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Comment by LaurensBER | Hacker News Reader

I've been using it extensively since the release and the best summary I can give is that it's good enough to use it for (almost) everything and cheap enough that the cost are irrelevant. I'm running it in Oh My Pi with a second instance running as "advisor" and even with 5-6 active sessions (effectively 12 streams) I'm struggling to spend more than 5 bucks per day.

OpenCode Go even has double limits temporarily so for 10 USD you effectively get 140 USD of tokens to spend. It would impress me if someone could burn that amount with "normal" usage. Even when running multiple sessions.

I have a Claude Max subscription but I've barely touched it, it just feels like a step back to have to think about limits and usage even though the models are stronger.

The beauty of intelligence at this cost (even if it's not SOTA) is that it opens a whole bunch of new use cases. Test failure in CI? Have the bot automatically propose a fix, its cheap enough that you can discard it w/h issues. Test coverage too low? Auto generate tests on CI for every pull-requests! Monitoring server logs, continuous security audits and investigating every received exception now becomes possible.

I'm thinking about having it automatically filter and re-rank my social media feeds so I can steer the algorithm instead of the other way around.

Perhaps other people (with enormous budgets) were already doing all of the above but for us this is a really exciting release!

View on HN

If what you're saying is true and accurate, then US-based AI labs are in big trouble. The only saving grace might be some sort of a 'national security' proclamation banning the use of state-of-the-art Chinese (and non-US) models across US federal and state governments and large enterprises (especially ones with federal government contracts), but even still, US AI labs will probably lose out massively on international market if a smaller model can match SOTA of just a few months ago.

There's no way large companies outside the US will pay the "US AI lab" premium if they can get the same workloads done at a fraction of the cost using open-weight models that they can self-host and optimize/fine-tune on.

I think you're overlooking the fact that for long-horizon tasks, even small errors compound over time and can lead to catastrophic outcomes.

For simple queries, we have reached the threshold since the beginning of the year, and models are good enough from every provider to make a meaningful difference between one another. (ChatGPT, Claude, Gemini, Grok, MuseSpark, Kimi, DeepSeek, GLM...)

The real unlock will be, and you can already see it with GPT-5.6 and Fable-5, to delegate complex enough tasks that will take more than 24 hours to get done and they will not lose track. I'm not talking about a loop, but the actual intelligence to recover from these compounding errors that accumulate in dumber models.

We're still a long way from the intelligence needed to let one of these agents go ahead and supervise multiple layers of sub-agents underneath to do complex orchestration. The future looks very promising and exciting. Imagine having the possibility of a Frontier model orchestrating as many sub-agents as needed that are running on cheaper models like DeepSeek.

That doesn’t make sense. It’s not like SOTA models are error free, yet we still use them.

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 think we put up with Fable's occasional hiccups because there's nothing better at the moment.

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.

> I think we put up with Fable's occasional hiccups because there's nothing better at the moment.

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.

To put actual numbers on it, since using AI to start solving all kinds of bottlenecks/inefficiencies in our small business, we've seen monthly net profit go up by around $4,000 USD. These are semi-permanent fixes, and the tech is only partially deployed. I am the only one using it, and I only use it part time.

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.

I think we're even starting to reach that saturation point now for a lot of people. In my industry (law) plenty of people have tried CoPilot once or twice, or tried ChatGPT a year ago, and as a result have basically dismissed AI as being useless. The setup required to be able to get it to do end to end tasks to your liking is also substantially more work than most people are willing to put in.

I find 500x returns on my $20 Chat GPT and Claude subscriptions as I litigate pro se against large law firms in federal court.

Case numbers? I'd love to take a look at the dockets.

I think HN doesn't really understand fixed costs, I spend a few hundred dollars per day on Fable and the costs are irrelevant compared to what we make.

I've never understood this line of thinking...

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.

No, its that going from $2k/mo to $20/mo is not worth the capability loss in the slightest, because AI just isn't a big part of our fixed costs.

"оur first Hermes agent, with direct API access to our main inventory system" – let me assure you that absolutely nothing can go wrong here, mate. /s

Don't you think that's a bit of an inane comment to make about a setup you know nothing about?

Surely they are using read-only access.

We are obviously limiting access to be read-only for anything customer-facing (it will be able to put recommendations in the dashboard but not actually change things directly) but I think the point of GP's comment was literally just to get a reaction.

Did you mean to post this on Reddit instead of HN?

I get what you were trying to say, but the quality difference is actually extremely low.

When you do things in life, sometimes things go wrong. Oh no!

This is such a ridiculous objection for how beloved it is. Wide swathes of the public can't cope with any adversity or risk.

No, the point is that with human in the loop the downside is (usually!) rather limited, as common sense would stop obvious fuckups (ok, not always, but still).

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.

There is a tradeoff to be considered between the utility gained from using the stuff, minus the risk severity/likelihood, plus available mitigations. As someone else posted, none of us are in a position to make that balanced post because we don't know if the guy has an airgapped backup or not etc.

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.)

An agent will read your comment and take it as a challenge to show something can go wrong. Thanks for prompt injecting, mate. /s

The question low cost models will create: Why would you massage output?

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.

Except Fable won’t be costing $100 for enterprises that will be considering the Chinese models.

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.

Didn’t deepseek recently announce prices will go up significantly?

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 far from significant, it's partially doubled during peak hours. They could 16x it and it would still be two orders of magnitude better value than OAI's $200/mo plan.

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.

