We also have Nerf Bench:<p><a href="https://www.bridgebench.ai/nerf-bench" rel="nofollow">https://www.bridgebench.ai/nerf-bench</a><p>They test it on launch day, then benchmark it against that. A deviation of above 10% is considered a change. They're currently tracking Opus 5.5 and GPT-6 Astra.<p>This bench famously detected a degradation of Opus 4.6 which Anthropic later blogged about. I personally think people sense nerfs more often than they happen and that it's often about honeymoon effects.
Used to work on a chat app where we had full control of the stack from the GPUs to the chat interface and everything in between. We were a small team too so I could be pretty confident that nothing changed in the stack and we still regularly had users complain that this or that model got nerfed. Perceived performance is actual performance over expectations and the latter just keeps increasing over time.<p>It doesn’t mean the big labs don’t also nerf models! But if they didn’t you’d still have users complaining.
For years I used to buy ASICS running shoes. Every year they released a new model of each shoe: "Nimbus 23", then "Nimbus 24" the next year, etc. And every year people would complain in the user reviews about how each shoe was worse than the last.<p>I was like, wow, I guess the shoes must be literal torture devices full of MRSA-covered broken glass at this point. They've been getting continuously worse for 24 consecutive years!<p>Of course, what was really happening is that they were not getting worse, but naturally every year there was some small percentage of vocal dissatisfied users, while the silent majority simply enjoyed their shoes and didn't have much to say about them.<p>(The sorta-opposite happens in sneaker reviews as well. People will gush about how cushy the sole in some particular new sneaker is. Well, yeah, <i>of course</i> it's cushy -- you're comparing a new sneaker to your old sneaker where the foam had lost its bounce...)
I wonder how much of that is caused by folks actually noticing actual degradation in product quality over time (whether or not this exact product is suffering from it).<p>Shrinkflation is a thing, which people suddenly started noticing in the past 5 years.<p>There's also "the Schlitz Mistake", which I've heard summarized as "most customers won't notice if you take your product's quality from A to B (or C), but they definitely will if you take it from A to J (or A to M)"<p>At this point I kinda assume that any company releasing year updates to a physical product that _doesn't_ take the opportunity to trim costs / reduce quality would be vulnerable to a shareholder lawsuit for leaving money on the table...
> Shrinkflation is a thing, which people suddenly started noticing in the past 5 years.<p>It's been much longer than that: <a href="https://en.wikipedia.org/wiki/Toblerone#2016_size_changes" rel="nofollow">https://en.wikipedia.org/wiki/Toblerone#2016_size_changes</a>
I'm curious how common such lawsuits are. I don't think I've heard of any specific instances where shareholders sued because a company didn't make the product worse.
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That's kinda me with respect to Claude. Generally i've had no issues and just kept pluggin' along.<p>The first real issue where i wanted to leave was the Claudish nonsense. If not for 5.5 i'd be on OpenAI by now.
It's pretty typical that physical-goods manufacturing "optimizes the process" to cut costs during years 1 & 2 of manufacturing.<p>Ikea is notorious for this: The early Billy bookcase had heavier veneer and sturdier construction early on, and was actually a really good purchase for the money. The later years replaced veneer with paper foil, used thinner shelves, frames, and backing panels, and was just significantly weaker.
Amazon Basics is incredible in this regard, they’ve optimized SKU identification down to a pipeline. They’ll essentially randomly pick items off their internal list of highest netting sales and test them to see how dependent they are on brand name recognition and price-quality signalling. To do this as efficiently as possible, they simply purchase a few hundred units of a high quality product in the space, stick it in an Amazon Basics box and list it on their site under their Amazon Basixs brand at a price they feel they can achieve via white labeling, and wait to see how it sells. The use of high quality items (with quality above what can actually be had at the listed price point for the duration of the experiment) means they are really only testing the user base’s willingness to forgo a brand name for the category in exchange for a discount. If it sells well, they then work on sourcing it in bulk as a white labeled item “for real”, while if it sells poorly they simply delist and move on.<p>I (used to) buy pre-spliced/terminated fiber optic cables with some frequency from Amazon and came to be familiar with the brands and their quality. One time while shopping for some fiber optics, I saw Amazon Basics-labeled OM-3/OM-4 MMF cable at a very tempting price, so I purchased some to see if it was any good.<p>To my utter shock and surprise, when I received the trademark plain cardboard boxes with the Amazon Basics label on them and proceeded to open them, I found that I was sent boxes of cables still factory wrapped with labels that clearly read “Corning Optical” – which if you know anything about optical fiber, was pretty much the premium brand in the game. I should have stocked up because the next time I went to order I found out their experiment had ended and they no longer sold “Amazon Basics” finer cables.
actually they were getting worse each year<p>most running shoe series get heavier year to year as manufacturers turn to cheaper materials and add cushioning to try to attract more adopters<p>it's almost universal, very few manufacturers seem to be able to resist tampering<p>(heavier shoes are slower, every three ounces is equal to another vo2max point lost)
You're literally doing what GP is describing. We have objective data on running shoes on: <a href="https://runrepeat.com/" rel="nofollow">https://runrepeat.com/</a><p>The foams are getting better, the shoes lighter, they are more cushioned and more responsive in general. Especially the ASICS.
> Perceived performance is actual performance over expectations and the latter just keeps increasing over time.<p>This is true of all reliability and performance paradigms, incidentally
I find that I learn to "trust" a model to get certain things right, as I would trust a colleague. So, as `expectation` increases, my prompting and context management gets sloppier.<p>`percieved_performance = actual_perf/expectation`<p>`expectation` is an increasing function over time.<p>`actual_perf` is a stochastic function of the model's true ability, context, etc. -> a recipe for some bad sessions.<p>As for multiple bad sessions in a row, this is a studied phenomenon in gambling where players perceive "runs" because our brains love to find patterns.
This reminds me of the fact that true random does not feel random to users due to the clumpiness that the average person does not anticipate existing in true random.<p>e.g. The original apple shuffle and the Risk app ins which a string of songs from the same album or three one roles are "not random"
I've been working on a game that has dice rolling and even knowing about this effect, I started going crazy yesterday when I had a long streak of numbers, like 1-20, it was 15-16 like 9 times out of 10. I was sure there was some kind of bug in how it was initializing random, or saving the number, etc etc. Just could not find it. Streak continued to another roll, another roll... still couldn't find it. Then the streak just broke. Apparently just random being random.
Some more on this...<p>How to Shuffle Songs? - <a href="https://web.archive.org/web/20220215030739/https://engineering.atspotify.com/2014/02/how-to-shuffle-songs/" rel="nofollow">https://web.archive.org/web/20220215030739/https://engineeri...</a> ( <a href="https://news.ycombinator.com/item?id=38330877">https://news.ycombinator.com/item?id=38330877</a> 78 points, 65 comments)<p>Took a little bit of digging to find it - I remembered the graphic at the top and found a blog post that copied it and linked to the blog post, but the blog post isn't there anymore... so web archive.<p>The current version of the blog post is from 2025 - <a href="https://engineering.atspotify.com/2025/11/shuffle-making-random-feel-more-human" rel="nofollow">https://engineering.atspotify.com/2025/11/shuffle-making-ran...</a> (which didn't get any traction on HN)
Yeah I bet most of us remember GPT-4 a lot more fondly than we would if we were to return to it today.
