Is Opus 5.5 nerfed? Thinking patterns changed on November 3

I don't have any evidence/study to substantiate this but I have the feeling there's a _massive_ amount of fiddling with the quant/functionality of the models at different stages of the release cycle and account freshness.

Every time a new model drops it feels wildly powerful, then regresses to some sort of generic SOTA level we've been used to over the last 6 months.

I remember my first coding prompt with, I think it was sonnet 4.6, on a fresh account and it was shocking how much it successfully did off a single prompt - I haven't found that with any other model at any other time and I suspect it was because they put me at the top of the priority list to get a "wow" reaction.

Regression after a release is pretty well-agreed upon, but I'm wondering if they secretly have a huge amount in the tank they aren't releasing. Either as a card to play in the future, or because they can't keep up with demand. I've heard anecdotes of people working in these companies saying that the public models aren't even close to their actual capabilities... Anyone have any opinion on this?

The local Qwen 3.8 27B might not be anywhere near as smart as Claude's models but I never wake up one morning and wonder if I am part of some test model to see if I will accept something somewhat less capable or not trained as well in the tools I am using day to day. Its also not suddenly going to start refusing to test the security of my system or scanning the code for vulnerabilities or continuing to back engineer that old binary the company lost its code for years ago.

It is something I also appreciate with some of the other labs like deepseek who publish the checkpoint they are running on and still sell you the old one for a period of time as well.

Its obnoxiously anticonsumer to be constantly chopping and changing what a model represents on customers who are paying them an awful lot more to be on the frontier models.

    $ free -m
                   total        used        free      shared       buff/cache   available
    Mem:         3095421       48746      819943         554       2242686     3046674
    Swap:          84390        2594       81796

According to our measurements this isn't true. We re-ran Opus 5.5 today on the benchmark we ran in September, with the same prompt and settings. Before and after, side by side: https://playcode.io/blog/macbook-svg-benchmark#reruns

Max, streamed (128K cap): 128,000 tokens, $2.56, empty file, in September and today.

Max, Batch API (300K): 255,957 tokens for $2.56 in September, 243,707 for $2.44 today. The two drawings: https://playcode.io/marketing/assets/static/claude-opus-5-5-... and https://playcode.io/marketing/assets/static/claude-opus-5-5-...

Xhigh: 86,096 tokens for $1.72 in September, 107,333 for $2.15 today.

The Batch API bills half price. Max ran out of the 128K ceiling again, to the token. Given 300K, it drew the same kind of laptop with 5% fewer tokens. Xhigh used 25% more and drew the same kind of laptop. That's inside the noise: 17 identical calls at high ranged from 17,757 to 59,705 tokens.

The model is still the best of everything we've tried. Most likely the next best would be Fable 5.2. But we don't like Fable because essentially the result is the same, just the price is higher. Its medium and high are close to what Opus 5.5 Max had, but the price is higher.

(I run Playcode, an AI app builder. We benchmark every model before we add it.)

Anyone with evals picking up a real signal? I've learned not to trust "vibes".

The only one I'm familiar with testing Opus 5.5 specifically over time is https://github.com/ninjahawk/livenerf and it's not showing anything.

It seems to be an A/B testing experiment:

https://www.reddit.com/r/ClaudeCode/comments/1xr9x5y/the_ner...

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.

- 1 week before releasing new model, they nerf the old. Probably to reserve compute for the new release. (insert your evil corp motive here). This is the third time I have seen this now.

- They run A/B tests continually. Hence the "how is Claude doing: good/okay/bad" question.

- They try to shrink the model while keeping performance, using quants, etc. they keep the name though. Opus at the first, is often not the same Opus a month later.

- My friend works at a top 100 spender on Anthropic. He complained about nerfing to his point-of-contact at Anthropic, and they did some setting change and it didn't happen anymore.

- He, European, says the models can get worse around 4pm, which is when America gets to work

Personally, I feel that Opus 5.5 is starting to behave like the old Opus 5. Lately, the model has been using a lot more of the compact, hard-to-read jargon that is typical in Opus 5

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.

