It could even just be that they apply rate limits and that this degrades the effectiveness of the feedback loop between the person and the model.
That said I don’t go beyond 70% of my weekly limit so there’s that.
Once I tested this, I gave the same task for a model after the release and a couple weeks later. In the first attempt it produced a well-written code that worked beautifully, I started to worry about the jobs of the software engineers. Second attempt was a nightmare, like a butcher acting as a junior developer performing a surgery on a horse.
Is this empirical evidence?
And this is not only my experience.
Calling this phychological is gaslighting.
Users are not reliable model evaluators. It's a lesson the industry will, I'm afraid, have to learn and relearn over and over again.
The only thing that matters and that can evaluate performance is the end result.
But hey, the solution is easy: Antrophic can release their own benchmarks, so everyone can test their models any time. Why they don’t do it?
On a diverse set of multi-step software and reasoning tasks, we record the time needed to complete the task for humans with appropriate expertise. We find that the time taken by human experts is strongly predictive of model success on a given task: current models have almost 100% success rate on tasks taking humans less than 4 minutes, but succeed <10% of the time on tasks taking more than around 4 hours. This allows us to characterize the abilities of a given model by “the length (for humans) of tasks that the model can successfully complete with x% probability”.
For each model, we can fit a logistic curve to predict model success probability using human task length. After fixing a success probability, we can then convert each model’s predicted success curve into a time duration, by looking at the length of task where the predicted success curve intersects with that probability.
If model A generates 10 tokens a second and model B generates 100 tokens a second, then using real LLM inference time puts A at a massive 10x advantage, all other things equal.
If it takes me 8 hours to create a pleasant looking to-do app, and Gemini 3 can one shot that in 5 minutes, that's certainly impressive but doesn't help me evaluate whether I could drop an agent in my complex, messy project and expect it to successfully implement a large feature that may require reading docs, installing a new NPM package, troubleshooting DB configuration, etc for 30 min to 1 hr without going off the rails.
It's a legitimate benchmark, I'm not disputing that, but it unfortunately isn't measuring the area that could be a significant productivity multiplier in my day-to-day work. The METR time horizon score is still susceptible to the same pernicious benchmaxxing while I had previously hoped that it was measuring something much closer to my real world usage of LLM agents.
Improvements in long duration, multi-turn unattended development would save me lot of babysitting and frustrating back and forth with Claude Code/Codex. Which currently saps some of the enjoyment out of agentic development for me and requires tedious upfront work setting up effective rules and guardrails to work around those deficits.
Criticizing it for “not being scientific” is irrelevant, I didn’t present it as science. Are people only allowed to share experiences here if they come wrapped in a peer-reviewed paper?
If you want to debate the substance of the observation, happy to. But don’t rewrite what I said into a claim I never made.
Feeling lucky?
Why in the world, if I'm paying the loss leader price for "unlimited" usage of these models, would any of these companies literally respect my preference to have unfettered access to the most expensive inference?
Especially when one of the hallmark features of GPT-5 was a fancy router system that decides automatically when to use more/less inference resources, I'm very wary of those `/model` settings.
Whether something is a bug or feature.
Whether the right thing was built.
Whether the thing is behaving correctly in general.
Whether it's better at the very moment that the thing occasionally works for a whole range of stuff or that it works perfectly for a small subset.
Whether fast results are more important than absolutely correct results for a given context.
Yes, all things above are also related with each other.
The most we have for LLMs is tallying up each user's experience using an LLM for a period of time for a wide rane of "compelling" use cases (the pairing of their prompts and results are empirical though right?).
This should be no surprise, as humans often can't agree on an end-all-be-all intelligence test for humans either.
Why? Because humans suck.
Look, I'm not defending the big labs, I think they're terrible in a lot of ways. And I'm actually suspending judgement on whether there is ~some kind of nerf happening.
But the anecdote you're describing is the definition of non-empirical. It is entirely subjective, based entirely on your experience and personal assessment.
Well, if we see this way, this is true for Antrophic’s benchmarks as well.
Btw the definition of empirical is: “based on observation or experience rather than theory or pure logic”
So what I described is the exact definition of empirical.
Unless he was able to sample with temperature 0 (and get fully deterministic results both times), this can just be random chance. And experience as SWE doesn't imply experience with statistics and experiment design.