> I personally think people sense nerfs more often than they happen and that it's often about honeymoon effects.<p>I believe in temporary nerfs. Operators reducing quality significantly to increase throughput for whatever reason (high demand?). Same session, model being completly incapable, and it being back to normal the next day. Experienced that with Anthropic models way too many times. Never on weekends, usually during US work days.<p>It's been a few months since I last recall this though, must have been pre-opus-5. And I know there are benchmarks for this as well, hence "believe".
I vividly remember when ChatGPT3.5 went fully mainstream, there were times where within minutes you would realize they were only serving up idiot mode and there was no point trying to do much until demand died down and they swapped back to the non-quantized version.<p>People called it lazy mode, in that instead of writing the script you asked for it would basically tell you to learn to code then check out xyz topics to tackle the problem.
Regarding lazy mode, I recall ChatGPT sometimes almost refusing to do a web search despite me asking explicitly for it, instead replying with speculation about what the search results likely would tell us. If I pretend to be angry that it didn’t search the web it would however do it. Haven’t noticed this in a while either.
"If I pretend to be angry that it didn’t search the web it would however do it."<p>You have to pretend to be angry in such situations?
Oh god I just realized these are the same types of stories passed down to me by sysadmins of yore about when microsoft did XYZ. Am I… old now?
It should have stayed that way. Be a good search engine and encourage the human to do the work themselves.
Anecdote - I work in a time zone offset from continental US. The performance of anthropic models would noticeably drop, around the time US work day started. It was so bad around 4.x time that multiple colleagues re-arranged their schedule to have least overlap with US work day. Admittedly it's been better recently.
Friend of mine works for a corp that is one of the top spenders on Claude models. He complained about these nerfs during peak demand.
Their Anthropic contact changed something and it did not happen since.
> It's been a few months since I last recall this though<p>Anthropic cut a deal with SpaceXAI in May - $1.25b/mo. Before that, they employed months of dishonest nerfy strategies, to an extreme.<p><a href="https://www.anthropic.com/news/higher-limits-spacex" rel="nofollow">https://www.anthropic.com/news/higher-limits-spacex</a>
But you are using API not the CLI right? I did not ever observe API degradation, only subscription stuff through their CLI.
There's also this one which has been around for a while<p><a href="https://marginlab.ai/trackers/claude-code/" rel="nofollow">https://marginlab.ai/trackers/claude-code/</a>
Often times people think of "nerfs" as my first prompt (which was greenfield - no or little code existed) used 5% of my plan usage. And then 2 weeks later (as the agent is busy reading hundreds of .rs and .ts files it previously generated) the user complains the usage is going down 30% for a single prompt instead of 5%. Attributing this to a "NERF" makes little sense because it's the same model.
I'd take this kind of benchmark with a grain of salt. At this point, I have a set of comprehensive guidelines covering both backend and frontend work, and for the frontend we go as far as explaining what we a good design is in our visual system, and even how to conduct a visual review when screenshots are handed to the model.<p>Deepseek 4.1 ranks very low in this benchmark but it has proven so capable that after being simultaneously on Max x20 and Pro x20 subscriptions, i've transitioned to using DS 4.1 as a daily driver and am very satisfied.<p>My point is, i think their overall ranking makes sense, matches my experience with out of the box capabilities for vague and underspecified tasks. But seeing a model rank low in their ranking does not mean that the model is incapable. Having skills and guidelines has a lot of influence on what you get out of a model.
Anthropic A/Bs my weekly quota amount. So I have an automated prompt that runs at 3 AM with a transcription task, I measure input and output tokens, and weekly/5 hour quota before and after. The absolute token counts stay within 0.1% while in mode A it counts for 1% of my 5 hour quota and mode B 4% of my 5 hour quota.
How did pissing off your customers ever become a business model?<p>I can't imagine sticking with a supplier that plays games like that with me. Tokens are a pretty vague quantity to begin with (you don't control how many tokens a model puts out in response) and giving a couple of purposefully wrong responses will happily inflate your bill, but you don't care because eventually it worked. It's almost an ideal vehicle to scam people.<p>Imagine the power company being able to decide how much you consume and at which price point.
I find it amazingly rich that they bill you for """thinking""" tokens and now you don't even get to see them, they're gonna train the thing to sing "99 Bottles of Beer on the Wall" to itself before it starts work.
> How did pissing off your customers ever become a business model?<p>Airlines, banks, health insurance…
Another way Antropic misleads its customers is the description of the max plans. They are advertised as having 5x/20x the 5h quota as Pro. But the description says nothing about how the weekly quota scales, leaving customers to infer it scales the same way. But from what I've heard, the weekly quota is only 3.5x/7x that of Pro.
Step 1: Oligopoly
Step 2: Regulatory Capture
Step 3: Profit
Their fate is coming. Until the open-source models will be usable in machine with 256GB memory, they are done. Their behavior is unacceptable (Anthropic) recently but it won't last long.
I think it's sinister, but <i>not</i> for the reasons you're thinking. I think they're just wildly unprofitable on subscriptions. The idea that most customers won't use their full quota is plain wrong: most people are maxing out their subs, or even reselling whatever quota they have left.<p>When you're running something at a loss, you can mistreat your customers and they'll still stick around (I'm an example). OpenAI and Anthropic are now cheaper than Chinese models on subscriptions, while being 6-10x more expensive on the API.<p>My guess is they need the user numbers for the IPO and are willing to take a temporary loss in the meantime. By the time they go public, they'll either drop the subscription model or it'll turn into what the Chinese providers already offer: basically just a cap on how much API you can consume. Same same.<p>It's not clear what API tokens actually cost them, but I looked into running a local model, and it's way outside the budget of an individual or even a small or medium business (hundreds of thousands of dollars). So my guess is that running these models economically isn't possible, even if they're delivering real business value (coding, research, etc.). In other words, at API prices I'd just stop using AI, and I suspect most other developers would too.
> <i>It's not clear what API tokens actually cost them, but I looked into running a local model, and it's way outside the budget of an individual or even a small or medium business (hundreds of thousands of dollars). So my guess is that running these models economically isn't possible</i><p>Datacenters have <i>massive</i> economies of scale. Everything from cheaper electricity to having specialized, more efficient hardware to simply being able to run it continuously at near-100% utilization, all adds up.<p>Many things in the economy - most notably, <i>manufacturing of most consumer goods</i> - only makes economic sense once you're producing for/serving millions of people. This is not unusual.<p>> <i>In other words, at API prices I'd just stop using AI, and I suspect most other developers would too.</i><p>Many say that, but I sincerely doubt they'd actually follow through. People might get more conservative about how they spend their tokens, but AI today is just too good at eliminating drudgery and boring / bullshit parts of daily work to give up on merely 3-5x price increase.
> Datacenters have massive economies of scale.<p>Sure. Issue is, no one is providing on how much it actually costs to burn these tokens. And as we don't know, we can only speculate.<p>> Many say that, but I sincerely doubt they'd actually follow through.<p>I have a $100 open ai sub and I track my token usage. Last month I spent roughly $2.600 in equivalent API usage. There is <i>no</i> way am paying that. I let my $100 sub lapse if next month I'll be using it less.<p>Look, I am not saying that there isn't a potential value out there. But the cost has to be bounded. If your opportunity is $1.000 and AI costs $2.000 to execute it, then you don't have a business model here.
> Sure. Issue is, no one is providing on how much it actually costs to burn these tokens.<p>You can assume Openrouter open-model providers serve at or above margin, because there's no branding so there's no reason to do it unless you can be profitable. If the Anthropic models are anywhere in that ballpark, they're very comfortably profitable on API.
