higher rating because of it... then add in-game purchases anyway. I think that stopped again though.
Pretty nice business model tbh. Whenever you need/feel like it, just tune it down.
Oh, even psychological effect would be nice if you slightly tune down the current frontier 1-2 weeks before releasing the new one.
I'm skeptical about nerfing. Are we saying they are instructing AWS to nerf the version of the model they're running on Bedrock?
If they did, there's no way that remains a secret.
If they didn't, Anthropic would be making their own service noticeably shitter than AWS Bedrock
People who never tried outsourcing before are discovering that there are risks associated with outsourcing.
The quantization will continue until version number increases.
I suppose they can achieve a benchmark then nerf the model to save compute once they've got the headline.
Ok, I'm not going crazy then. Noticed that it is slower and results were of a lower quality.
It's dumbing down a little bit but I don't think its a lot worse. I think the change in display adds to the feel
Wasn’t there a platform that kept testing that ?
It seems to be an A/B testing experiment:
https://www.reddit.com/r/ClaudeCode/comments/1xr9x5y/the_ner...