How does it compare to 5.6 Luna after the permanent 80% price cut?

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.

It's still laughable. They blew it. I'm not sure they could even pay me to use their models at this point (and I don't mean via employment, meta and its refugees are permabanned to me). My experience over the past few months with open models has me seriously entertaining moving east.

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.

Since you did not compare them, I now checked myself, and it looks like Luna benchmarks about the same as DeepSeek V4 Flash 0731.

The cost per task was $0.03 with DeepSeek, $0.05 with Luna. $1.23 for Sol.

Tokens per second 132, 202, 70 respectively.

> Didn’t deepseek recently announce prices will go up significantly?

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.

Deepseek is open weight/source ie it will be running on US servers in the US maybe by on each companies own servers.

I don’t think the industry knows how to price this stuff. Deepseek is great (I’m running it on a RTX 6000 pro setup) but it’s nothing like Fable. It’s still strongly human-in-the-loop which is fine, until you experience how good these models can be.

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.

If that’s the case businesses would be seeing millions to billions of profit gain (or cost reduction) in the past 4 months as they went from Opus 4.6 to Fable 5.

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.

I work for a FAANG, and have my own personal projects for which I use the Chinese models. and the big models do indeed save/make us a lot of money.

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.

> just a very last-gen way of using agents.

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 stuff is just obvious to anyone working with the latest models. Fully autonomous agents are a game changer. Having to pair program with one is indeed "last gen", I haven't done that for a month and I won't ever be doing that again in my life, outside of personal projects.

> This stuff is just obvious to anyone working with the latest models. Fully autonomous agents are a game changer.

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.

I use Kimi K3, Opus 5, and Fable on a daily basis.

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.

> This kind of takes makes me cringe. Why don't you go back to LinkedIn?

This is not appropriate for HN. Please review the guidelines: https://news.ycombinator.com/newsguidelines.html

It depends on the use case. And most companies (like 90%+) do not have the coffers FAANG has and price does make a big difference.

Unless you are very cash strapped, Fable is a very nominal fixed cost compared to the benefit of what it offers (fully autonomous agents, and no longer needing to pair program with one).

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.

> [...] probably until we have AGI that can replace employees, maybe even a bit after.

Is this what you are looking forward to?

>I don't know why this is so hard for HN to understand.

There are businesses other than FAANG. I don't know why this is so hard for FAANG employees to understand.

I use Chinese models, even smaller local ones, for much more than pair programming. If we are talking about deepseek v4 flash, which is basically a frontier model, it is much more capable than the local models I run on my MacBook Pro. The only issue really is finding the right harness.

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.

> 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.

If you don’t have a way to automatically check the results via redundancy, you need a really good model, since even 99.9% reliability is going to cause slot of headaches. If you do have a way to automatically check results, then you can use something that fits in your computer and was produced 3 years ago.

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.

> The Chinese models are not good enough for anything other than pair programming, which is just a very last-gen way of using agents.

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.

> This matches my experience with DeepSeek V4 Pro at Max reasoning,

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.

> GLM 5.3 bridges this gap.

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.

> 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.

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).

> It’s very clear that vast majority of the business do not generate additional value from incremental intelligence gain from these models.

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.

> but it’s nothing like Fable. It’s still strongly human-in-the-loop which is fine,

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…

> not your $100/month..

This is made brutally obvious by anthropics customer support for people with such accounts.

Yeah, fair. If we were talking $2,000/mo vs $200 then the maths starts looking very different.

The discussion started about cost-vs-usability, and then you brought in "but humans, the best LLMs around, cost much more" into this discussion to make it a not cost-vs-usability discussion. Do you not find it a bit disingenuous?

Not really, no. My point is that a $100/mo LLM subscription is generating $5000/mo in value, so even a negligible difference in performance wipes out the cost savings completely.

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.

> 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?

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).

SOTA models are exceedingly good at self-correction in the right harness especially in domains where things can be proven mathematically. Most people are just deploying Claude Code or Codex with default settings and rub the genie lamp expecting exceptional results. Garbage in, garbage out.

"isn't the old version good enough?"

You can say this about literally every product we buy. And yet...

>"You can say this about literally every product we buy..." - and this is exactly why I am not buying many new things just for the fuck of it

"It's only real AI if it comes from the silicon region of USA"

There are a lot of tasks that are hard for organisations to run consistently but require some intelligence - monitoring logs and metrics for anomalies and security events, backup audits, audit processes in general, ensuring document quality and consistency, database advice and tuning, customer experience management, process optimisation - that are not "long horizon" in the classical sense of each step depending on the last, but are the result of consistency and attention over a long period of time and a large amount of data.

For this genre of task execution can run with limited horizon and is independent but would be too expensive to do with "us frontier tokens", I think for these, there is value in availability of cheaper tokens.

These are not 24 hours of inference with floating point errors accumulating; largely the system guards against errors compounding. Tool failures, compile failures, test failures, etc, push back against the model taking a wrong turn and force it to correct.

Yes it's much easier to have a smarter model that goes straight to the correct answer first, but it may not be necessary or economical. There's a minimum bar for the model where it understands problems and knows the right step to correct them, and above that newer models give diminishing returns.

> it's much easier to have a smarter model that goes straight to the correct answer first

That's basically ASI not AGI, if you agree humans are NGI (natural general intelligence) and make mistakes and wrong decisions in solutions all the time. Right steps with some wrong ones is acceptable though for AGI.