> I looked into running a local model, and it's way outside the budget of an individual or even a small or medium business (hundreds of thousands of dollars).<p>That is a big exaggeration. You can have a perfectly usable local LLM setup that will power your agent for single digit thousands of dollars. Can even power multiple agents simultaneously, depending on the hardware and setup. Won't be fast and won't be frontier intelligence, but definitely useful.
You can put stuff like "make sure your reply is between 800 and 900 tokens" at the end of your prompt and the vast majority of the time it will do so.
It's worked for online PvP gaming for a long time. Nerf stuff the min-maxers "earned" through game mechanics and sell over-powered "premium" things to everyone else to pwn them. Then nerf the old premium stuff and make new premium stuff. Forever.<p>I don't know if that's the actual origin of the term nerf, but it was the first time I'd heard it.
Do Anthropic quotas give you precise remaining token counts or something? I have something similar set up for tracking my ChatGPT usage but it only gives percentages remaining, which is a pretty coarse metric.
Claude code supposedly has otel you can set via env. I haven't set it up, so I'm just repeating hearsay.. but it supposedly has everything relevant in it wrt token usage and cost<p>It's meant for their test env I think, so is not documented to my knowledge
Tokens used / percentage change is a pretty obvious metric. They give you both, but they don’t do the math for you.
Could be load dependent, not an A/B test.<p>Is the fraction of the 5h quote consumed consistent with the fraction of the weekly quota consumed?<p>I heard there is a usage tracking tool you can install that tells you if tokens are more or less expensive at the current time.
Pretty amazing to see enshitification happen live with a product still in development… Truly web 4.0
If this is actually even close to reliable tracking, it's one of the most awesome benchmarks I've seen. I gave up on feeling the zeitgeist for what people were saying.
We could also use something that tracks concrete token amounts each tier gives you, in case they ever mess with it - and also maybe even the tokens needed to accomplish a particular benchmark, to see how much you can actually get done.
So that's why even Qwen-3.8-27B caught up with Opus4.6, it was nerfed to the ground.
The whole nerfing narrative puts in the spotlight now crazy supertitions come into being. The group think every day that everything is falling apart is crazy.
I dunno, I never sense nerfs for local models, but consistently a few months after launch for corpo hosted models, seems odd my internal model for the capacity of a model drifts for anthropic models but not local ones. I've been using LLMs heavily even before ada/babbage/davinci days, and trust my internal calibration over baseless handwavey explanations for why im imagining things, especially when I have data that shows capacity regression on frontier models for tasks, e.g. one shot success at loss, 0 success in 15 attempts once nerf is sensed. Others publish their quantified capability regressions which are also more trust worthy than this kind of handwaving.
What could be the reason to nerf?
It's cheaper to run a quantization of a model, but its quality is reduced.<p>For example, if your weights were trained as 32-bit floats and you need 1TB of RAM, you could reduce that to around 256GB by quantizing to 8-bit floats. You also make the model faster in the process because there is less data to process to calculate the next token.<p>The game is to balance between the savings of quantization and making the model dumb enough the people notice
Usage bench is also very useful! Thank you for doing this!
Wouldn't it be trivial to detect benchmarking if the same requests are running on a fixed interval?
Or nerfed version of models rolled out gradually.
Nerfbench isn't helpful if it's 3 days old.
These guys are on twitter angry about the rate limit decrease and allegedly cancelled all their OpenAI accounts. Wonder how they'll maintain this.<p>I am quite convinced that the whole nerfing phenomenon is 90% AI psychosis. I have the word muted on X.
The vibe bro science is this always happens on every release, every Tuesday, and twice on Sunday.<p>Of course it's almost entirely unsubstantiated BS.
Do they use private benchmarks? Because if not, it could be selectively nerfed.<p>I also wonder if cache could be used to throw these off as well, where it's serving un-nerfed cache results for context windows that are identical to ones they've previously had for benchmark requests.<p>Seems like the only way to do it well would be to have some randomness involved that couldn't be cheated on - but you'd want to do it in a way that doesn't throw out the benchmarks too much, so your results can be compared still.
> This bench famously detected a degradation of Opus 4.6 which Anthropic later blogged about.<p>this bench was just released, it couldn't have detected opus 4.6 degradation.
Theory (Conjecture? Hypothesis?): What we notice as "model nerfing" is the company diverting compute to training/running new unreleased models..<p>Remember that some people get access to the next flagship version long before us peasants do. I recall seeing the mention of "Astra" more than a month before it was officially announced
wouldn't less compute result in slower inference, rather than worse performance?
They could potentially quantize the model and run it at lower quality taking less VRAM.
The more likely thing that would happen is that the provider begins silently interpreting (perhaps some) high effort-level requests as medium, etc., or having a classifier do this far more subtly. As such, the load on the cluster is less, and more resources can be devoted to training. Whether the frontier labs actually do this is purely conjecture at this point.
I assume there's classification going on where a really basic "Hi how are you?" style request sent to a high-effort instance can be routed to a lower-level instance. This... is pretty much fine with me, assuming they do a good job of it.<p>I would also assume they use nebulous labels like "Medium Effort" or "High Effort" map to quantitative amounts of compute allocation... and that these amounts can be varied manually or automatically. Right?<p>I mean, there's a reason why they call it "High Effort" and not "Exactly 5 Minutes of GPU Time on Exactly 10 GPUs." They want to be able to move those sliders and tweak those knobs.
The problem is that if benchmarks are run at a tight classifier that says "a request for high effort means check-under-every-stone regardless of simplicity" but a user request is run on a different classifier where "high means maybe high, maybe medium, maybe even low, even for meaningful tasks" then you're not getting the model that you saw in the benchmarks.<p>And, while you might be billed fewer tokens as a result (because the lower thinking would result in less investigatory work), <i>you might not know</i> this is happening, and know to dial up effort accordingly - you'd simply get a worse work product. And certainly, Anthropic's incentive for anyone on a subscription is to push this as aggressively as they can, so people use less of that subscription.<p>Sadly, I'd also expect that the OP's benchmark will be detected as a test of model capabilities, and thus be given a high classification so that this strategy remains undetected.
why is that more likely?
My guess is that they are dynamically changing the quality of the model to always keep the speed above some floor. So once it gets below that they switch to a worse quant or reduce reasoning level, or some combination of both.
There is no nerfing, look at the data before coming up with a theory as to why the nerfing that isn't even happening is happening.<p>fuck
you can't explain honeymoon effects. like wow wow wow and then suddenly: same task, lesser performance is a misperception?<p>my fair lady gained a bit weight and the bjs lack variety?
[ I'm certain that that's a quote from some time ago by some commenter in a thread with a similar or even the same context ...but my Amnesia (Ｔ▽Ｔ )<i>:･ﾟ</i>:･ﾟ] fuck off.<p>you open two files, before and after you notice a nerf, and from worse comments to logical oversights, it's all damn obvious.<p>don't normalize this make believe bullshit and misleading people who you think barely understand what they see anyway ...<p>you wouldn't even know if models had somehow timed nerfs hardcoded into them, however much control over the stack you have.<p>ridiculous
Glad someone's keeping track. These threads always turn into “they made it dumber” vs “nah, you're imagining it.”<p>A few bad answers can be annoying as hell, but who knows what's behind them. I'm curious to see how the numbers look after a few weeks. Props for tracking improvements too.