The way this works is:
1) x% of users have an exceptional first experience by chance. Nobody who has a meh first experience bothers to try a second time. 2) x²% of users also have an exceptional second experience by chance 3) So a lot of people with a great first experience think the model started off great and got suddenly worse
Suppose it's 25% that have a really great first experience. 25% of them have a great second experience too, but 75% of them see a sudden decline in quality and decide that it must be intentional. After the third experience this population gets bigger again.
So by pure chance and sampling biases you end up convincing a bunch of people that the model used to be great but has gotten worse, but a much smaller population of people who thought it was terrible but got better because most of them gave up early.
This is not in their heads- they really did see declining success. But they experienced it without any changes to the model at all.
The first time you see a dog that can make pancakes, you’re really focused on the fact that a dog is making pancakes.
After a few weeks of having them for breakfast, you start to notice that the pancakes are actually kind of overcooked and don’t taste that good. Sure it’s impressive that a dog made them, but what use are sub-par pancakes? You’re naturally more focused on what it can’t do than what it can.
The real issue is that there is no reliable system currently in place for the end user (other than being willing to burn the cash and run your own benchmarks regularly) to detect changes in performance.
It feels to me like a perfect storm. A combination of high cost of inference, extreme competition, and the statistical nature of LLMs make it very tempting for a provider to tune their infrastructure in order to squeeze more volume from their hardware. I don't mean to imply bad faith actors: things are moving at breakneck speed and people are trying anything that sticks. But the problem persists, people are building on systems that are in constant flux (for better or for worse).
There was one well-documented case of performance degradation which arose from a stupid bug, not some secret cost cutting measure.
I have seen multiple people mention openrouter multiple times here on HN: https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...
Again, I'm not claiming malicious intent. But model performance depends on a number of factors and the end-user just sees benchmarks for a specific configuration. For me to have a high degree of confidence in a provider I would need to see open and continuous benchmarking of the end-user API.
People are claiming that Anthropic et all changes the quality of the model after the initial release, which is entirely different and the industry as a whole has denied. When a model is released under a certain version, the model doesn’t change.
The only people who believe this are in the vibe coding community, believing that there’s some kind of big conspiracy, but any time you mention “but benchmarks show the performance stays consistent” you’re told you’re licking corporate ass.
For example, in diffusion, there are some models where a Q8 quant dramatically changes what you can achieve compared to fp16. (I'm thinking of the Wan video models.) The point I'm trying to make is that it's a noticeable model change, and can be make-or-break.
That’s not the point — it’s just a day in the life of ops to tweak your system to improve resource utilization and performance. Which can cause bugs you don’t expect in LLMs. it’s a lot easier to monitor performance in a deterministic system, but harder to see the true impact a change has to the LLM
That's case #2 for you but I think the explanation I've proposed is pretty likely.
They could publish weekly benchmarks. To disprove. They almost certainly have internal benchmarking.
The shift is certainly real. It might not be model performance but contextual changes or token performance (tasks take longer even if the model stays the same).
I do suspect continued fine tuning lowers quality — stuff they roll out for safety/jailbreak prevention. Those should in theory buildup over time with their fine tune dataset, but each model will have its own flaws that need tuning out.
I do also suspect there’s a bit of mental adjustment that goes in too.
"There's something still not quite right with the current technology. I think the phrase that's becoming popular is 'jagged intelligence'. The fact that you can ask an LLM something and they can solve literally a PhD level problem, and then in the next sentence they can say something so clearly, obviously wrong that it's jarring. And I think this is probably a reflection of something fundamentally wrong with the current architectures as amazing as they are."
Llion Jones, co-inventor of transformers architecture
Conclusion: It is nerfed unless Claude can prove otherwise.
I was having really nice results with the o4-mini model with high thinking. A little while after GPT-5 came out I revisited my application and tried to continue. The o4-mini results were unusable, while the GPT-5 results were similar to what I had before. I'm not sure what happened to the model in those ~4-5 months I set it down, but there was real degradation.
However, benchmarks exist. And I haven't seen any empirical evidence that the performance of a given model version grows worse over time on benchmarks (in general.)
Therefore, some combination of two things are true:
1. The nerf is psychologial, not actual. 2. The nerf is real but in a way that is perceptual to humans, but not benchmarks.
#1 seems more plausible to me a priori, but if you aren't inclined to believe that, you should be positively intrigued by #2, since it points towards a powerful paradigm shift of how we think about the capabilities of LLMs in general... it would mean there is an "x-factor" that we're entirely unable to capture in any benchmark to date.