> these compounding errors that accumulate in dumber models

While SOTAs handle these errors better, they compound in all models and there's a term for that. It starts with cluster and ends with an expletive.

I wish I could, but I don't see the need for human steering going away soon if the task involves anything novel (see Terry Tao's chat).

What you're overlooking is that any large task can, and should, be broken down into smaller individual components that can be reasoned about and tested in isolation. This is literally the whole basis for how we do programming. You don't need a model that can keep track on a gargantuan tasks all at once. You need a process for breaking problems down into manageable chunks, and then assembling them into a solution. This is a problem that can be solved by a harness through steering and and having a decent agentic loop.

Majority of white collar work absolutely does not require sota models

White collar work will require SOTA models up until the point where said models can automate white collar work entirely. Then, we might finally see a meaningful commoditization crunch.

I genuinely don't know any work that has been taken over completely by AI without humans in the loop managing things

At this point, I don't even know if its possible

I have a silly (but honest) question. What's an example or two of a > 24hr task that people are actually asking something to do? Like real life ones.

Decompiling / disassembling and annotating old software, making sure it can build cleanly back to the original binary, and then look for bugs or subtle issues.

Another one I did was a printer data stream translator from an obscure format to PostScript/PDF (or just PNGs), complete with cups support, etc so these old apps can easily be hooked up.

Flash is capable now of running long range defined-goal tasks like this.

Working through the total backlog of issues in a repo that may have accumulated from planning sessions

I wondered this too. Also, the duration of the task depends on the quality of the prompt and the model used. I have a feeling a lot of these day-long tasks are bogged down by suboptimal tool use and on-demand python slop.

Amazing how many inaccuracies you fit in there.

- You describe the breakdown in terms of time but it's more accurately a function of reasoning complexity.

- You seem to assume that no intermediate evaluation is possible.

- Often it is (e.g. the build breaks or tests start failing), allowing for course correction. There's definitely a cost to that but it can still be cost effective if the accuracy is "good enough" and the price difference significant.

- There are numerous tasks that don't require Fable or GPT5.6 level reasoning to improve efficiency by an order of magnitude.

> even small errors compound over time and can lead to catastrophic outcomes

So, death sentence even to frontier models?

It is true. I don't care about having infinite frontier-level intelligence, and I don't care if Fable can one-shot frobnicate a klaxelzorp with a benchmark performance of 97%. I doubt most people do, in fact. I just want something that meets the baseline level of intelligence needed to be a really, really good pair programming agent. It shouldn't have any silly dealbreaker issues involving laziness or hallucinations, it should be smart enough to bounce ideas off of, and it should automate doing tedious boilerplate. And - most of all - I want to be able to afford using it as much as I want. That's what has happened here.

I wonder when we crossed the "99 percentile of intelligence for 99% of the usecases" threshold. At this point, the gains seem to be right at the very edge of bleeding edge for narrow and specialized use cases, and wonder if it'll be a sort of diminishing return from here on.

In April

Probably the best counterexample is the games they are able to design. It's still mostly AI slop, few would want to play.

With those cheap models the idea is you're still in the loop anyway so the more expensive model is a waste of time and money. In this case, that means you're steering the game to look like you want not how AI wants.

Same with the expensive models. Fable can't one-shot a good new game. Game dev is still human-in-the-loop no matter what model you're using.

> narrow and specialized use cases

Such as Decision Making. /s

You just can't set a high enough threshold of intellectual effort for critical decisions.

It is amazing how fast it happened. Right now one of my main projects is fully running on DeepSeek flash. My reason was that I was blocked by both of the main US AI labs from working on it because it involves viruses. DeepSeek flash has been killing it since I switched it on, completing the first phase of the project and setting up an iteration in another application space. It isn't the most brilliant model, but it is reliable and I don't have to manage my weekly token allowance. I just spend freely and end up spending only a few dollars a day. Intelligence is going to become a basic commodity. Only special stuff is going to drive us to use special models. And maybe not even that.

Seconding this, flash and especially pro are damn near batting 1000 for me in my experiments. In one instance it figured out that the poc was running on a much slower machine and locked affinity to a single core.

Many devs who have never tried from either side build it all up in their head but it's almost always been a matter of thorough tedium, which LLMs are excellent at churning through, especially when there's api docs/headers/code comments.

If you have the space, try mirroring your port at the switch level and capturing every packet then making it go through them all to look for whatever. We have NSA at home lol

> If what you're saying is true and accurate, then US-based AI labs are in big trouble.

I've been working with DeepSeek V4 Flash 0731. I'd say that it's maybe not quite as smart as Opus 4.5, but it's willing to think things through carefully and keep going until it gets a good answer. So it's a decent Opus 4.5 replacement. Just let it cook.

It isn't Opus 5 or Fable 5. But it's nearly free on Open Router, and it's self hostable on a Mac Studio with plenty of RAM, or using an RTX Pro 6000 Blackwell or two. Which is chump change for any company that employs programmers.

It would absolutely have been a frontier model last December.

> only saving grace might be some sort of a 'national security' proclamation banning the use of state-of-the-art Chinese (and non-US) models

In what kind of sad and failed dystopia is this a "saving grace"? For whom?

> "saving grace"? For whom?

For Anthropic and OpenAI, presumably. And the rather large economic distortion field around them, that may or may not go very badly for all our retirement funds if those firms become insolvent...

> If what you're saying is true and accurate, then US-based AI labs are in big trouble.