> It could also mean nothing happened and people are pattern-matching on noise.<p>It is definitely not this. Anthropic has thousands of employees making probably > 100,000+ tiny changes across the entire stack and infrastructure everyday.<p>The compounding effect can definitely cause temporary regressions in some domain or use-case that doesn’t have really good coverage in their internal tests.<p>What makes this particularly challenging in the case of LLMs is how changing some language in the prompt can vastly affect the output.<p>But this is less true as models become larger and additional parameters are able to capture each and every possible nuance of the language. I’d say caching and cache tuning or token optimization/tweaks to thinking are the biggest culprits today.
> It is definitely not this. Anthropic has thousands of employees making probably > 100,000+ tiny changes across the entire stack and infrastructure everyday.<p>What "stack" do you have in mind here?<p>An LLM is a monolithic slab of weights, not millions of lines of code spread over many microservices. The changes they make will be showing up in web and app responsiveness, and in the performance of training runs for one or more next versions of the that monolithic slab of weights, but I'd be surprised if there's a way for their efforts to show up <i>directly</i> in a "has this model been nerfed?" sense.<p>Both Anthropic and OpenAI used to have multiple snapshots per model; both seem to have switched to updating version numbers when anything changes, looking at the "snapshots" section in their recent and old models: e.g. <a href="https://developers.openai.com/api/docs/models/gpt-4o" rel="nofollow">https://developers.openai.com/api/docs/models/gpt-4o</a> for how far back I had to go to find multiple snapshots on an OpenAI model, and <a href="https://platform.claude.com/docs/en/about-claude/model-deprecations" rel="nofollow">https://platform.claude.com/docs/en/about-claude/model-depre...</a> seems to be a more useful list for Anthropic, but both are now things they did a year ago.
> <i>An LLM is a monolithic slab of weights, not millions of lines of code spread over many microservices. The changes they make will be showing up in web and app responsiveness, and in the performance of training runs for one or more next versions of the that monolithic slab of weights, but I'd be surprised if there's a way for their efforts to show up directly in a "has this model been nerfed?" sense.</i><p>It most definitely is not, hasn't been for a while now.<p>I.e. when dealing with hosted models of the large providers, you are not interacting with a big bag of floats. You are interacting with an <i>API/UI that presents</i> an unholy web of software components, some of which may be large or small bags of floats, <i>as if</i> they were a big bag of floats.<p>Even with local models, you have dozens of parameters you can tune for inference these days, all of which affect quality of output in some way or another. And that doesn't touch load balancing, A/B testing, shunting token burners ("Hi chat, how are you?"), protecting user from themselves (refusing to answer "bad" queries, stopping "bad" responses), protecting user from third parties ("prompt injection" mitigations), protecting the model from self-pwning itself when calling tools, then the tools themselves, their prompts, the stack of system prompts used by the vendor, etc.<p>There's <i>a lot</i> of things to tune there, and just as many reasons to do it.
The API documentation linked in my comment seems to say that (with two exceptions*) when we ask for a specific models, we get that specific model.<p>The livenerf tester appears to be testing a specified model, just as the website (and Claude Code) do when a user makes that choice.<p>> Even with local models, you have dozens of parameters you can tune for inference these days, all of which affect quality of output in some way or another.<p>Good points.<p>> And that doesn't touch load balancing, A/B testing, shunting token burners ("Hi chat, how are you?"), protecting user from themselves (refusing to answer "bad" queries, stopping "bad" responses), protecting user from third parties ("prompt injection" mitigations), protecting the model from self-pwning itself when calling tools, then the tools themselves, their prompts, the stack of system prompts used by the vendor, etc.<p>The behaviour I'm seeing from the companies these days, the A/B is what I'm saying <i>is not</i> showing up like this, they present user A/B options openly, and the impression I have is this is to train n+1 models; the other stuff (but I say with low certainty) appears to be done in a more headline-grabbing manner, "model taken offline due to ${news}"? Short update cycles seem to allow that.<p>But the prompts you're probably right, I wasn't giving that enough consideration.<p>* the two exceptions being automated safety downgrade for dangerous topics, and "Auto-switch to Thinking" as a used-specified option in ChatGPT
Serving LLMs at Anthropic scale is very, very different. It’s not SGLang or vLLM.<p>If you’ve tried setting either of these up, you’ll know how various tricky settings can impact throughout and model output quality; and those are much simpler stacks.<p>Even homelabbers are getting into disaggregated compute; e.g. one GPU for prefill, another for decode.<p>Obviously Anthropic and co are using a mixture of GPUs and hardware and clusters; not everything is just GB300 or whatever; so you then get into hardware quirks, kernel optimisations that may deliver huge speedups at the cost of a tiny bit of KL divergence, etc.<p>And I believe they’ve publicly said they use TPUs for inference too, but I doubt exclusively; and I’m sure that’s well optimised too.<p>Finally, Google has publicly stated they intentionally and silently degrade/poison models in response to distillation attacks; who knows what the other companies do.
> It is definitely not this. Anthropic has thousands of employees making probably > 100,000+ tiny changes across the entire stack and infrastructure everyday.<p>I don't follow. That sounds to me exactly like a reason why it <i>could</i> be people pattern-matching on noise: because there is a lot of noise in which a matchable pattern could emerge.
> It is definitely not this.<p>I don't think you understand what "this" is--or rather, the "thing" that didn't happen in "nothing happened".<p>> Anthropic has thousands of employees making probably > 100,000+ tiny changes across the entire stack and infrastructure everyday.<p>So, not the sort of thing referred to.<p>> The compounding effect can definitely cause temporary regressions in some domain or use-case that doesn’t have really good coverage in their internal tests.<p>Yes, but you claimed that this <i>definitely did happen</i>. But the whole point is to determine whether it did.
As a claude code power user, when I get the 'rate the feedback on Claude' pop up, I used to say good or fine out of habit, and immediately after sending this feedback, I felt an instant degradation and mistakes that usually don't happen.<p>Now I dismiss it every time and the quality is more consistent.<p>Complete adhoc and personal experience but something I've observed, wouldn't be surprised if they nerfed on a per session basis
This is "rub your gameboy the right way to catch more pokemon" levels of insanity.<p>Random performance is random, your brain will jump through hurdles to fit patterns where there aren't any.
On the one hand, yes, on the other hand it would also not be too hard to do routing based on such a parameter and Antrophic has shown (through Fable and Mythos) that they have such a transparent routing system in place.<p>In fact, since they have some rule based system (if bioenegineering or security, route to degraded model) it would be almost trivial to add 'user has filed feedback' to it.<p>Not saying this is what happens, but just that it's not as insane as it sounds.
I could totally see them running an experiment to test user's stickiness/quality perception with decreased model performance. Facebook was doing exactly this in 2016. They tested the loyalty of Android users by secretly introducing errors that would crash the app to find the threshold at which a person would abandon. That was 10 years ago - imagine what the state of the art in user manipulation is like now.
As a counterexample, I click that feedback all the time and nothing ever changes afterward.
Another personal anecdote, but I had the case where Claude would visibly improve after a negative feedback.<p>And strangely, expressing frustration multiple times in a row would reliably trigger a feedback popup as well.
When the source maps leaked for the harness, it was revealed that they track how often tool use is rejected and how many times you say "fuck". They definitely try to track frustration sentiment.<p>That said, given the propensity for mature code bases to have "fuck" in commit messages/comments and those are typically of higher quality, I curse up a storm when the clankers make mistakes, if only to try put more quality-code valence into context. <a href="https://news.ycombinator.com/item?id=36584464">https://news.ycombinator.com/item?id=36584464</a>
True, but tracking ≠ Claude using that feedback to improve performance right there and then
To be fair, I also do my best work when I get to swear like a sailor.
