[eterm]

That is a well recognised part of the LLM cycle.

A model or new model version X is released, everyone is really impressed.

3 months later, "Did they nerf X?"

It's been this way since the original chatGPT release.

The answer is typically no, it's just your expectations have risen. What was previously mind-blowing improvement is now expected, and any mis-steps feel amplified.

[quentindanjou]

This is not always true. LLMs do get nerfed, and quite regularly, usually because they discover that users are using them more than expected, because of user abuse or simply because it attract a larger user base. One of the recent nerfs is the Gemini context window, drastically reduced.

What we need is an open and independent way of testing LLMs and stricter regulation on the disclosure of a product change when it is paid under a subscription or prepaid plan.

[landl0rd]

There's at least one site doing this: https://aistupidlevel.info/

Unfortunately, it's paywalled most of the historical data since I last looked at it, but interesting that opus has dipped below sonnet on overall performance.

[dudeinhawaii]

Interesting! I was just thinking about pinging the creator of simple-bench.com and asking them if they intend to re-benchmark models after 3 months. I've noticed, in particular, Gemini models dramatically reducing in quality after the initial hype cycle. Gemini 3 Pro _was_ my top performer and has slowly reduced to 'is it worth asking', complete with gpt-4o style glazing. It's been frustrating. I had been working on a very custom benchmark and over the course of it Gemini 3 Pro and Flash both started underperforming by 20% or more. I wondered if I had subtle broken my benchmark but ultimately started seeing the same behavior in general online queries (Google AI Studio).

[Analemma_]

> What we need is an open and independent way of testing LLMs

I mean, that's part of the problem: as far as I know, no claim of "this model has gotten worse since release!" has ever been validated by benchmarks. Obviously benchmarking models is an extremely hard problem, and you can try and make the case that the regressions aren't being captured by the benchmarks somehow, but until we have a repeatable benchmark which shows the regression, none of these companies are going to give you a refund based on your vibes.

[judahmeek]

How hard is benchmarking models actually?

We've got a lot of available benchmarks & modifying at least some of those benchmarks doesn't seem particularly difficult: https://arc.markbarney.net/re-arc

To reduce cost & maintain credibility, we could have the benchmarks run through a public CI system.

What am I missing here?

[Maxious]

Except the time that it was to the point Anthropic had to acknowledge it? Which also revealed they don't have monitoring?

https://www.anthropic.com/engineering/a-postmortem-of-three-...

[jampa]

I usually agree with this. But I am using the same workflows and skills that were a breeze for Claude, but are causing it to run in cycles and require intervention.

This is not the same thing as a "omg vibes are off", it's reproducible, I am using the same prompts and files, and getting way worse results than any other model.

[eterm]

When I once had that happen in a really bad way, I discovered I had written something wildly incorrect into the readme.

It has a habit of trusting documentation over the actual code itself, causing no end of trouble.

Check your claude.md files (both local and ~user ) too, there could be something lurking there.

Or maybe it has horribly regressed, but that hasn't been my experience, certainly not back to Sonnet levels of needing constant babysitting.

[F7F7F7]

I’m a x20 Max user who’s on it daily. Unusable the last 2 days. GLM in OpenCode and my local Qwen were more reliable. I wish I was exaggerating.

[mrguyorama]

Also people who were lucky and had lots of success early on but then start to run into the actual problems of LLMs will experience that as "It was good and then it got worse" even when it didn't actually.

If LLMs have a 90% chance of working, there will be some who have only success and some who have only failure.

People are really failing to understand the probabilistic nature of all of this.

"You have a radically different experience with the same model" is perfectly possible with less than hundreds of thousands of interactions, even when you both interact in comparable ways.

[olao99]

Just because it's been true in the past doesn't mean it will always the case

[ojr]

Opus was a non-deterministic probability machine in the past, present and the foreseeable future. The variance eventually shows up when you push it hard.

[spike021]

Eh, I've definitely had issues where Claude can no longer easily do what it's previously done. That's with constant documenting things in appropriate markdown files well and resetting context here and there to keep confusion minimal.