You use Fable 5 right? If that’s good enough for you now, why wouldn’t a Chinese model that’s as good as Fable 5 but at 10% the cost be good enough in 6 months?
I use Claude Code semi-heavily for my small business, and the $100/mo I pay for that is a rounding error compared to the value it provides.
If I can avoid spending an hour or two "massaging" the output from a lower-end model once, or it avoids introducing one load-bearing (sorry, couldn't resist) bug, then that's the entire $100 right there.
Hell, you could argue that the best "coding model" that we have at the moment is the human brain, and people will gladly pay $10,000/mo for one of them.
Arguing over $20 vs $100 for something that actually puts in work just seems insane to me.
Fable 5 is still going to mess things up at any sufficient complexity. The advantage of low cost models with "good enough" intelligence is they can recursively correct. Why? Because it is cheap. Proper requirements and tests and subagents take away increasing amounts of work, at a cost that is not prohibitive.
If you are reviewing code manually you might consider Fable 5 a worse option. As it articulates itself with higher confidence and you already know it is capable, you are may be more likely to miss a mistake. You know to be on guard with a junior engineer. Reviewing a senior who suddenly makes some weird stochastic mistake can be a lot harder. It would be like if the smartest human engineer you knew was capable of some random brainfart in the middle of their massive diff. Imo, much harder to deal with.
Of course, we should keep in mind Fable 5 is only expensive today. It will be cheaper in the future. Autonomous, recursive prompting and improvement is the clear end state. Especially for entities that will always have the budget for that at the SOTA frontier.
Which was an argument for using every less powerful model since the moment they got useful, right?
When was that? Opus 4.5 maybe? Let's say Opus 4.5 for the sake of the argument. So back then we were like "DeepSeek is not good enough, I need Opus 4.5". Now DeepSeek is better than Opus 4.5. So if Opus 4.5 was good enough back then, DeepSeek is better than that now.
Sure, it's always nicer to have a slightly better model. But the price difference starts mattering a lot more when all the models are already sufficiently good.
We've just spun up our first Hermes agent, with direct API access to our main inventory system and that's expected to find another few grand per month in misallocation/inefficiency.
I wouldn't be surprised if we were doing more like $10k/mo higher in 6-9 months' time.
When you're talking about numbers like this, the fact that one AI is $100/mo and another is $10/mo or $40/mo doesn't matter. They could make GLM-5.2, or any other Opus 4.5-class model free and it still wouldn't make sense to deploy in a commercial context.
The other angle I'd approach things from is that Opus 4.5 (and I'd agree with you that that model was the saddle point) was "good enough" for the types of things we were asking it to do back then, but as the models have become more capable the tasks we're asking them to do have also expanded with it.
I know I've personally gone from "hey can fix this race condition with a Redis mutex" 6 months ago to "Independently redesign this full embedded USB stack and QA it end-to-end, working around a specific Kernel bug in macOS Tahoe that requires decompilation to find the source of, while keeping in mind the constraints of our 8-bit AVR chip from 2011" now.
But that said, yes, maybe in 5 years' time we will reach an "intelligence saturation" where the average person won't be able to even conceive of how to use the new SOTA.
Surely they are using read-only access.
This is such a ridiculous objection for how beloved it is. Wide swathes of the public can't cope with any adversity or risk.
With an agent (especially incompetently employed), the danger of unwittingly destroying your company (or at least, the crucial data/reputation) is rather higher. We are notoriously bad at estimating the downside risks in complex systems.
The most obvious case is the downside risks in complex financial constructs... things look great for a while ... until a sudden surprising collapse arrives and totally destroys all the upside you think you have created.
In any case, GP's post was not such a balanced consideration; it was just parroting a beloved risk-aversion meme that can easily be deployed against building anything (what if the building falls on top of someone?) or even leaving home to go to work ("travelling in a hunk of steel at lethal speeds – let me assure you that absolutely nothing can go wrong here, mate.")
What I find tiresome about that meme is the presumption that "something can go wrong" is useful input on its own. It's not. Mistakes are made all the time, the only way to avoid that is to stop breathing. Even in the process of me standing up and going to the loo, something can go wrong.
If the guy wants to make a case that it's too dangerous for the expected benefits, he has to actually make that case. Saying "risk exists" with no elaboration is a waste of HTML. "something can go wrong" every time he swallows food, yet mysteriously he still does it.