I had a baseline evaluation from before Nov 3, so I re-ran it this morning.

Eval context: 30 bug fixes and features evaluated by tests the agent didn't see. Ran on Opus 5.5 at medium effort in Claude Code.

20 of the tasks were last run on Nov 1, twice each. Nov 1 solved 36/40, and today solved 19/20. Thinking tokens per task were 0.95x Nov 1’s and output tokens 0.97x. The one significant change was speed: median time to first token decreased from 3.0s to 1.7s.

Caveats: one run per task today, medium effort only.

Its coming a time that these commercial offerings will have to provide independent daily benchmarking to ensure their numbers align every day, with the risk of being called out for defrauding customers.

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.

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.

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

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 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?

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 agree, I also noticed visible drop in quality, a week before I was able to ship two apps and when I was working on the third (which is almost as much big as the first) it started acting weird, I even started a new session thinking it was because of hallucination... It did give me the output but burnt through my tokens earlier than I expected. It was even missing and getting confused between branches... All in all doing more mistakes... One point I would like to bring to your attention is, it has started using background agents a lot lately (maybe because I prompted it to use Sonnet models for scripting and Opus will supervise, but still it was better in the earliest week.)

With open weights, YOU get to decide when the model changes.

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.

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 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.

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.

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.

Isn’t this just like when people think their IPhones start getting sluggish whenever a new IPhone model is about to be released?

I have a small eval set that I rerun every week with my own tasks. That's the only way I've found so far to distinguish between model changes and having a bad day.

Sadly this thing stopped running a few weeks ago, the propduct half life is truly low these days otherwise wed have some data to rely on: https://marginlab.ai/trackers/claude-code/

Nerf bench tracking model benchmark performance over time: https://www.bridgebench.ai/nerf-bench

The way Anthropic continues to get away with testing these nerfing "experiments" on customers without their consent depriving them of the model quality they've paid for is wrong.

The Windows Insider program has shown plenty of people will willingly sign up to be testers for a taste of the rawhide. Anthropic could do this in a transparent way but they continue to choose not too.

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.

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.

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.

What I'm surprised by is that no-one has mentioned that FAANG companies run experiments (often multiple overlapping ones) on 99% of the traffic. You're almost never getting "vanilla". And Anthropic is full of talent from those places.

If you look at any sort of tracker, there's no telling whether you're in the same experiment group as the tracker (most likely not).

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.

I have noticed a mass lobotomy of Anthropic models recently. The frustrating part is that the target keeps changing and however you work with it today, it can fail tomorrow.

I've noticed I've been running into more safety guard rails the last few days. I've been using fable 5.1 for scientific lit search (e.g., on topic X, give me a table with links to the top 20 papers, and then major criticisms and rebuttals with direct links to the papers, etc.) to just save me time in getting acquainted with a new sub-topic. The last couple of days it's not just been dropping me from 5.1 to opus 5.5, but all the way to opus 5, stating that it's for safety reasons.

Sometimes I feel like the model is not necessarily needed, but the harness might be. Asking questions/permissions more often, delegating less, creating fewer sub agents. Just overall making workflows slower/harder to review. The main model might be just as smart/fast, bur it’s like the agentic side that starts crapping out

I noticed a massive quality change a week or so ago. Major downgrade

Exactly the reason I had previously cancelled my openai subscription

I'm looking into Gemini

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/

It has been nerfed for a long time now. Anyone who tried original Fable 5, knows how good that was, and all subsequent models, including current Fable and Opus have definitely been nerfed. It's like they add a mandatory 5% error added to all code, etc...

It's hard to tell, but it felt the same to me. Either way, Fable 5.2 just came out so abnormalities might have caused by infra-side changes related to that.

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 could explain needing to blow through my limits last week. So much of it’s outputs on xhigh needed serious revision, and took much longer than usual to meet specs.

I’m only prototyping so most of the code will be discarded, but even in this circumstances you can’t allow too much sprawl and decoherence within a single project. It makes prototyping cost so much more over time. It was pretty frustrating.

If this is real, it is straight up violation of implicit contract. When you pay for a service you expect stable and reliable performance. Intentional degradation of service is basically fraud.

