Is Opus 5.5 nerfed? Thinking patterns changed on November 3(twitter.com)
140 pointsby rzk3 hours ago44 comments
  • Zambyte5 minutes ago
    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.
    • jesse_dot_ida few seconds ago
      It's because the frontier models are superior and much faster.
  • benwills2 hours ago
    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.

    • jdcasale2 hours ago
      I've been using Fable for a couple months as a relatively independent, long-horizon task executor and it worked well. About two weeks ago it completely stopped working. Like ~85% success rate down to ~10% in my workflows.

      Totally possible that I'm just hallucinating/projecting/etc or my workflows somehow changed in terms of difficulty, but it's been kind of shocking.

      • cromkaan hour ago
        I have the same exact experience. Working on one project, brainstorming. It was brilliant in July and August, like a seriously smarter, more experienced version of myself, reading in my mind, one step ahead of me, when pointed out a mistake it would remedy on the spot with the exact brilliant solution.

        Now it's like talking to my hungover self, after 3 days bender, who just wanted to be left alone.

      • Roark66an hour ago
        Same thing for me, but the day Fable got lobotomised Opus 5.5 started actually working pretty well for me.
  • jarym21 minutes ago
    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.

  • ricardobeatan hour ago
    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.

    • amelius12 minutes ago
      > It is very frustrating.

      Not just frustrating, it can also be dangerous. What if you trust Claude with certain security-sensitive things (because experience), and suddenly you cannot trust it anymore like that?

  • gherkinnn7 minutes ago
    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.
  • shikck2002 hours ago
    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.

    • cromkaan hour ago
      It's not LLMs! Its their providers! If you self-hosted, you'd observe the slow down but you'd get same quality.
    • KeplerBoyan hour ago
      It's probably not token usage, but a flexible 5 hour limit. There's a reason why they only state it in percentages.
    • monster_truckan hour ago
      They're actually extremely predictable. What you are experiencing is the provider you are entrusting with your time, effort, and money fucking you because they need the capacity to train the next one. So without any real notice or indication other than things vaguely seeming broken in ways everyone seems to be experiencing differently, they're serving up some shitty quant of what you already paid to use.
      • singularity200127 minutes ago
        Extremely predictable is an exaggeration. I was teaching machine learning and we needed half a dozen lines to make even simple networks deterministic. LLMs are not really predictable, even if we want to, they will sometimes behave almost chaotic depending on the input.
        • bmacho13 minutes ago
          > we needed half a dozen lines to make

          6 lines per model? Can't you put that 6 lines in a function/macro/decorator/library so you only have to call it?

        • monster_truck24 minutes ago
          I'm not sure how you're arriving at "if you use the same seed with the same version of the software on the same hardware with the same drivers you get the same result is an exaggeration", it's wild that you're a teacher and you seemingly do not know this.
  • ghoul22 hours ago
    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.
  • aurad42 minutes ago
    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.

    • jarym17 minutes ago
      > & had to spend 1.5k on API usage

      You rewarded Anthropic's failure with more dollar spend. I get why you did it but...

  • setnone11 minutes ago
    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
  • Retro_Dev3 hours ago
    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.

    • Gareth3212 hours ago
      This has happened to consistently across OpenAI and Anthropic models that I can't believe people are still denying it. Obviously they're silently quantising their models or making other efficiency changes. If they can save 40% RAM and only hurt intelligence by a few percent in their benchmarks, they're going to do that. Unfortunately their benchmarks are obviously not representative of the real world, and these downgrades are very noticeable for people who rely on these models for work.

      Of all the reasons to look at local inference, this is the one which pushes me in that direction the most. I want to rely on a model for consistency in my workflows. If I wake up on Monday and suddenly it's refactoring things it built on Friday, that's a huge problem. That makes it unreliable.

      • yfontanaan hour ago
        > I can't believe people are still denying it

        Probably because those "nerfs" do not show on sites that track model performance over time. Sometimes Anthropic/OpenAI mess stuff up in their harness, but usually not in the models themselves.