Well... I would think that the whole AI industry in the US are working towards public bailouts... Which I guess they'll get under the current administration... So they'll be fine... Nobody there really seems interested in actually creating a profitable business anyway...

Laguna S 2.1 is American and seems to be on par (or better) than DeepSeek v4, is only 110B, and it's faster! Weird there's not much talk about it vs other models.

These companies know their value was never in the models. There’s a scramble to acquire as much hardware as possible, and to build as many services that people get soft locked into, such that they have a moat when the open weight models fully catch up. Doesn’t matter that the models are open weight, if you want access to the hardware you will have to pay.

I dont see how the outlook is any better for the open weight companies. They’re in the exact same situation as the closed weight companies except they have had much less revenue, and built up less of a brand, leading up the the point where they are equal in terms of model quality.

I've been using DeepSeek exclusively since I've been playing with LLMs, largely due to the price.

I joined a company that is an Anthropic shop and I am genuinely shocked.

Sonnet 5 is a little better on long horizon tasks and headless unsupervised agent workflows - but for in-IDE workflows, it's virtually unusable.

I am so used to flipping around my codebase at warp speed with DeepSeek flash. It's so fast and accurate I don't have time for parallel agents. It's a really rewarding workflow.

Moving to Sonnet, you ask is something simple like "split this into a seperate file" "implement this method" "this is my schema, implement a repository for it". It'll spend 30 minutes thinking and charge like $12. And no token caching, what are you even doing Anthropic?

It's unusable.

DeepSeek are in a league of their own

If programming in the US to become unconditionally 10x more expensive, then the exodus from the US is about to begin.

Could we look at manufacturing, for example the car industry, to predict what will happen?

I couldn't agree more, and think of all the wasted inference across accounts overpaying for their subscriptions.. Need a secondary marketplace for this stuff.

Only to to find out they end up in a much worse place

> Only to to find out they end up in a much worse place

Like Europe?

Like Shanghai

One random says they are using DeepSeek and you proclaim it's all over for the top AI labs. Brilliant.

First of all, no one knows the "true cost" of any of this, yet, but we know it's expensive. To what extent are the Chinese labs being subsidized? Are they real businesses?

Second, the Chinese labs aren't some "super geniuses", while the American labs are full of clowns. As of today, like the past 3 years, American labs are SOTA. That might change, but let's not act like the American labs don't know what they are doing.

The idea that people are going to use cheaper models for cheaper work isn't some novel revelation, it's completely obvious. People are doing it already, eschewing Fable.

Subsidy doesn't matter once the weights are released. You're conflating subsidized training and research with subsidized inference. DeepSeek's (allegedly government-) subsidized inference is coming to an end soon per their docs, but that's fine because the weights are open. You can run this on-prem with very modest hardware. And you can fine-tune it to your business with your data.

Then why are the major American labs in "deep trouble"? They can also continue to be subsidized and release the weights if they want to, if that's what you think it takes for survival. Seems like a low bar.

The point is it takes money to keep developing models. Everyone is playing by the same rules. At this point, the US labs are trying to build businesses. I'm not really sure what the Chinese labs goals are. But I do know they aren't doing charity work.

They are in trouble because they offer something 5% better for 2000% the price.

>"To what extent are the Chinese labs being subsidized? Are they real businesses?"

And who gives a flying fuck. I am a "real" business and I count my money. It is not my life goal to prop some fat cats crying crocodile tears. Granted I do not use Chinese models. I use Junie straight from my JetBrain's IDEs that in turn uses Gemini Flash. Very cheap and more than enough for my use.

> There's no way large companies outside the US will pay the "US AI lab" premium

this has already happened with manufacturing, so it isn't surprising that other industries follow.

The US premium in engineering and scientific endeavors have been lacking for the past 30-40 years, and if it werent for tech and silicon valley, the US would have nothing state of the art. Even on that front, the US is falling behind given how much effort in tech has been diverted into privacy invading, and advertising.

The US has been riding momentum, but eventually that momentum will stop. It will take half a century to get back up to speed, and by then, the US will have fallen behind so far that catching back up will seem impossible.

The telling evidence would be if china has the first moonbase before the US does. I think this is highly likely looking at today's US administration.

It's not necessarily true. OP has not provided actual objective figures. The nuance is in tokens spent per successful completion. So sure, you can blast DeepSeek for 1 hour solving a hard task, or Opus-5 for 5 minutes on the same task. Opus would ultimately be cheaper because time IS money and solving things faster is ultimately cheaper. Sure, benchmarks will show cost to be lower but people who test all of these models in real world use cases know the benchmarks are not reflective of real world use cases and Chinese models choke on problems Fable or gpt-sol will breeze through.

Sample of 1…

I downgraded my Claude subscription and delegated my Claude Opus access to serve the role of an Architect to brainstorm and plan every step of development.

I leave the development to Deepseek.

Claude gets to review at many layers. It is often just as good as if I let Opus develop it by itself(the architect session will find similar number/level of gaps).

Deepseek flash as an architect and problem solver is not as thorough as Opus5 + high. Codex sol+ high is even better than Opus 5 at this moment for my needs.

It's been true for almost every business. "Cheap and good enough" usually trumps "excellent but expensive". Ikea, McDonald's, Ryanair, AliExpress, Aldi - these brands prove that catering to poor people is more profitable than catering to rich people simply because there are so many poor people that their collective spending power outweights the one of rich people.