As a hiring manager I now have a rational reason to prefer people willing to use profanity. Thank you.
Wow .. really! Definitely testing that out a few times meself
"Nerf"ing models isn't real in the vast majority of reported cases. Benchmarks like this or the 100 other "let's see if nerfing is real" copies would have shown it by now if it was.<p>I made a graphic to explain why people feel like the models get nerfed:<p><a href="https://x.com/thesilenceturns/status/2103551351825543610" rel="nofollow">https://x.com/thesilenceturns/status/2103551351825543610</a><p>The idea is that new models can handle up to a certain level of complexity, at which point they fall apart. Every new model can handle more complexity, so there's a wonderful time upon release when you feel like you can do anything, only for you to hit the complexity ceiling a few days later when you saturate it. Rinse and repeat for the next model.
Incorrect.<p>Anthropic has admitted to nerfing in the past. There have also been inference bugs. On top of that, model performance changes as they move compute to schwaggier providers as well.<p>Your chart is wrong.
> Anthropic has admitted to nerfing in the past<p>Where?
Earlier in march/aprile, there was a regression in Claude code.<p>Unintentional tbf.
Unrelated to the models
does it really matter wherever its the harness or the model for the vibe coder user claude code?<p>fwiw, i think almost all regressions are down to a/b testing in the harness by anthropic, but it is objectively indistinguishable beyond "the coding agent ceases to be usable" and i'm back to traditional coding for a few hours until its back to normal again
Yes. The agent allows you to switch models, so you could sidestep a bug in one model by temporarily using other models.<p>The /r/antigravity SubReddit is full of users who very much notice bugs with the tool/agent. We should be thankful that Claude Code is pretty stable by comparison.
...<p>It absolutely matters because something like Claude Code has no guarantee that there won't be changes between updates but a model pinned at the API version level that is getting enterprise traffic absolutely does have that guarantee and would be a much more widespread problem...
Honestly you need to start making pelicans on day of release, and following up a month later. Only way we are going to catch them
Man this was last year and some Claude subreddit drama that I can’t furnish offf the top of my head but maybe one of the historians remember it.
Noone can seem to remember anything with certainty when asked to actually substantiate these claims.
There <i>was</i> this:
<a href="https://www.reddit.com/r/Anthropic/comments/1sl5wfh/the_degradation_of_claude_opus_46_people_are/" rel="nofollow">https://www.reddit.com/r/Anthropic/comments/1sl5wfh/the_degr...</a>
I mean there is a direct link two comments down from here from 30 minutes before your comment: <a href="https://news.ycombinator.com/item?id=49902477">https://news.ycombinator.com/item?id=49902477</a>
Er, perhaps take a look after the edit:<p>> There are recorded cases of real regressions, but they're better characterised as incidents, not nerfs
That’s NOT Anthropic admitting to “nerfing” their model as claimed above (which implies intent), that’s a regression which they quickly fixed.<p>Christ this forum has become intellectually dishonest.
I’m typing from my phone and im not going to review the semantics of Anthropic’s storied history of performance issues.<p>It’s not just ant. There are so many small knobs that providers can claim isn’t nerfing but “load management” or “improving user experience”. One example from OpenAI is reducing juice to reduce time to first token.
<i>I asked a historian:</i><p>Two postmortems, neither quite "admitted to nerfing":<p>Sept 2025, infra bugs: "A small percentage of Claude Sonnet 4 requests experienced degraded output quality" [0], alongside "We never reduce model quality due to demand, time of day, or server load." [1]<p>April 2026, Claude Code: default reasoning effort was lowered from high to medium, plus a caching bug and a verbosity prompt. Per Anthropic, "The models themselves didn't regress, and the Claude API was not affected." [2]<p>So users were right that quality dropped, but the confirmed causes were bugs and a product default, not deliberate model degradation.<p>[0] <a href="https://status.claude.com/incidents/72f99lh1cj2c" rel="nofollow">https://status.claude.com/incidents/72f99lh1cj2c</a><p>[1] <a href="https://anthropic.com/engineering/a-postmortem-of-three-recent-issues" rel="nofollow">https://anthropic.com/engineering/a-postmortem-of-three-rece...</a><p>[2] <a href="https://texxr.com/handle/claudedevs" rel="nofollow">https://texxr.com/handle/claudedevs</a><p>source: <a href="https://claude.ai/share/4435bbcf-d6df-44a0-b1db-f08a11858bc2" rel="nofollow">https://claude.ai/share/4435bbcf-d6df-44a0-b1db-f08a11858bc2</a>
> bugs<p>There are no bugs, just happy little accidents.
> "We never reduce model quality due to demand, time of day, or server load."<p>That just means they don’t reduce model quality for those reasons.<p>They didn’t mention other reason, for example, “Make more money”.
Sorry, you are correct - I modified my original post. I get frustrated every time there's a model release and 1 week later everyone is saying NERF! NERF! 99.9% of the time these people are wrong, but you are right that it's technically not 100% due to a few edge cases.<p>I am more skeptical about the compute provider claim - do you have any evidence of that?
Unintentional bugs aren't nerfing. Nerfing is deliberate Enshittification done secretively.
Right, and it would be simple to un-nerf or shadow nerf by any kind of angle they want.
There are recorded cases of real regressions, but they're better characterised as incidents, not nerfs, e.g.: <a href="https://www.anthropic.com/engineering/april-23-postmortem" rel="nofollow">https://www.anthropic.com/engineering/april-23-postmortem</a><p>Btw, you have a typo in the twitter handle on your profile (not in your comment), 'thesilencesturns'.
Off topic for sure, but why do people insist on using X/Twitter in this day and age?<p>The majority of people are not on it, and the links are gated by a ton of toxic dark patterns and horrible UX trying to force people to sign up or log in.<p>I try to click on the image to enlarge and make the text readable, and I'm greeted with a login screen instead of a larger image.
At least in robotics basically everyone is on it.
There is an absolutely massive tech community on Twitter and its by far the place to get real time updates on tech news (yes - better than HN). It isn't all a far right cess pit and that is easy to avoid by just using the following tab
Get an extension like LibRedirect and load it up with some public nitter instances and you can get an actually sane twitter-browsing experience.
It's a real thing<p><a href="https://marginlab.ai/trackers/claude-code/" rel="nofollow">https://marginlab.ai/trackers/claude-code/</a><p>This site has been documenting it for a while
That's a good observation, though I'd say here that two things could be true at the same time. But, I do personally believe that most of the reported nerfing is the case of your chart + latent evidence-less complaining. Honeymoon phases are real.
Nerfing is certainly real and I don't see how you could argue it isn't.<p>A/B testing alone would result in a performance nerf for one group.<p>8-bit quantized models will barely show degradation on benchmarks. The performance is reliably at 99% of the non-quantized model. 4-bit quantization retains somewhere around 95-98% performance on benchmarks. But if you've ever used a 4-bit model, it feels lobotomized.<p>And just consider what a compny serving these models would do if they were at capacity. Would they stop serving the model altogether? Of course they wouldn't...<p>Denying that models experience purposeful degradation is gaslighting.
I have not been doing increasingly complex things since Opus 4.6 when models got really good.<p>My work at my job has stayed the same. But the model quality has varied.<p>They definitely tune the models in production after launch, if not only to share load during high traffic times. It’s not a crazy conspiracy that the same model can be stupider at different times.
