[throwaw12]

Why Anthropic models are always leapfrogging these benchmarks, but in real life work I do feel like after 3 weeks I am back to Claude Opus 4.5? (regardless of the model I use, Fable was exception for 1 day when it was released)

[OtherShrezzing]

It could be that the set of your day-to-day workload which could feasibly be accelerated by AI just happens to be saturated around Opus4.5, but you can still see lots of “reasoning” which makes you think the model is more performant in the first days of use. That’d mean you couldn’t perceive any meaningful difference in more powerful models’ results, even though you can see a difference in the raw output due to the length of reasoning traces leading up to the result.

So for example, if your workload was literally just addition of sets of numbers, you’d never have noticed progress in the result beyond GPT3.x level models. But you would perceive a difference in the now-Tolstoyan length reasoning text accompanying the result.

[submeta]

Some 20 years ago, the telecommunications sector in Germany was liberalized. Many telephone card providers entered what had previously been a barely competitive market. They advertised their products with aggressive claims like: “Buy our €10 top-up card and get 660 minutes to destination X.”

For the first few weeks, they would actually provide those 660 minutes to establish trust in their cards. But after a while, they would quietly start reducing the number of minutes on subsequent top-ups—say, from 660 minutes down to only 300. They wouldn’t do this for every card, so it was difficult to prove. Instead, they relied on averages across their customer base to make the economics work.

Lately, I’ve found myself wondering whether something similar may be happening with frontier AI models. Companies launch with an exceptionally strong model and generous compute limits to build adoption. Once the model is established as a market leader, the incentives change, and users may start perceiving the service as becoming more constrained or less capable over time.

I don’t have evidence that this is what’s happening with Anthropic—or with any other AI company. It’s simply a pattern that the current situation reminds me of.

[stared]

It's called frog boiling.

We get used to the new level of intelligence so fast, any deviation feels like going back to the stone age.

If you don't believe me, create something complex with Opus 5 and then with Opus 4.5, and notice the difference.

[vmg12]

The actual term for this is hedonic adaptation.

[ffsm8]

esp. important to point that correct term because frog boiling is a urban myth.

frogs dont stay in a pot even if you slowly increase the heat. they leave. it has reportedly been attempted multiple times and they. always. leave.

[SubiculumCode]

5 seems incredibly smart to me in my conversations today about some pretty niche ideas in.longitudinal modeling. It.felt.like a big step up.from 4.8, to me

[kranke155]

5 felt both smarter than me and dumber in some ways - it gets stuck to its original ideas. I had never seen a model harder to talk into changing its initial opinions. it continuously hedges.

[yorwba]

Well, what kinds of things do you see Opus 4.5 completely fail at? Maybe those are not the ones that newer models have improved on.

[lwansbrough]

Going to call it user error if you find Opus 4.5 better than 5, sorry.

[slopinthebag]

Same, like I prefer 5.3 codex over the “stronger” models.

[tudelo]

I honestly just use GPT models nowadays, Claude models are too restrictive and more of a quitter and fable/whatever is just too expensive to be worth it.

[sscaryterry]

Enshittification.

[rf15]

I've worked with these systems for four years now and they have not meaningfully improved in that time frame.

We still have:

- statistical correlation between two things will always cause one thing to lead to the other, no matter how much you prompt it to not have that connection (to be expected with a stochastic system)

- Math completely fails in longer contexts

- "thinking" token generation being on the correct track just to 'no, wait' on an already correct conclusion

- smearing of properties between logically distinct objects (a red ball and a green cube can quickly become a red cube and a green ball)

[IanCal]

> I've worked with these systems for four years now and they have not meaningfully improved in that time frame.

Not meaningfully improved?! Four years ago was gpt *3.5*! ChatGPT hadn’t been released!

[rf15]

Yes! Impressive, isn't it? I see how it has improved for some minor points, that the big models can cover more finetuning ground, but my big gripes are still the same - you could do the same back then with multiple models and more targeted finetuning.

[Marha01]

> you could do the same back then with multiple models and more targeted finetuning

Definitely not, lol.

[Narciss]

I have no idea how you could try and hold this argument without being facetious.

[rf15]

Am I missing something that my original points no longer hold for their products? Did it get meaningfully solved? Are your experiences flawless on that front?

[Kiro]

What do you hope to achieve here? Posting absurd and ridiculous things and then continuing like if someone would take you seriously after that. Keep going I guess.

[thops-barrier]

You're moving the goalposts. Initially it was "no meaningful improvement" and now suddenly it has morphed into "they're not flawless".

I'm pretty sure you're just baiting for engagement though so well done, ya got me.

[yreg]

This is obviously not true to all of us here…

[virgildotcodes]

> you could do the same back then with multiple models and more targeted finetuning

I mean, come on, this is just not true. You could not achieve anything like what you can with modern agentic coding with Fable / 5.6 Sol from any combination or configuration of GPT 3.5 era models.

It's like saying that a teenager isn't an intellectually meaningful improvement over a toddler.

Sure they're both still fundamentally flawed humans prone to cognitive error, but one is clearly more likely to hit the mark than the other when assigned a task.

[UltraSane]

The only thing impressive is how wrong you are. LLMs have improved by an absolutely incredible amount in the last 4 years.

[IanCal]

Utter nonsense.

There’s no way you could get models as smart by fine tuning. I couldn’t throw a problem like “build a pokemon database with UI to teach my son sql” and get a working system, nice ui, tests (which it iterated on) examples and explanations in one shot.

There weren’t thinking tokens. Maths is now dramatically better, making actual contributions when before they were mostly mocked for making extremely basic errors. Smearing is also something say is very rare in frontier models.