        • xscottan hour ago
          It's easy to imagine they can detect when they're being profiled and route accordingly. Paranoia or not, it's possible.
      • fastball2 hours ago
        I guess the question really is: is this malice (trying to trick people into thinking new model is better by nerfing old one), or is this capacity constraints, where they obviously need to make room in their infra for inference of the new model by shoving the other models into a smaller box?
        • timschmidtan hour ago
          Little of column A, little of column B.
      • grey-area2 hours ago
        These machines are inherently unreliable.
  • xscott2 hours ago
    With open weights, YOU get to decide when the model changes.
    • beebmam2 hours ago
      Not true at all, there's much more to a model than its weights.
      • digitalPhonix2 hours ago
        Like?
        • phyalow2 hours ago
          Thinking budgets…
          • digitalPhonixan hour ago
            In the context of this question, that's still something you as the user control.

            Specifically

            > YOU get to decide when the model changes

      • xyzsparetimexyz2 hours ago
        Yeah obviously but you can still control it.
        • f6van hour ago
          You can still control it if you can trivially spin up a node with eight B200s and 2TB of memory to run your Deepseek v4 instance. Nobody on a subsidized plans is doing that. Local models that fit 128GB memory don't come close even to a "nerfed" frontier model.
          • Roark66an hour ago
            Actually Qwen3.8-flash-next comes very close to a "nerfed frontier model" the way I host it locally at 4.75bpw with int8 kv. It is pretty much equivalent to opus 4.6 up to 4.8 on coding. What it lacks is some factual knowledge - this is to be expected, it's a far smaller model. But if you run the kind of coding tasks where you have to spec them in high detail anyway (large very custom environment) this model is very usable.

            I run it on a 6*rtx3090 consumer PC (no threadripper etc sadly) with very constrained pcie topology (4 cards sit on 2 lanes basically) and i still get 7k prefill and 50t/s single user or 200t/s 7 users. All at full 262k contexts of course and with 96GB of ram kv cache that means I can easily have 10+ Claude code sessions open with 1-7 generating at any time.

            I've been the person that said, before opus 4.6 came out that this AI stuff is absolute crap for programming. Then it suddenly wasn't and I was using it many hours each day.

            Local AI was the same. It was all crap. Models like GLM5.3 Flash that are many times larger than that qwen, lots of people are oooing and ahhing over I found way too inferior. Previous DeepSeek V4.1 Flash 0731 was atrocious. They benchmaxed it to hell, but at least it was small.

            The current model of same name but release 1017 and double the size is currently the best open source model IMO. It even beats my self hosted Qwen3.8-flash-next.

            I've tried hosting that DeepSeek model, but even adding another rtx3090 for a total of 7 cards and my 192gb of ram bought when it cost under $1000 I get only about 25t/s decode and few hundred prefill. That is far too slow for me to use.

            But, I believe once this pyramid scheme of circular financing finally bursts there will be a glut of hardware on the market where people like me will be able to buy equipment to run 750B models like we now run 120B.

            • Sphax42 minutes ago
              Just curious, did you put all these cards in a server or desktop case ? and what kind of motherboard ?

              Also, how much power does that consume at full tilt ?

            • DeSinc20 minutes ago
              I've been very impressed by it and recommended it to everyone. 32gb ram, 12gb 3080ti, and I run 3.8 flash next coder (half the experts ripped out, leaving in coding) int8 kv 128k context at 35-55tk/s and I agree with the opus comparison; I personally say 4.7 is what it seems like the most.

              Truly insane times

          • Tepixan hour ago
            For Deepseek V4 Flash (not 4.1), you can use two 128GB unified memory boxes and run it at the official weights (mostly 4bit) with lots of context.

            That's around 1% of the price of 8x B200. At current prices, it's as low as $6200. At last year's prices that's $3700 or so.

          • 44 minutes ago
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  • amelius9 minutes ago
    Nerf means something else in deep learning parlance.
  • terminalcommand2 hours ago
    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.
  • anonzzzies11 minutes ago
    I guess it’s AB testing and we are not on the bad side (yet).
  • Asmod4nan hour ago
    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
  • lukaxan hour ago
    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.
  • PostOnce18 minutes ago
    We are on the cusp of an intellectual obesity epidemic.
  • jonathanlydallan hour ago
    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.