>Aldi - these brands prove that catering to poor people is more profitable than catering to rich people

At least here in Germany Aldi isn't even really limited to the poor, it's famously a place where you can run into anyone. Where I used to live in Berlin close to the government district I literally on occasion ran into the chancellor (and her bodyguards). Aspirational shopping where you buy premium goods to pretend to have higher social status honestly seems a bit on its way out. Even middle class people seem to consciously shop more utilitarian now.

> RyanAir

https://en.wikipedia.org/wiki/Category:Defunct_low-cost_airl...

https://en.wikipedia.org/wiki/List_of_defunct_airlines_of_th...

Seems more like a overall industry problem, not limited to low cost carriers

Well, not universally. It’s a tradeoff. If what you said was universally true Apple wouldn’t exist; Spirit Airlines wouldn’t be bankrupt, etc.

Apple sells to half the American population. And by definition many of them are poor.

Spirit was broken by oil prices which everyone pays the same for. (There is no cheaper jet fuel alternative).

Not a good comparison to the point of wrong conclusions.

Apple doesn’t target the low end (they are a luxury handset maker, their low end is the mid-end at best). The fact that people buy their products anyways shows that cost isn’t the only factor in business success.

Spirit was broken by oil prices because they target the low end customer with their ticket prices. Oil prices went up and they had no pricing headroom to charge more on tickets so they simply went kaput. Ryanair also suffered a fair bit. Other airlines did (comparatively) fine because they had the ability to increase prices since their customers are less price sensitive.

It’s a classical business lesson that being a “cost-sensitive” vs a “value-sensitive” business (what this tradeoff is called) is a tradeoff. It’s notably recommended that startups don’t target the lower end in prices since you can’t compete on cost with a business that has more economy of scale than you; you have to compete on features. And being a cost-sensitive business means that you are affected much more than other businesses by changes in material/component prices, because a 13-cent increase in the cost of a component matters more the more product you sell, and if you increase prices too much customers will start to wonder if the “budget” brand is really a good value proposition over the mid-end or high-end ones anymore.

Ah yes, Amazon, notable for targeting premium end to get started.

Literally everything you're saying doesn't line up with reality. Apple's most popular product today are two lower end (neo and mini).

You make the mistake of thinking your theory defines reality, when in fact reality says quite the opposite here.

I'm saying this because your logic is the classic logic that everyting thinks makes sense until you do it. Everyone can't target the high end, there are limited customers with many choices, making it far harder actually to win.

You target the demand/pain/need regardless of market.

Yeah this is what I’m curious about. How good are they after the benchmarks. I’ve been told yeah they’re good but they’re just building to show off for benchmarks.

The ByteDance folks are apparently training a mythos level model 10T params apparently. If they do would it still be subsidized at these cheap rates?

The bet isn't that people will be able to automatically reply on bugs and rack up API charges.

The bet is on using AI to gain competitive advantage. You don't win the stock market or make the deadliest drone by switching to the cheap model

> You don't win the stock market or make the deadliest drone by switching to the cheap model

Really? How many times a small team has outperformed a much bigger one just because they were "doing it right"?

I have been in software companies where most software produced was bad. Not just the code, the overall design everywhere. So... bad engineers with the most expensive model, or great engineers with cheaper models?

There are more than two options. What about great engineers with great models?

Again, I was answering to:

> You don't win the stock market or make the deadliest drone by switching to the cheap model

The question is not "can you win with the best model?", it is "can you not win without the best model?".

It is true.

I have been using it since it got released and its as good for scoped coding tasks, as the other big models I use, but just soo much cheaper.

As a business though I can spend 100k and run this model and support about 10 fte- not bad…

> If what you're saying is true and accurate, then US-based AI labs are in big trouble.

As it is now clear by behaviour of companies and US govt, all these investments will be backstopped by US govt. No US AI company will go hungry, they are national champions.

How is $5/day irrelevant? In the $150/mo range you can get effectively unlimited usage of GPT 5.6 Sol (Pro plan). Why use a much weaker model for the same price?

> In the $150/mo range you can get effectively unlimited usage of GPT 5.6 Sol

With 5 active sessions going nonstop? That seems like a pretty important qualifier.

Others have said similar but I disagree, I'm spending $200/m and I can easily burn through my weekly quota with a few overnight goals using 5.6 medium.

Same here with Claude Opus on 20x Max plan. Easy to burn through with 3-5 parallel sessions, each with their own subs going.

Then, once I go over, API pricing racks up FAST!

I burned through $100 of credits in literally 40 minutes doing the same long running task I always do.

And what do you do with all that?

I've reverse engineered multiple classic games and turned them into popular, browser-based MMO-like experiences.

I'm also creating a free platform that replaces extremely out-of-date software, some of it only available with mutli-million dollar contracts, to help medical physics professionals with cutting-edge radiotherapy devices used to treat cancer.

https://brynnbateman.com/ for a list of projects

Nice! I tried to play https://eternalsagas.com/play but it stalls at 45% loading with the progress bar always at 0 but the music playing.

And out of curiosity, how do you automate testing the porting in the browser that's actually playable etc? And aren't you a bit scared of hosting and serving the "hairy bits" such as full assets? Nice job anyway!

Yeah that's my weakest browser game since I've actually transitioned to using a desktop Godot client I haven't released yet. It will load eventually it just takes forever bc the assets are quite large, which is part of the reason I've had to move to Godot. That project doesn't exactly have an active user base which is why it's lower on priority list to fix.

I automate testing playable parts in browser by adding a dev mode that allows text commands for everything instead of having to rely on clicking UI or 3D elements. It can see the full game state in JSON and interact in any way via commands.