> I have not been doing increasingly complex things since Opus 4.6 when models got really good.<p>This is a more a statement on the work you do and how you work versus the models. I'm doing more complex work since Fable (and now for way cheaper thanks to Opus 5.5)<p>With 4.6 I would still babysit a lot more code quality and so on. With the newer model I see myself talking about features at a higher level, and then not having to nitpick PRs to death. Which means most of my time is now spent talking to the model about the product instead of the implementation of the product.
What sorts of things, if you can say? Is it a similar sized/complexity codebase? Most projects do become larger and/or more complex over time. And most people's standards do creep up as they learn.
It's not about doing more complex things - complexity is more dictated by how large your codebase is, etc.<p>> It’s not a crazy conspiracy that the same model can be stupider<p>Sorry, I really do think it's a conspiracy. If nerfing were real, it would be trivial to prove. DeepSWE, SWEBench, and other benchmarks are all available for anyone to run. A "nerfing" hypothesis has to survive the fact that a statistically significant dip in benchmarks has never been observed.
Open AI admits to such here: Open AI aims to have a stable API and admits to meddling with effort levels and such for subscriptions -<a href="https://news.ycombinator.com/item?id=49804316#49809266">https://news.ycombinator.com/item?id=49804316#49809266</a>
Nerf is real, i think we initially get full precision models and later quants. My own logs show it clearly for opus 4.5 to 5, consistently a few months post launch, models start making quant based mistakes, like slipping in inappropriate tokens (e.g. chinese ones in english text) which doesnt happen at all in the first few months and regularly later. Additionally frontier problems previously done well start being done poorly, until later model variants where performance mostly holds, likely due to them training on your data reguardless of what boxes you tick.<p>My local models don't display that degradation, sensed or measured. They consistently perform equally to what I expect of them, precisely because they don't change.<p>How does twitter explain that? Is my internal model for expectation of capacity magically not drifting for local models but somehow is for anthropic api call based models?
it is, mainly for subscriptions.
Admittedly, I didn't click your link, however, based on what you've stated, there is some inaccuracy. All these big companies take your requests and the context, and route it based on the content, cost, etc.<p>What Anthropic presents as Opus 5.5 isn't actually a single model...it's Anthropic's ecosystem as a whole. If you are lucky, you get the top model handling your issues all the time, however, that never happens. What really happens is that your request and content are graded along with your subscription (example: API? subscription, if so, what tier? how much has the user used it? Do we trust the user? how much? how much are they paying? are they asking something we think is dangerous?) and your request and context are routed accordingly.<p>Anthropic isn't alone in this behavior, Open AI does it as well, just look at the respective subreddits on reddit for both if you need some examples, or just play around with the various models from both companies.<p>There are a few folks who've done some analysis on this (their findings were posted on reddit and X), and a bigger multi-national study is apparently coming, though I admittedly don't know their findings.<p>I guess the tl;dr is that Anthropic and Open AI are actually selling you "best-effort" routers, so you may or may not get the best in class model, and only they get to determine if you do or do not. No guarantees.
> "Nerf"ing models isn't real in the vast majority of reported cases.<p>That sentence... This conversation is indistinguishable from a billion conversations had around multi-player online gaming.
“Nerfing is a myth” - “I made an imaginary chart to show you why”
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That’s right, and it’s been like this ever since we stopped programming in assembly language. Programmers’ brains used to grow manly and strong on a strict diet of manual memory management and custom stack frame handling. Once we transitioned to soft, weak modern languages like C it’s been all downhill.
You are on actual drugs my dude.
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if that's the theory people won't keep using 4.6. Personally I've felt the nerf for 4.8, when 5.0 is (near) launching. And my theory of a model being nerfed several days / weeks after launching has to do with the number of users. At launch there won't be too many users so the computing power per user is huge. As time goes, users and agent has been adjusted to newer model, the computing power per person gets reduced
It's very much real but not necessarily malicious. We track upstream providers pretty closely. Sometimes it's a just matter of a single GPU runtime layer bug/update to break inference outputs. The model weights don't necessarily change/get quantized.
Given the tens of bot accounts in this thread making obviously misleading statements, I think you’re onto something here. Might be a good idea to open a Patreon or something, you probably need throwaway accounts to prevent anthropic from feeding you good models.
Is it the models or the users' dopamine receptors that get nerfed after a few weeks?
I think Anthropic tries to adjust things to handle their user load, which has negative effects. For example, before they added 1 million tokens back in February, I could effectively prompt Claude to keep going until x number of requirements were completed, now I have to make a loop, not only that, but Claude will finish before my loop time sometimes and wait for the next pass, which could be in like 30 minutes or so. This probably helps them keep a lighter load, but it could probably tick off anybody, the output is the same, you just aren't getting it as quickly as you once did. I don't hate it, I use Claude on my off-hours to work on personal side projects.
I 100% believe the models are being nerfed and feel it when I use it. Launch day LLM + the week it's released is fantastic. After they get all the press the nerfing starts. When they release a new model sometimes it's not significantly better, it's just not nerfed.
This is great. The business these companies are in is actually quite simple, and managing the expense of running the hardware is an enormous lever for them when it comes to profitability. They have many developers whose sole focus is on this optimization problem.
I wonder if more organizations approving the model on a fast-tracked basis means Anthropic is straining for more compute and thus sheds a tiny bit to handle the increased demand, especially at peak times.
Glad to see good old human cognitive biases (or, in very advanced cases, just sloppy methodology) are still highly competitive with SOTA model hallucinations.
All this dishonesty and shadiness is part of why open models feel inevitable. Even if the total cost of ownership is higher (debatable; seems that way at small scales, but likely not as you grow), I'd rather have intelligence controlled by <i>me</i> that works for <i>me</i>.<p>The current period is as pro-customer as we're ever going to get, with cash still flying around and neither OpenAI nor Anthropic on the public market, and people are already forced into this sort of business to keep them true to their word. The point isn't even whether they're nerfing the models (I don't think they are), but that people can't seem to trust them to do right.
This is like the Ig Nobels. First, it makes you laugh, then it makes you think.
The only reason why claude fable is better than opus in my opinion is that it has more "criteria"... if you present a problem and then ask for his recommendation you can get an opinion on why and reasoning on why that one... Opus is going to vomit 10k lines of extremely dense prose in nerdify++ level.<p>Yesterday I fought claude fable to not just jump to make changes like a dog following a treat, that we were researching... at some point I introduced the word HAWAI... and only if I say HAWAI the thing can start making changes..<p>I was going to post here in HN just to have a "I knew this was the reason" when they release fable > 5.1<p>I had the exact same feeling every time they have a new big release
I experienced similar tendencies with Fable as well.<p>Even in fresh sessions with minimal context like a file with a couple hundreds of lines of code, it would frequently ignore clear instructions, avoid work, and even sometimes “think” things like “looking for ways to code without approval”.<p>The model is just tuned for long running and doesn’t like to work with a human in the loop, or work back and forth.
Did you just say “his”?
What does HAWAI mean here?
Complete anecdote, and nothing to do with relative nerfing or not: Opus 5.5 has been surprisingly good for me (including the past couple hours), especially for following research-level questions/directions.
Opus 5.5 feels like yet another massive increase. I've got it at my job and it's basically one shotting quite large refactors that would have taken me at least a day if i had to do it by hand. Now i let Opus do the refactor in ten minutes and i go through it by hand to clean it up for an hour and it's done.<p>Its quite worrisome honestly and i feel like that 'im in danger' Simpsons meme more and more. Right now i still have a lot of domain expertise which helps in knowing which questions to ask and which problems to solve, but well, i wonder how long that is going to save my job.
In my experience(feelings), the biggest nerfs usually come when they’re preparing to release a new model.