  • xbmcuseran hour ago
    From what I understand they are picking and choosing who is running the latest models. So nerfed yes but for you not for everyone
  • fractalf37 minutes ago
    5.5 is useless below max effort, just frustrating and plain old wrong
  • throwaw12an hour ago
    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).

  • Cthulhu_4 minutes ago
    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.

  • peri-cl2 hours ago
    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.

    • MallocVoidstar37 minutes ago
      If Anthropic are doing A/B testing it almost certainly only affects subscription users, not API users. Every benchmark I know of uses the API.
      • peri-cl8 minutes ago
        IMHO the only defensible way to benchmark this is to use secret-shoppers via subscriptions. As is standard practice in other product-review fields, where the stakes are a lot lower.

        The purpose of AI benchmarking is to inform people making large spending decisions what alternatives are available to them.

        I *do* wish we wouldn't let software companies call this "A/B testing". That's not what that term means to a typical reader. In any other market than software, you'd call it something like "misleading consumers in respect to product composition" and "an unfair commercial practice". That's verbiage from the EU "dual quality" food scandal, where transnational food brands (Coca-Cola was one) adulterated their recipes to use cheaper ingredients in select markets, though the external packaging was kept the same.

        https://ec.europa.eu/commission/presscorner/detail/en/qanda_...

        (I suppose Coca-Cola's CEO would prefer we talk about what they did as "A/B testing").

      • dstu17 minutes ago
        If I were a cynic I’d also say that it wouldn’t be particularly difficult for them to detect when a benchmark/eval context is active and to route those requests to larger models or higher quants.
  • etcetcetcetcetaan hour ago
    I suppose they can achieve a benchmark then nerf the model to save compute once they've got the headline.
  • 0x70ddan hour ago
    Ok, I'm not going crazy then. Noticed that it is slower and results were of a lower quality.
  • gib44411 minutes ago
    Does nerfing usually come before a new model release?
  • hugodan22 minutes ago
    all good as long as it keeps killing young girls in the middle east
  • igleriaan hour ago
    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.

  • PUSH_AXan hour ago
    Fable 5.1 quality took a significant dive for me last week. I'm not imagining it.
  • tammyselvi2 hours ago
    not as bad as sol 6.1. im getting better results out of glm 5.3
    • cromkaan hour ago
      I was down voted into oblivion when I said that 3 weeks ago. Both Codex and Claude are absolutely nothing compared to what it was.
  • sunaookami2 hours ago
    It's just a new summarizer, the model and performance itself is unchanged.
    • kolinko2 hours ago
      +1, this tells nothing about the quality. Or even whether this is an upgrade or downgrade.
  • listedb2 hours ago
    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
  • perks_12an hour ago
    Welcome to the world of post-IPO AI. Now the 20% growth needs to come somewhere, and profitability would be a nice starter.
  • hebelehubele2 hours ago
    They're preparing to release a new model, so it has to look better than the current one.
  • davidd_10042 hours ago
    Seems fine to me
  • Madmallard17 minutes ago
    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.

  • Giorgi2 hours ago
    Yes, I noticed it too, dumbing down continues, there goes their IPO.
  • FastDeepan hour ago
    [flagged]
  • 1STORM324 minutes ago
    [dead]
  • novoreorx16 minutes ago
    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.
  • tsukikagean hour ago
    LiveNerf suggests not.

    https://github.com/ninjahawk/livenerf

    • overflowyan hour ago
      My paranoia tells me they A/B test users which means consistent benchmarks go through the high performance pipeline while regular requests fall back to the quantized / experimental / low cost version. I can see the main reason being the oldest one in the book, which is making more money.
      • musha68kan hour ago
        It could also be a regional effect where systems are under higher load and hence dynamically routing to / provisioning lower precision instances; or some other factor where one is affected whereas someone else isn't.
      • eithedan hour ago
        It's not paranoia and would align with https://www.reddit.com/r/ClaudeCode/comments/1xr9x5y/the_ner...

        I wonder as well whether would this pop up on nerfbench