And re: IP - I just accept that I might get a C&D any day and have to take it all down. I'm careful to not accept a single penny for any reason and don't even have Patreon. Usually monetizing is what makes IP owners unhappy. And for the Pokemon MMO I just don't advertise it anywhere meaningful since they'll C&D the second they see it. Largely made it for my nephew and we play it together.

Time to make creative software for linux that can replace adobe for Video/photo editing :P

GIMP isn't bad these days, I use it constantly

An MMO of Full Throttle would be amazing!

It'd be interesting to see what someone could do to turn a 90s era adventure game into an MMO! I never played the game myself but I think basically any game dev project that modernizes stuff is really neat.

5.6 medium is pretty good at implementing moderately complicated things as long as you've done a good job specking out the types and the API contracts and acceptance criteria.

I can pretty easily burn through my weekly quota over several agent coding hours with minimal supervision when tasked with some pretty large but well-planned refactors.

not at all true. if you're truly using it across the board for smaller things (translation of pages, filtering of every individual tweet based on its relevance to you etc), the costs ramp up super quickly.

i used for work where i did less and it quickly reaches thousands if you're not careful. i can already see what some will say: skill issue et cetera - whatever.

$100-200/mo is the subscription price. You aren’t going to go over. And you can select smaller models as well. Not everything has to be done by the most expensive one.

You just get throttled, which interrupts your whole workflow.

>> you can get effectively unlimited usage of GPT 5.6 Sol (Pro plan). Why use a much weaker model for the same price?

> Not everything has to be done by the most expensive one.

Ok then.

I thought it went without saying that GPT 5.6 Sol is the wrong model to use for things like filtering tweets. Apparently not?

You would think that no one would be stupid enough to use Fable or Sol for small one-off tasks like filtering tweets, but AI has opened up a lot of avenues for stupid people to ship code. It's only going to get worse.

yeah man it's me who's stupid and not you when i use an example of the author who claims that you can use pro subscription on all of the mundane tasks w/o going over the budget. sure. my point is that you can only use it with cheaper models. also some tweets are fairly dense in their compression/context, so yeah sometimes it's necessary.

If you factor in cost then it may well be, but it's definitely the case that the high-end models can get you significantly better results than the cheaper models even for tasks that feel like they should be straightforward.

I really doubt that the either of the pro plans are subsidized heavily enough to support you swapping v4Flash for Terra, much less Sol.

$5/days is ~330 Mtok/day, that’s a nontrivial amount of work, and none of the gpts are more efficient than deepseek at $/task if deepseek meets your quality bar.

They do give a significant amount more than you would expect from the API pricing. The US provider API pricing has heavy margins by all accounts (and most are short enough of GPUs that there's little incentive to drop).

5 USD is at the "raw" API price.

OpenCode currently offers 60 USD API credits at 10 USD per month (OpenCode Go) and have even doubled it temporarily as a promotion.

Effectively you can get Deepseek for 1/12th the already ridiculous cheap API price.

From here, it looks like opencode is hemorrhaging money. I've got a Opencode Zen free account, and I've been using deepseek-v4-flash-free on Pi for a bit, and I haven't hit a limit yet. Sometimes my request fails, but retrys work. I know this is a very cheap model, but it's being given out for free. I assume they might be training on outputs?

Those are not at the same price. Opecode’s 60 USD of deepseek usage is charged at much higher rates than what deepseek themselves charge at.

> Opecode’s 60 USD of deepseek usage is charged at much higher rates than what deepseek themselves charge at

Per the open code zen pricing page[1], it appears that the token prices are the same, but their cache is 10x more expensive?

[1]: https://opencode.ai/docs/zen/#pricing

Flash Opencode 0.14/0.28/0.028

Deepseek 0.14/0.28/0.0028

Pro Opencode 1.74/3.48/0.145

Deepseek 0.44/0.87/0.0036

For flash the input/output is the same, but the cache difference is big, you're paying 10x on >95% of your tokens.

For Pro, it's even worse, input/output is 4x and cache is 40x. The price different is really brutal. Yes you will still come out ahead by spending your first 10$/month on opencode go, but you will be saving a lot less than initially appears from their (60 USD for 10 USD pitch).

[0]: Deepseek: https://api-docs.deepseek.com/quick_start/pricing/ [1]: Opencode: https://opencode.ai/docs/zen/#pricing

> In the $150/mo range you can get effectively unlimited usage of GPT 5.6 Sol (Pro plan)

Not true. Sol on XHigh or Max runs out even on the $200/mo plan. It's not close to effectively unlimited. Maybe at 2x the current allowance it can.

What are you doing where you're getting Sol on XHigh or Max to run out on the $200 plan? Last night, I had 48% of my weekly limit left, so I spun up 4 projects I had been working on and ran them all on Ultra with Fast mode and /goal, and it took over 4 hours to burn through that. I mean, I was reeeeally trying to use it up because I just can't use it up with normal usage for weekend and after-work programming. I've had Max crunching on something this morning for 3 hours now and I've only used 4% of my weekly limit ...

> What are you doing where you're getting Sol on XHigh or Max to run out on the $200 plan?

Real work. $200 looks good on the outside until the essence of it, e.g. the models lie. I gave a list of spec to Sol and Sol decided some items didn't need to be done and the reason was "unproven", "not enough evidence", etc.