We don’t know the model architecture but there’s a lot of evidence to suggest load dependency on the hardware affect models quality (see for example how the original Google Translate models got worse depending on time of day). The GPUs at maximal utilization is what they’re shooting for with their pricing models so you’d need to measure model performance at peak loads to know the floor of performance I would think.
Anecdata: I've been running a long-lived claude code session with Opus 4.6 for the last few days. Yesterday, almost right after the Sonnet 5.5 announcement, codex starting asking for permission to run things a lot more often<p>The quality of the output/work seems the same, but the speed at which it gets stuff done is a lot slower, because it's asking for permission so much more<p>I don't have any numbers/stats, just my impression. However, I imagine that if Anthropic could make the models ask for permission more often, it could be an interesting way to throttle access, without degrading quality of the output
ChatGPT tends to modulate the speed at which you can type. If it is a bug they probably should have fixed this months ago, so I'm guessing it is intentional.
Yes across all devices (mobile, PC, etc.). If it's a performance issue then it's one of those "happy accidents" that is a bug in their favor. Either they don't care about quality or they really like money more than quality, but no reason it can't be both.
Super long chats are slow and sluggish too at least on chatgpt/claude.ai; even on a semi beefy machine.<p>I’m sure it’s lowkey intentional, probably encourages users to spin up new chats; hence less context.
This is the case since Opus 5, the latest models (from all providers) favor using shell tools instead of the View/Edit tools available in the harness, and “accept edits” doesn’t let those calls through. Auto mode is the best option.
Running agents in MicroVMs and skipping permissions is the most impactful change I have done in my habits for a while.<p>Have a look into Docker sbx for instance.
I noticed this, too. I suspect their classifiers which prohibit certain tasks or require user permission for others is the reason for this.
I just use auto mode but there is also some config settings for more fine grain control. The model could even help you customize them.
I think most people are on 'auto' mode nowadays.
subagent perhaps? afaik subagent by default uses sonnet and perhaps the 5.5 uses different permission definition
Actual question is this:<p>As my weekly/hourly quota is nearing its end, does Anthropic start to serve me a worse model?<p>Am i personally being throttled? testing against API is meaningless.
If it can’t even tell apart Opus 5 and 5.5 (according to the readme) then it’s not useful
Hi, I’m the author, The Opus 5 substitution was a validation test, not the primary measurement. At that sample size the accuracy difference was -3.8 ± 6.3 points, so it did not clear the pre-registered 99% threshold. Interestingly, output tokens moved much more (-23%), which is why token usage is tracked as a secondary signal.<p>The actual 10-day windows contain substantially more samples than that validation, but I haven’t demonstrated that they’re sufficient to distinguish a same-family swap of that size, so I’m not claiming they are.<p>The goal isn’t to make the instrument say “nerfed.” A null result is a result too. I’d much rather publish “we couldn’t detect a change of this magnitude” than overclaim what the data can support.
It seems as if this is based on demand. Whenever a new model is released, I'm guessing tens of thousands of us switch over to try the latest and greatest, which overloads the servers, leading to nerfing. It's 100% dishonest, but they realized they would lose users a lot quicker if they were honest and just said "our models are overloaded, come back later".<p>After Fable launch I switched over to Codex and it was simply amazing, with frequent usage resets that seemed never ending. They clearly had more compute than they knew what to do with. Post Astra, Codex has gotten dumb again across all models, increased usage for no real reason, and no resets.<p>I'm guessing Opus 5.5 will take the heat off Codex for a bit, leading to better performance. So I guess I stick around here instead of switching <i>again</i>?
If servers/resources are overwhelmed it should result in slower responses not degraded quality, or at least have an option for the user to chose from. I'd almost always prefer to wait than get broken or poor results. Even a warning would help, I'd at least not waste my time.
It's absolutely based on demand: If you increase the number of experiments, you also increase the number of statistically significant-looking results. See also: <a href="https://xkcd.com/882/" rel="nofollow">https://xkcd.com/882/</a>
Only ten day interval? I felt Astra got nerfed within a week
The 10-day window is mainly a tradeoff between sensitivity and detection speed. Shorter windows give faster results but are much noisier; longer windows give more statistical power but could take weeks to flag a change.<p>Also, it isn’t comparing one 10-day period once and calling it done. The window rolls forward daily, and a change has to clear the pre-registered 99% threshold in two consecutive windows before it’s flagged.<p>Ten days isn’t sacred, though. Once there’s enough longitudinal data, one of the things I want to evaluate is whether that window length is actually well calibrated or should be changed in a future version.
already getting poorer results today
It's always around a week<p>(which has led me to believe that's a good approximation for hedonic adaptation, I've seen tons of attempts at demonstrating nerfing via benches, none persist)
"wow this model is really good I can push it so much further"<p>_pushes model further_<p>"Ugh why is this model failing now even though I'm asking it to do harder things and also got sloppier with my prompts because I got used to it being able to figure stuff out"
Brilliantly put. Hedonic adaptation is exactly what it appears to be.<p>It’s frustrating to observe communities made up of smart, professional individuals as they behave like spoiled children on the day after Christmas when new toy novelty has begun to wane.<p>I understand it’s relatively harmless but for goodness sake, take a step back and appreciate what you have instead of immediately wanting the thrill of a newer model. Slow down and do deliberate work to get the most out of these amazing tools. Don’t just live off the temporary thrill of finding something marginally better than what you have.
Game of whack-a-mole: people looking for ways to detect nerf, while companies adapt in hiding nerfing.<p>At scale where anthro and cgpt operates - every single token matters.
I think the baseline should be the first 3-5 days after launch. By week 2 - you are already on the downward slope.
One thing that these nerfing conspiracy theorists fail to realize is that <i>Anthropic isn’t the only organization that directly serves Claude inference</i>.<p>My company uses Claude models exclusively via Azure and AWS bedrock, which have their own licensed copies of the weights.<p>If all these people are so convinced Anthropic is nerfing models, have they tried other inference providers? Do they think the nerfing is coordinated across independent providers? Why wouldn’t any of these nerf-benches use these comparison points <i>or even talk about them</i>?<p>I think the most likely conclusion by far is that this is a psychological phenomenon.
This is a good point but is it actually the case that inference providers directly control the inference code and weights, as opposed to being a hardware/infra provider? It seems like it would require a nontrivial amount of work, I doubt the inference is in any kind of standardized form. Moreover it seems that this would risk things like degradation if the model provider used different inference techniques (e.g. 16-bit vs 32-bit floats)
Opus 5.5 was suppose to be faster but is does not seem to work at all in batch mode. Nothing completes.
It seems the subscription based plans these big vendors have has different quality on the service side based on time. Is this fair?
This is a great idea because it holds LLM providers accountable, plus it's slightly embarrassing for them that it's needed in the first place!
Are we spending energy tokens to monitor IA? WoW. I mean, It looks necessary but also a trap.
I wonder if API is affected by this issue, especially Claude on public clouds? Would that means the subsidized rate just means they use cheaper quantized models and it's not comparable to API spending.
Fable 5 seems like it got nerfed when 5.1 came out.
Pure gold
Been keeping an eye on output quality for specific tasks, no red flags for me yet. YMMV.
I've been using both Opus 5.5 (high) and GPT 5.6 Sol (medium) intensively for the last few days and I'm noticing more and more that, even though Opus gets most of the things correct (and it's great when it comes to visuals), GPT Sol is the only one able to identify edge cases and inconsistencies.<p>I wouldn't say Opus has been nerfed. They are just two different models but, at the end of the day, GPT Sol is the model I trust the most, at least for now.
