IMO this comes down to your harness. Any frontier model from a huge shop has an inherent benefit in the system you're using it in. Search, memory, skills, integrations you don't realize even exist make them much more powerful. It is some effort but I recommend trying Hermes Agent and setting it up fully, that's the closest you'll get to a more complete experience.

Really!? Glm5.3 is my daily driver and I feel im having the most productive experience with agentic collaborations so far, by a lot. Using pi with tons of custom extensions, that to be fair I developed since making the jump off of codex and claude about 12 weeks ago. I primarily do not write code for a living. I do a lot of modeling and commercial analysis and a lot of math (related to differentiable simulation)

Whenever I see this comment I'd wish they'd preface it with their hardware.

Yeah, expecting the world when all you have is a 8GB graphics card? You're going to be disappointed.

16GB is table stakes (IQ3_XSS). 32 GB is better.

Which models did you try for which tasks?

Qwen3.8-Flash-Next seems pretty much auto pilot when I get it the right context.

Perhaps reverse the question: Are your build/construct requirements just really counter-productive to how LLMs need to understand things?

I've found constructing the code, writing the tests, adding the docs; then running through them gets most of the way there.

I've also found that making a simple obvious edit is a useless endevour when the LLM is primed for the long context tasks.

So, again, the question is reversed: are you over reliant on the LLM to do even stupid simple likes like editting a css variable?

Qwen 3.8 Flash-Next is not dumb. If you've used it and that was your experience, your workload is either ultra-ultra-sophisticated or you're dealing with a broken quant/buggy chat template/other issue. That model is a smart, reliable workhorse.

Combination of: hardware, model, harness, tool-use by the model.

My calculation (that is 3x 20x subscriptions) it will pay off after ~18.5 months, if I were to stop my subscriptions today. I will likely keep at least one though, so it's more like 27.5 months for a payoff. I am not doing it to save money though. I am doing it for security of supply. Constant quality - I know my model isn't getting lobotomized etc. - and open weight models mostly just lack very little behind. I'm sure I will have affordable, fast and efficient sol 5.6 capabilities with open weight models on my sparks within 12-18 months easily.

I have 2 x ChatGPT Pro 20x, Claude Max 20x, and Kimi Vivace. It's about ~12 months payback for two units and the cable.

The problem is they can't fit any frontier level open models.

Last time I estimated, it would only take 3 months to pay back because the 1TB Mac Mini running Qwen RSIingly developed ASI and made infinity dollars off of crypto and I got put in jail by the SEC.

Where'd you get 30 years from? Show your work.

reliability and self reliance is worth a lot to most. Heck, you could be the best in the world at what you do, but if you're unreliable you wont find stable employment. So, not having some amoral shady company errode model quality out from under you constantly is also worth a lot more than simple cost balancing calculations can capture.

I'm getting really sick of the constant rot and "magic breakthrough" cycle, so im going full local, at expense on paper but being able to trust something which I need to understand the reliability of is priceless.

I like predictable. I'll take slightly less capable over unreliably capable, since with reliable i can calibrate my expectations and learn what aspects of my workflows to entrust and trust it will work. You simply cannot do that with models you don't control and in my experience they will all errode after the initual marketing wave passes, likely you eventually get fed heavily quantized versions and are expected to accept degraded service when what convinced you to pay was a far superior product. No such issues with local.

> Yet… even Altman called out Anthropic for serving dumbed down models.

Even Altman called out Anthropic? Isn't Anthropic the biggest competitor Sam Altman has?

I wouldn't be so sure. The generosity of the subscription plans has declined GREATLY over the past 6 months or so. They are likely trending towards api pricing parity. In which case, having your own hardware makes sense if you can utilize it well.

There could be gym logic at play. Hundreds signed up, 20 people actually exercising. Though it's probably more likely in the lower tiers.

> * Tune it down over time so that you end up only scoring 75 on the benchmark

Where?

I see so many accusations of this happening and it's so easy to check, but nobody ever proves it.