We are on the cusp of an intellectual obesity epidemic.

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.

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.

Somewhat related:

Livenerf: Has Opus 5.5 been nerfed yet? - https://news.ycombinator.com/item?id=49901736 - Sept 2026 (394 comments)

Anecdotes and conspiracies and mass hysteria abound imo. And probably a bit of astroturfing from competitors. Models are not deterministic, nor are your projects. I work on code constantly for years - and across every anthropic and openai model people claim the same thing - yet I've never experienced this degredation, not even one hint of it. I would be acutely aware of it.

Variable token usage? Sure. To be expected in non-deterministic systems like these.

Distinct increase in errors and issues? Never. Not within a single model's lifespan have I ever experienced this.

And this whole notion of the A/B testing of performance/usage is completely ridiculous. There is zero evidence it has anything to do with performance. There are a trillion things to A/B test besides things that hamstring groups of users, so this is just being used to confirm the bias of people already caught up in these "theories."

I await any realistic indication.

Usage limits on Max plan also feels like its reduced. I now approach my usage limits much faster.

Yes, it feels like that as earlier, the "low" effort was able to do the great work for which now I need to use "medium".

Feeling like it's better on my end :shrug:

On some long running conversations it seems to better understand the end goal, follow the constraints we have established.

Has anyone used their new tool FlashProve to verify the weights haven't changed?

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

It's very frustrating, even at max effort. It used to do things correctly on its own, now I need to keep reminding it to do obvious things like "Make sure you are not dropping steps as you rewrite the docs". It became unreliable, like how Opus 5 was.

I feel strongly that GPT-6 Luna has been nerfed. I was super-impressed by it in the beginning, now it seems to think overly much and get absolutely nowhere with even very simple tasks.

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

not as bad as sol 6.1. im getting better results out of glm 5.3

A official statement is much required

I assume that arbitrary rerouting won't occur with hosted inference like AWS Bedrock?

I’m wary of this whole "watermarking" thing. I don’t believe it won’t end up having a negative impact on the quality of the responses.

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.

Same here. I've noticed that the output quality has changed about two weeks ago, in the latest days the thinking quality is super low.

It's just a new summarizer, the model and performance itself is unchanged.

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.

I look at these threads as the other side of the "LLMs are super-intelligence" movement. If my superhuman champion has disappointed me in some way, it must be nefarious interference. The unpredictability of LLMs is immanent in the technology, but the movement elides this for its own purposes.

Isn't this just the tried-and-true planned obsolescence playbook? The frontier labs need to capture as much value as they can from the next "hit", and that includes eliminating competition where they can. Including when it's their own model. I have zero evidence for this, but it seems patently obvious: Nerf Claude to drive usage to their next model, where they compound value and (theoretically) can serve more efficiently with each generation.

This tool I use to code/think/write for me doesn't work as well as it used to. Can I just go back to coding/thinking/writing myself?

Model releases remind me of https://commons.wikimedia.org/wiki/File:Shepard_Tone_spectru...

You know what doesn’t get nerfed? DeepSeek. Costs me maybe 0.50 per day if I’m using it a lot.

I just don’t see the point of using these expensive models anymore.

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.

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”

They probably nerfed their internal compute usage to justify their data center spend to the new shareholders now that they're IPOing.

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

5.5 became useless last week, so frustrating

They always nerf before they drop a new model to make it look better imho

Ok, I'm not going crazy then. Noticed that it is slower and results were of a lower quality.

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.

Yeah because fable 5.2 is here

> "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.

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?

People who never tried outsourcing before are discovering that there are risks associated with outsourcing.

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.

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

I suppose they can achieve a benchmark then nerf the model to save compute once they've got the headline.

Yeah I realized some decrease performance on htmls it generates

You have to keep the model unchanged from when you released it. Changes have to be mandated to roll over to a new version. Otherwise all of this is useless, you have to atleast be “deterministic” with the model capabilities.

Fable 5.1 quality took a significant dive for me last week. I'm not imagining it.

The quantization will continue until version number increases.