The nerfing/quantization strategy is unsustainable. The first lab to <i>not</i> do it wins (short term). The Anthropic pause on Fable might just have been that.<p>My gut tells me this involves an undisclosed, never-released grandparent model (higher-class than Fable/Astra level, roughly unsellable due to unfeasible cost). That grandparent model is distilled into lower models, of which Opus 5.5 might be an instance of.<p>That also guarantees protection against distilling a core business. You never make your prime weights available to the public, you only make distillings themselves available.<p>The downside of this strategy is that you spend a lot of compute on something that you never release, but it might be just the right play (for now) for closed weight companies.<p>It's a gut feeling, I have zero hard evidence to back it up (it's what I would do as them).
Another reason not to use proprietary models for your products
that's interesting!
OpenAI will simply set up a classifier to detect if the client is livenerf, and selectively not nerf those requests.<p>Open models are the endgame.
So make it so all clients pretend to be livenerf at first. Same as the ol' pretending to be Google UA for free articles
And doom3.exe or quake3.exe for GPU drivers :)
I'm not talking about HTTP headers.<p>The classifier is a model; it examines the actual prompt.<p>They already do this for the safety "guardrails".
This is actually why I've been reluctant to setup my own degradation trackers. I'm afraid it might be too much of a time investment for something that's much easier for them to detect.
This repo already has too much visibility now. Anthropic will soon benchmaxx it.
This is a limitation of any public benchmark. Anthropic could theoretically identify the prompts and treat them differently, and there’s no way for an external observer to prove that isn’t happening. Which is partly why we need more capable open-source models.<p>A few things make it harder: the panel contains questions drawn from multiple benchmarks rather than one recognizable test, the evaluation is automated and fixed ahead of time, and the raw outputs/results are public so odd behavior can be inspected.<p>But ultimately LiveNerf measures the behavior exposed through the API on a fixed public panel. It can’t prove what’s happening internally or guarantee the provider isn’t conditioning on the benchmark.<p>Longer term, I’d like to add held-out/private or periodically refreshed panels specifically to make benchmark recognition harder. I just don’t want to quietly change the current panel, because having a fixed instrument is important for the longitudinal comparison.
Going by the comments on this thread: Faith, belief, conspiracy, unverifiable claims, incomplete answers from closed cloud services that cannot be made deterministic.<p>Great future everyone has chosen for us.
My answer is yes.
n=1 is useless. The output is not deterministic.
Author here, agreed that a single generation isn’t meaningful. That’s why the benchmark is designed around distributions rather than individual outputs.<p>The initial calibration screened 2,336 questions with 4 samples each, then selected the 78 questions where Opus 5.5 showed useful variance. The panel is run daily, and the actual decision is based on paired per-item differences across 10-day windows with clustered standard errors, not on any single day’s result.<p>The n=1 in the daily sampling rate means one sample per item per day, not one sample for the experiment. By the time a window is evaluated there are hundreds of observations, and a change has to clear a pre-registered 99% interval in two consecutive windows before LiveNerf calls it a change.<p>The nondeterminism is basically the reason the statistical part exists in the first place.
Hot take, none of the models are getting "nerfed", people are just getting used to the new level of intelligence.
No, whether or not it's intentional, maybe can be debated. But there's <i>definitely</i> an experience of a model losing horsepower quickly after launch.
Has there ever been any measurement of this, of any sort? Honest question. I frequently see a plural of anecdotes to that effect, but I've not seen a concrete statement of fact or measurement that could be scrutinized or tested in any way.<p>If so, please share. This should be measurable, and I'm glad this project is measuring it.<p>Answers in the form of additional anecdotes, stated with even greater passion but still lacking a statement that could be tested and falsified, would validate my exact concern.
Official tweet from Tibo from OpenAI confirming they ran experiments tweaking “juice” (reasoning effort mapping values) and have reverted them: <a href="https://x.com/thsottiaux/status/2076495156757577895" rel="nofollow">https://x.com/thsottiaux/status/2076495156757577895</a><p>As he confirmed, for at least a period of time, and for some users, “Sol xhigh” was actually “Sol high”, etc.
prove it
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No there isn't.
You mean to write YOU haven't experienced this. Many have indeed experienced this.
I'm not sure "experienced" is carrying much weight here. This is why there are benchmarks, anecdote doesn't mean very much.
Where is the evidence? HN is no better than the "I've done my own research" on Facebook.
No you didn't.
who is paying you to say that?
The claim was that there's an <i>experience</i> of a model losing power. Your claim amounts to "No, you are not experiencing what you say". That's quite a claim for you to make with no data and no argument.
I was just wondering if, like certain processors, bugs get fixed and the speed goes down. Like, they find it's doing things it shouldn't, restrict it, and harm the throughput.
Yeh it's absurd that people claim this all the time. It's some crazy conspiracy theory and when you ask for examples nothing ever shows up.<p>It would be economical suicide from anthropic and OpenAI to actually need models intentionally.<p>But hey I guess it's hard with technology that truly seems like magic.
People say if you'd bring electricity to the middle ages you'd be called a witch and burned. The same is happening to the model labs here because they are bringing tech that the world isn't ready for yet.
It's not a hot take at all. Every benchmark shows that.
Anthropic has admitted in the past about bugs in the harness after users complained.<p>Links have been provided by others in this post.
Yeah people push the models to the limits of what they are capable of almost instantly.
Suuuuure...
You used Claude to make some slop to see if Claude is getting worse…?
This explains a lot actually.<p>First two days of this thing was like working with Einstein, then about 24-36 hours ago I started getting frustrated at bullshit that hadn't been a problem before. It was so egregious that I checked to make sure I was still on Opus 5.5 Max.
This is bad data at its finest.<p>Truly, madly, deeply sloppy.
New model releases that have positive reviews should come with a nerfalert reminder service to make hay until it's shaped and shaped and shaped.
This is genius. I’m so worried opus 5.5 will get nerfed cuz sonnet 5 was such trash I can’t go back.
People just tend towards conspiracies you have to actively fight it.
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Nothing nefarious is actually going on here: You have to understand that AI models are like fruit. Fresh fruit can quickly spoil and mold. We all know the meme about strawberries getting moldy before you can even bring them home! This is the reason they regularly bring out new, unspoiled models and also the reason why they will need to keep doing so just to keep model performance at the same level.
I’m pretty sure 99% of what people perceive is the old model training on the usage logs wherever they were stuck.<p>Step 1.
Model can’t do something challenging
Step 2.
You try a bunch and fail
Step 3.
Anthropic trains on your usage data.
Your current code base and current problem are now in domain
Step 4.
Model comes out and you’re shocked when it can tackle the thing you were stuck on
Step 4.
Codebase drifts significantly and you try new problems you thought were a similar level. Your code is less familiar and the problem doesn’t have a bunch of failure cases in the train set.
Feels of it being worse on similar problems
I don't think so, because so called nerfing manifests itself in ridiculous code quality, or even in failing to do a comprehensive code analysis which results in "actually there is a bug in the implementation i've just done because this flow has 5 steps and i didn't bother to review them all before confidently laying down my plan", and this can happen 4 times in a row during a session.<p>This is definitely not about dealing with the frontier of AI. I wasn't part of the nerfing chord, but Astra changed my mind. Quality got me to upgrade from Pro x5 to Pro x20 on launch day. A couple of days later was dumb af, horrendous code quality etc...<p>Something fishy, or at least unethical is going on. Not sure it impacts API users though.
TFA has data.