Why can't the models be benchmarked again after a few weeks/months to confirm this (likely true) theory?

I imagine some people have their own personal in depth benchmarks they could do this for.

We are being A/B tested on and there is nothing you can do about it.

Oh, yes there is. DeepSeek 4.1 Flash on max thinking can simply be dropped into Claude Code. Close your eyes as the chain-of-thought traffic scrolls by and you can easily fool yourself into thinking you're still running Opus, in terms of both cognition and throughput.

To be fair, matching Opus's throughput costs about as much as a new car, but cars suck nowadays and you didn't want a new one anyway, right...? Failing that, rent a cloud server, one that you control.

> might they all pursue this kind of deception

They might but multiple competitors engaging in ongoing deception as an intentional corporate strategy isn't required to explain what we're seeing. It's entirely possible to get the same clearly unethical outcome without any employees knowingly participating in an explicitly unethical plan of record.

Instead it happens without overt coordination when individuals and groups within an org each pursue their local metrics and incentives. In isolation, no individual action seems obviously unethical on its own. They just look like 'optimizing performance', 'maintaining ASP or ARPU targets' or 'achieving operating margin', etc. Customers are still getting deceived and receiving less for their money than they think. The difference is most of the people involved in enabling it get to not feel bad about themselves.

See Shepard tone. Similarly model releases could be engineered to appear that they’re always getting better by slowly degrading and upgrading at the right time. That plus hitting some benchmarks and making a lot of noise around that.

Kinda. Off the top of my head, DeepSeek and their thinking model was pretty new and interesting. Multi input models are also newish (combined input of text, image, video, audio, etc). Then there's Jev, a recently release that has a lot of people talking. It isn't really an LLM, but also is one.

Sam Altman believes he can train a model entirely on synthetic data, which he admits would not have human world knowledge but is interesting none the less, which likely led to their mathematical models.

Overall models have become cheaper to run and smarter per token.

Some documented examples along with confirmation from the vendors themselves:

https://www.tomshardware.com/features/crucial-p2-ssd-qlc-fla...

https://www.tomshardware.com/news/wd-blue-sn550-ssd-performa...

https://www.tomshardware.com/news/adata-switches-nand-on-sx8...

AI, if not AGI, has certainly become uniquely dangerous in the past 6 months though. We have a lot of evidence to that effect.

The best case scenario is a situation like Y2K: a ton of people coordinate and work hard to produce no perceptible change, because unlike catastrophe, averting catastrophe feels boring.

>Change is fast and abundant, and at the same time, it is hilariously more mundane than the dangerous-AGI-in-six-months tune we've been reading daily for years.

Absolutely none of this points to “stagnant.” Stagnant is a terrible description of the AI industry.

This is wild to me, but to each their own. Mythos-class stuff is insanely better at nearly everything than Opus 4.5 was in my experience.

Same experience here, anything frontier human knowledge wise, same if not a regression. For human understanding and emotional intelligence, for many tasks regressiin is so bad that many near anchient llama era models now beat frontier anthropic/oai models. Notable exceptions to capability rot seem to be qwen models, and previously deepseek but the latest gen of models has started showing the same rot. General writing quality is down significantly accross the board, often it is outright ass. For example, I didnt mind reading 4.5's outout, but opus 5 makes me goddamn near violent, its fucking insufferable.

True. New coke is a good example. So was unity licensing. But these even feel slow motion compared to the Anthropic rise and self immolation.

So are they making big breakthroughs, or are they stagnating?

Raising prices also has second order effects, like consumer and business expectations around how widespread the tech can be. Valuations depend on it being reasonably affordable to roll out on a much more massive scale than today. If people get the impression that it seems too limited to very rich people (200 is affordable for a North American / Western European professional), the impression about the trajectory will change.

This really depends where the load shedding point is.

A very small raise in prices may cause a very large loss in customers that you risk never getting back.

For example if customers figure out that the Chinese models are just as good, they are gone because they are so much cheaper.

Uhh, they're expressing incredulity, not a lack of reading comprehension.

Unlike you, however...