I noticed. Seems to be worse now.

I guess it’s AB testing and we are not on the bad side (yet).

They're preparing to release a new model, so it has to look better than the current one.

Note to future self: When nerfing is detected it might be resources being allocated towards the next impending model release.

Nerf means something else in deep learning parlance.

Wasn’t there a platform that kept testing that ?

Welcome to the world of post-IPO AI. Now the 20% growth needs to come somewhere, and profitability would be a nice starter.

I also noticed a change. Normally it said, "Found XYZ. Now I do ABC". Now it says "Found XYZ. Now I will do ABC." I liked the robotic "Now I do ABC" better.

There's this ongoing theory that Anthropic makes it's model dumber on purpose to make the difference noticeable with new model release. It has been documented by someone, you could see performance being dropped

I thinks more likely ya'll are experiencing a phenomenon that has been discussed on here before.

At the model release people become high/manic/addicted to the newest model think they now have some new superpower and then that "high" wears off and you think the model isnt as impressive.

The truth is llms have a slot machine-esque UX to them, and the labs designed it this way.

Dont worry you'll get your new model with some mystical name any day now and a month or two afterwards you'll be posting about how it no longer feels as good to use.

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.

LiveNerf suggests not.

https://github.com/ninjahawk/livenerf

Namber leelavathi

Ever since I started using Claude at its inception this was the biggest breach of trust that I've experienced.

I'm afraid to have touch my code base because a nerfed model could slip in stupid bugs.

This is also why a weaker local model might be preferable because it allows me to calibrate trust, and my expectations don't change.

I have had Astra analyze my long running Opus conversations and the degradation definitely happened on November 6th and 7th.

Does nerfing usually come before a new model release?

These kinds of threads always make me feel like I'm watching some satire. Pure consumerism, like the people that used to fight in line on Black Friday. Maybe Anthropic is nerfing the models, why are you so addicted to them that you care?

It's gone pretty shit, I made a comment about this yesterday: https://news.ycombinator.com/item?id=50412897

Instead of being Opus on high (effort), it's Opus is high.

Yes, I noticed it too, dumbing down continues, there goes their IPO.

all good as long as it keeps killing young girls in the middle east

Seems fine to me

Anecdotally, it's currently feeling nerfed. The model keeps restating my intent as if it had thought of it. Output accuracy is slightly down, but the increased friction in communication is having an impact on my worfklow. Very annoying. Also, it flat out ignored system claude.md several times over the weekend. These observations hold between my personal pro and works enterprise.

I am curious. How do people figure this out?

I use Sonnet 5.5 daily and Opus rarely. My usage is also a little involved as in I do all the design and strategy and get Sonnet to just write the code

For that, Sonnet has been consistent

I use Opus or Fable when it's a dumpster fire situation where we have no time to think even

So far I've never had an issue with Sonnet even.

People are already so used to have genies and unicorns around they now feel infuriated when they are a bit slower than before.

Dude, you have a mythical creature at your disposal at retail, chill, it's going to be ok.

i’m so tired of this

all the measurements always say it isn’t

anyone who grew up playing dungeons and dragons gets that this is just people saying “you don’t understand, these five used to roll high”

every llm space is inundated with rank amateurs explaining at great length how it used to be better yesterday

nobody has the courage to clean out the obvious bullshit, so the drone gets ever louder and the signal gets ever rarer

do you remember what programming was two years ago?

stop complaining so much

Can we get a self-hostable distilled opus 5.5 yet please?

This company does not deserve to be successful and has no moral high-ground.

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Each new model version hits your dopamine receptors very hard when it first comes out, but in the nature of things it stops hitting after a while and you need novel models to fire them off again. That's why people think the models are getting nerfed after a couple of weeks; it's actually their endocrine system getting nerfed.

Is there a practical reason this link uses twitter.com rather than x.com, or is it a nod to the old name? If it’s the latter, I don’t really see the point.

5.5 is useless below max effort, just frustrating and plain old wrong

IPO is coming so they will have to maximize profits going forward which will mean constraining the compute they give everyone to ensure the subscriptions make money.