If you think they have barely changed you’ve either forgotten what they were like or not used them more recently, or you’re just being obtuse.

[rf15]

My company had such a system four years ago, for internal work, somewhat more limited in scope (one language). What you are seeing as the frontier is not necessarily the best you can have - just because people don't try to push it to market as a product doesn't mean it's not there.

Edit: we do have a system that uses LLM and fixes the above issues largely (tracking of state, calculations and objects, still flawed in finer details). No, we don't sell, it's experimental fun and not really ready in terms of setup/ux/etc.

It codes really well for our case though.

[anonzzzies]

> you could do the same back then with multiple models and more targeted finetuning

Are you one of those anonymous billionaires as if you did this a few years ago, you would've been famous and rich.

[danielbln]

OP is delusional or deliberately optuse. I work in the space and stare down these systems 12h/day, and saying the systems haven't meaningfully improved is ludicrous.

[rf15]

OP is largely pissed with what OAI/Antrophic are trying to sell as meaningful improvements and the market-bending money they ask for it. I work in the space and we trained LLM models on conceptual tokens, not language tokens, for example. See Symbolic AI and all the attempts at hybrid models.

Also, uh, fame and riches are not really my thing. Middle income is fine. My mistake was speaking up here because I got carelessly annoyed because I have skin in the game, research-wise. I'm sorry for that.

[fakwandi_priv]

> - Math completely fails in longer contexts

Not sure what longer contexts we're talking about but didn't we have an old math problem optimized, which even the LLM itself was surprised about, just a week ago? Something which wasn't possible 6 months ago.

[rf15]

I mean calculations, not mathematical proofs

[Barbing]

If they use Python to fill the gap, and the end user doesn’t have to know or care, is it unfair to assess this as progress and attribute the progress to the _system_?

OK, the core technology that is the language model still can’t math as well as you’d hope, but how about the end result users see from the system when they interface with it?

“Did you know humans are better at flying today than they were a thousand years ago?” ‘No they’re not, they need planes.’ Technically correct in a way but isn’t it kind of annoying to be so stubbornly pedantic when the context is speed of reaching Point B from Point A?

[rf15]

You are correct, the frameworks around it have improved. In that regard, my assessment is unfair: I only judge the underlying technology and what is sold by the sota providers, with the premise of what it's like when you start fresh. You can achieve a lot by coding around the issues, but that's kinda against the point of 'AI', is it?

[wahnfrieden]

No, its capabilities with a harness are what we are interested in. Your assessment is only relevant to benchmarking, not practical value.

[Barbing]

orwin‘s response in a cousin comment helped me see your original valid point on harnessless LLMs!

>You can achieve a lot by coding around the issues, but that's kinda against the point of 'AI', is it?

Will think on that a bit more.

[Barbing]

Like how toddlers’ skills don’t meaningfully improve on infants’, because either could wake up in a wet bed.

[mdp2021]

Let us be more clear: there is no structural jump, no architectural overcoming of the original fault.

(Edit: and on a similar point, structural properties such as having static ntetworks, as opposed to continuously learning and improving architectures (such as us), will reveal that there is still road ahead.)

[Barbing]

Maybe more fair then would be: “I've worked with these systems for four years now and while they _have_ meaningfully improved in that time frame, they’re not perfect and remain fundamentally flawed in various ways.”

You prompt less. You need not inject search results into the context window yourself, a window much larger than years ago. You get code that’s already been run successfully once instead of finding an obvious show stopping bug yourself.

The technology is not a brand new one that fixed everything wrong with the old one, no, but not sure I would’ve noticed your comment if it had been such a bland observation. I genuinely assume good faith here… will say am tempted to assume the standards of someone posting such a thing might be impossibly high. Glad to be having a fun conversation instead of getting your grades on my work product or something :)

[orwin]

If you go to a LLM without harness, GP original point in completely right.

LLMS by themselves are still shit at math, they still confuse weird correlation to causation every time (and sometimes in ways even a 9 year old would say "no, that's dumb"), and confuse original parameters very often.

I disagree with " "thinking" token generation being on the correct track just to 'no, wait' on an already correct conclusion", because i think that is an effect of the harness, not the LLMs.

80% off all the improvements since ChatGPT4 are in the harnesses, and the LLMs by themselves, while they improved in areas they already were good at (translation especially) did not fix any of they original issues (object permanence, calculusm correlation).

Just run old models in the playground and get them to play chess (maybe make a small custom harness if you feel like it), then replace it with a frontier model (i don't know if you still have API access without harness on US models, but if you don't try K3), you will see LLMs weaknesses were not at all fixed, even marginally. They're way better and not inducing bugs in the code, so that make them usable since Opus4.5 (anyone using them prior to that either had a greenfield project or like spending hours debugging).

[rf15]

I agree, harnesses is where everyone improved the most. Our internal experimental tool can now semi-reliably formulate small programs to assert the correctness of their theories, for example. Context length is still a weird factor that we haven't sensibly solved - if anything, the lesson learned was to keep the context as small as possible and do most of the true "thinking" in the harness and temporary generated code.

[user43928]

> 80% off all the improvements since ChatGPT4 are in the harnesses

That seems easily falsifiable by putting an old model into the current harness and comparing it to 5.6 Sol or Fable.

[Barbing]

Sounds very fair, thanks :)

[glimshe]

Messages like this in the training data are how LLMs learn to say absurd things with total confidence.

[azan_]

> I've worked with these systems for four years now and they have not meaningfully improved in that time frame.

That's absolutely insane. Is it some case of anti-AI psychosis?