(the suicide analogies may seem mean-spirited, but I kind of mean it. If you consider every action primarily from a standpoint of "what harm or irreversible change can result from this", the only permissible path is to do nothing. To be moral is to be as close as possible to a rock or another inanimate object.)
Are you saying we shouldn't care about the future of affordability and access because at this moment we have seemingly endless access?
Sounds extremely short sighted.
If $100 Claud Max subscription works for you, then great.
But you have to remember your pricing is subsidized by enterprises that pay hundreds of thousands of dollars each month, if not more, to Anthropic.
For those companies, a Chinese model that can cut their AI spend from $1M/month to $200k suddenly seems attractive.
And unfortunately for the American tech industry, the valuation is based off those enterprise deals, not your $100/month Claude Max subscription.
This is made brutally obvious by anthropics customer support for people with such accounts.
Right now the US dominates everyone else in actual chips in data centers. So even if deepseek etc tries to undercut, they’re very capacity limited.
It's that good. They are far from capacity limited, and even if they were, you can rent a single MI300X from somewhere like Hot Aisle and get more tk/s than you'll be able to use.
That one is dirt cheap at API pricing, I can't imagine quota is going to be a concern on the $200 subscription, which in my opinion easily supports full time use of 5.6 Sol on xhigh.
A couple of very talented friends were uttering curses upon the entire bloodline of whoever convinced them to try letting sol xhigh do serious work. Deepseek cleaned it up for a fraction of $20.
The cost per task was $0.03 with DeepSeek, $0.05 with Luna. $1.23 for Sol.
Tokens per second 132, 202, 70 respectively.
They also previously said prices will go down significantly once they get a hold of the upcoming Huawei chips (later this year).
Prices are going up just because they can. It can easily come back down. They aren't strained by some IPO / VCs requiring them to 1000x their earnings.
Think about it this way.
Let’s say you could buy an LLM that gets things right 98% of the time. But there’s another LLM that’s 100x the price but gets things right 99.9% of the time. To the lay person this sounds trivial but to a serious business this intelligence gap could represent millions, or billions of dollars.
But that’s simply not the case. It’s very clear that vast majority of the business do not generate additional value from incremental intelligence gain from these models.
There is a reason why Chinese open weight models are now popular even in American enterprises, because CTOs realize that they are indeed good enough.
The Chinese models are not good enough for anything other than pair programming, which is just a very last-gen way of using agents.
And when the big US models get better we will move with them. Until we stop seeing returns there is no "good enough", I don't know why this is so hard for HN to understand.
And even it isn't "enough". I can very clearly see myself using more advanced agents to move up the abstraction ladder.
For businesses that have actual problems to solve, I see them investing in the frontier for a good bit longer, probably until we have AGI that can replace employees, maybe even a bit after.
This is why I find the "good enough" arguments silly. Like, the usefulness of an AI tops out to you when you can pair program with it? Seriously? You cannot envision ways in which more advanced AI enables you to do more, better? That's bizarre to me. I don't ever see myself running out of problems to solve.
Is this what you are looking forward to?
This matches my experience with DeepSeek V4 Pro at Max reasoning, the preview version of the model kept regularly messing things up. About 30-60% of additional time to fix the output was needed.
On similar tasks, GLM 5.2 at Max reasoning screwed up maybe 20-30% of the time, while it still definitely made noticeable mistakes, they were far fewer in total and less egregious.
Kimi K3 at Max reasoning drops that value to below 10%, it's about as good as Opus or approaches Fable in some tasks. At High reasoning it also seems to be pretty close to Opus 4.8, not sure about the latest Opus model yet, but it's up there.
Only problem is that K3 is nowhere near as cheap as DeepSeek models, despite me personally liking the writing tone more (less Anthropic slop) and finding that it doesn't block my cybersecurity prompts, recently reproduced SQLi with a proof of context so I could justify fixing it.
My overall thoughts (released over some time):
https://blog.kronis.dev/blog/ai-slop-is-a-self-inflicted-tra...
https://blog.kronis.dev/blog/kimi-k3-is-out-is-anthropic-don...
https://blog.kronis.dev/blog/z-ai-s-glm-5-2-is-a-great-model...
I'd say as Chinese models get better, whatever moat Anthropic and OpenAI have dissipates. Currently the main things keeping me with Anthropic are their performance (tokens/second) and the fact that their visualization abilities within the app are pretty good.
That was ages ago (in LLM release timelines). DeepSeek V4 Flash beats it now and a lot cheaper.