They all come up with amazing ways to lie (or be lazy). Often times what you get isn't what you asked for (only on the surface). E.g. I ran it to iteratively bench and optimize a better data structure for the project. It spent hours and finally came up with something. When I check it out -- it benchmarked the wrong criteria and was way off. So here we go again. Most AI work looks good on the surface. There are infinite edge cases.

So to do real work and gate it you need to:

1. Plan

2. Get it to do the work

3. Get independent agents to check from different angles

4. Take that feedback and get it to fix those gaps

5. Match against the plan and redo parts if needed

Every task is easily 4-5x the estimated amount of tokens.

p.s. well I did burn some banked resets building a compiler for some language AND it is still NOT done. Every time it says done I say check it says ok we still have bugs...

The cost per token is super low. If you're used to paying OpenAI or Anthropic API-based fees then the same workload on DeepSeek feels free.

Ignoring the other side of the equation is a pretty wild thing for you to do here:

> I'm running it in Oh My Pi with a second instance running as "advisor" and even with 5-6 active sessions (effectively 12 streams)

$5 a day is pretty extreme in DeepSeek. You really have to abuse it to get anywhere close to it. Maybe something in around hundreds of millions of tokens per day, considering cache hits and all.

And to be frank, it is not that much weaker for regular software development work. I use Claude at work and I see no difference in capability. I only notice a dramatic difference in how much more expensive it is.

But DeepSeek now has a warning they’re going to sharply increase their API pricing sometime in the future.

Dax (from Opencode) has tweeted that they can replicate or beat the price with rented GPUs. Deepseeks secret sauce is the incredibly cheap caching (magnitude cheaper than other providers).

vLLM has recently released a similar approach. It's not as effective as what DeepSeek does but still an interesting development.

I have no doubt that in due time other providers will match or perhaps even beat the current DeepSeek prices.

As someone who recently tried it on some blackwell cards, it's possible to match the prices especially the input can be even cheaper and output can match the costs so you can easily build a net 20-30% margin business even at current GPU prices.

The entire issue is caching, I tried to write some custom to dump to disk kv-caching using some ideas from their papers and my experience with snapshots and vm checkpoint systems, I must say they must have really squeezed that lemon it's hard.

Atleast me with Sol couldn't figure it out over a couple days, a few hours each day, which isn't much but I did feel a bit stuck with existing solutions and felt like I might have to write something from scratch. But if you are willing to put in the effort into the infra I do think it's doable. But it will be really hard to pull it off.

My congrats to anyone who manages to pull it off, they might be able to kill off most AI labs. Assuming they can find the compute, Deepseek really has killed all models for me other than Sol/Fable/Opus/K3 tier stuff.

Mild info dump, since this has a few too many upvotes and some folks might be misunderstanding, 20-30% is assuming a typical agentic workload where input tokens dominate by over 20:1 or at least 10:1, if you are output token heavy then this is going to be a different ball game.

And there is no way in hell anyone can afford caching prices same as what DeepSeek is offering, and DeepSeek keeps the cache available for an insane amount of time most providers will flush it in 5-mins like Claude/Anthropic (some offer customizing it but I am not sure of the pricing, it's load based on some like Fireworks, which means assume a couple minutes at most, they say several minutes god knows what that really means).

There is no way to match DeepSeek's current prices, "profitably" if you are renting a GPU and reselling tokens, unless you have some really amazing caching infra or something.

Deepseek's prices are just insanely cheap, I am not saying it's impossible to get there the overall performance suggests it should be feasible, but I will be damned if any provider could match their tps and caching any time soon at those same prices profitably.

I believe even if Deepseek 2-3x their prices across the board even then they would be cheaper for most long running tasks, that's just how good their caching is.

For one I have managed to hit the cache after over 24 hours on their system it's insane, I honestly didn't care because it was so cheap but it truly made me incredibly happy to think about the engineering that must have taken. TTFT is slightly worse, but it's good enough, for those cache prices I can take a few seconds worth of hit on TTFT.

From what I understand about deepseek’s pricing, they are only charging what they need to break even.

Thanks this is a comment with a great amount of useful detail.

One read is 1) they're getting a lot of traffic for Flash, 2) they've said they're updating Pro soon and expect that to lead to a traffic spike for Pro, but 3) that would leave them overloaded, so 4) they're going to raise prices to avoid it.

It's interesting that most open models adding 1M context did it in a way that reduces KV cache size (though DeepSeek was the most aggressive, using compressed attention on all layers), but only a couple providers turned it into a discount on cache reads.

> Deepseeks secret sauce is the incredibly cheap caching (magnitude cheaper than other providers).

Can anyone working at one of the main US labs (Google, OpenAI, Anthropic) comment on WTF they haven't even tried MLA - despite the obvious massive advantages?

I know enough to know they aren't completely incompetent. So there must be a quite good reason.

But it remains a mystery to me.

DeepSeek's MLA is like almost 2 years old at this time. They've got thousands of people working on this stuff. They clearly have the ability to at least try it...

They already are?

There’s a measurable performance tradeoff versus gqa so there’s reluctance.

For the most part though the new deepseek v4 tech is hca and mhc and people are still catching on like with moe and rl. Wait for 6 12 months, minimum time for next pre train.

Are they not?

The big US labs are opaque and don't publish much of any technical details anymore. We don't know what they are or aren't doing, honestly.

> they can replicate or beat the price with rented GPUs

They "can" is the caveat here. Rented GPUs are going up in pricing. I recently got an email that DigitalOcean pricing of GPUs were going up.