> On similar tasks, GLM 5.2 at Max reasoning screwed up maybe 20-30% of the time,
GLM 5.3 bridges this gap.
> I'd say as Chinese models get better, whatever moat Anthropic and OpenAI have dissipates.
Their moat, especially OpenAI is funding and hardware resources. They gain train models 10x as large and also serve at large scale. That's it.
I’m sure the next models will only get better, when they’re released. Also super curious about what Moonshot will achieve and the full DeepSeek V4 Pro release!
> Their moat, especially OpenAI is funding and hardware resources. They gain train models 10x as large and also serve at large scale. That's it.
I’ve seen how much slower Kimi K3 can be and that part seems correct, their own GPU production still has ways to go and export restrictions definitely limit what they can do.
Not sure about the size part, if Kimi K3 achieves SOTA performance at 2.8T parameters, western models being >2x that size would be insanely bad in regards to efficiency. I bet they’re all within the same order of magnitude and below 10T and won’t really have a reason to go even that high for the foreseeable future.
As investors will start squeezing them for profitability, I suspect focusing more on efficiency will be commonplace.
They're a lot larger e.g. Fable. It is insanely bad. Do you know how much more resources "Western" companies have? Most in China don't have random GPUs to "play with" like every "frontier lab" employee does.
> As investors will start squeezing them for profitability, I suspect focusing more on efficiency will be commonplace.
They're born lucky though. Efficiency is "free". The next generation hardware e.g. Nvidia claims Blackwell -> Rubin is 10x efficiency (verified by Neoclouds apparently).
I do have a way of correcting through redundancy, though. If you are just vibe coding, you need to use the most capable model you can find and even then it might not be good enough.
I mean, this proves my point. Better models enable you to get more done. With Fable, 80% of the time, I no longer have chat with an agent over the details of a PR. I give it an outcome and it gets done. This means I can work on much more with the limited time I have.
And I don't see this ending. When better models come out that take that from 80% to 99.x%, I will have that better model manage teams of other models and move up the abstraction layer.
If models get even better than that, perhaps I stop reviewing PRs entirely. Maybe normies can start using agents to build real things.
Unless your business doesn't have many problems to solve and isn't in a competitive environment, it will benefit from using the best models.
My point is you don’t need the best model if you just put in QA processes that can be done by models also. And if you don’t have that, the model is probably not going to be good enough.
Fable has only been out for a month but somehow everyone is supposed to have moved to a completely different way of working that supposedly only works for Fable and nothing else…
This kind of takes makes me cringe. Why don't you go back to LinkedIn?
I have no idea what you mean by “pair program with an agent”, but Opus have been able of autonomous coding since last November, and with any half-decent harness even local Qwen3.5 was able to do so 6 months ago.
Fable is a stronger model, which means it can solve harder tasks but it's also over-hyped, because only a small fraction of task is hard enough to be Fable-worthy.
Fable is the only one that reliably one shots complex changes and makes the right design choices. Everything else requires handholding.
I can let Fable loose on a 12+ hour (for AI) task and it will have performed it flawlessly when I come back the next day. K3 and Opus are not like this.
And no, our harness is not the limiting factor here.
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There are businesses other than FAANG. I don't know why this is so hard for FAANG employees to understand.
It's been 2 months since Fable was released to the general, man. Nobody knows what's going on inside of these companies except the people at the coal face.
It's funny to see that Anthopic shills have been saying the exact same thing for the past two years now (and it was OpenAI fans before). It's amazing to see that Claude 3 Sonnet was "great" but now that even Qwen 9B is better than this version of Sonnet DeepSeek V4 is still not good enough despite being stronger than Opus 4.7 was.
> Let’s say you could buy an LLM that gets things right 98% of the time. But there’s another LLM that’s 100x the price but gets things right 99.9% of the time
If you think Fable makes 20 times fewer mistakes than DS4 you're delusional. It doesn't even do 20 fewer mistake than Gemma 4…
Same reason it makes sense to assign a team of humans that cost $100k/mo to a product that brings in $5M/mo, rather than one human with 5 Claude Max subs.
The cost is a rounding error.
This is why I quite like Kimi K3 - close to the same performance (definitely like Opus, approaching Fable), noticeably cheaper, generally good enough for me to daily drive. Only problem is that their official provider (on the Vivace plan) feels kinda slow, I'd say close to 2x slower than Opus on Max reasoning on average (probably more relatable than Fable).
You can say this about literally every product we buy. And yet...