1. They have to get a hold of them (availability is bad)

2. They have to maintain the pricing

Eh, what are you guys even talking about? Deepseek is not cheapest provider as is, and it's MIT. So deepseek making it more expensive to use is just nonsense, they can only change their own pricing. It's the beauty of MIT license and open weights. If anything, these models are some of the safest in the world to use if you worry about a rug pull.

There's more to inference than just the input/output token cost. Caching has a massive impact.

Deepseek charges $0.0028 per cache read on Openrouter. The next cheapest is $0.018.

That's a massive difference and quickly adds up on coding sessions (which often hit 95%+ cached tokens).

90%+ cache hit rate is common, and so you'll see on places like openrouter that Deepseek cache cost is indeed a magnitude cheaper than the rest.

My usage thus far from api.deepseek.com

  - input_cache_hit_tokens: 1,265,646,976 x 0.0000000028 = $3.5438115328
  - input_cache_miss_tokens: 18,208,088 x 0.00000014 = $2.54913232
  - output_tokens: 9,615,178 x 0.00000028 = $2.69224984
  - request_count: 10,837 (no price)

Total cost: $8.7851936928 (approximately $8.79)

Cache:

  - Hit: 1,265,646,976
  - Miss: 18,208,088
  - Total input tokens: 1,283,855,064

Hit rate: 98.582% (1,265,646,976 / 1,283,855,064)

So your cost would be $40.68 with another provider that has one less zero in the cache hit price.

Cached input tokens are what drives most costs.

any link to this caching tech?

[Feat][Core] Add disk offloading support to SimpleCPUOffloadConnector — #49644 https://github.com/vllm-project/vllm/pull/49644

This adds disk as a tier in the HBM → CPU → Disk KV cache hierarchy.

There's also a cluster of related KV-offload FS PRs: #49225 (read/write batching, still open) and #49152 (batch store/load in C, merged Jul 28).

It's hard to say if these are similar to the approach DeepSeek takes but they definitely seem very interesting.

I'll believe it when I see it. Their prices are still much higher than deepseek, especially the caching.

Yes, there is warning, but also there are many providers on OpenRouter[0], hosting open weight model with similar pricing. The question is Will they go up as well?

[0] https://openrouter.ai/deepseek/deepseek-v4-flash-0731#provid...

DeepSeek has far cheaper cache pricing. That's the difference.

But the model is open weight?

I assume/hope this is about prices going up for the next release of Pro

even if they double it it`s from such a low base it is still supercheap

Source?

If you're on the DeepSeek Platform, you'd see this:

"We plan to raise the overall pricing for DeepSeek API services in the near future, with a significant increase expected. Please plan your usage accordingly. The specific pricing plan will be subject to official notice."

https://www.reddit.com/r/DeepSeek/comments/1vgpysh/deepseek_...

I made a HN submission about it yesterday with a copy of the email: https://news.ycombinator.com/item?id=49197176

I kept running into it looping two nights ago or else getting trapped in a reasoning loop it couldn’t escape from. Switching to Pro helped, but I ultimately had to use GLM-5.2 to recover my session. (GPT-5.6-Sol’s cybersecurity guardrails went off since the problem I was trying to fix involved a race condition where it would segfault and the words “stack frame” in my session made it decide I was being naughty. Yet another reason not to use American models…)

Another time, Flash started trying to make tool calls by just calling bash and catting the tool call to stdout. Then it started running echo xx for every two letter UNIX command it could think of: mv, cp, etc and the it dug into uv, ty, and jj

What was your prompt? If something benign, then it sounds like your harness has an issue with its tool calling that the agent is atrying to work around.

It was simply repeating the same text across 50-100 words over and over. MiMo UltraSpeed periodically does this, but seeing Flash do it was a new surprise. (The agent eventually detects the loop and kills it.)

> it's good enough to use it for (almost) everything

which in your case is?

I've posted a few times about my project that's a collection of 30k-250k webapps that are served from a WebDAV server. The apps know how to write updated copies of themselves back to the server.

My family uses it. I have gallery apps (yearbooks for each year are a lot of fun!) of us on trips and just living, an outlining app that's a mesh of Workflowy and Org Mode (it's called Fluxtral), a markdown-backed app (it uses marked.min.js, and is called Dextral) that offers documents, logs, calendars, and kanban boards, all parsed from markdown. I have a List app for gear, trips, shopping, etc. that we all can contribute to. There are utilities (world clock, calendar) and games (an oracle for RPGs, a KenKen implementation), and apps (a diagram editor that exports to SVG, a web-launcher that uses pneumonics, a Scheme-based hacking environment, and a spreadsheet that does most of what you'd expect aside from Solver and Pivot tables).

I started these projects before AI, and made slow progress over the years, but the modern versions of all this stuff have been built with Deepseek V4 Flash. I've also used Gemini in the very early days, and Kimi K2.6 later on, but these days, since I can now host Deepseek v4 Flash 0731 in a 2-bit quant on my Strix Halo box (128GB, but only about 250GB/s of memory bandwidth, so 15t/s), I used Deepseek with omp for almost everything. It's a very capable model, and I'm amazed I can run it locally and get good results. It's really revolutionary for my (small) use cases.

A collection of 30k-250k apps? Like individual unique apps?

Sorry, a collection of apps whose size is between 30kb and 250kb.

Haha, I misinterpreted as well. That would be a lot of apps!

I read that and thought "ah that's going to confuse people, but I can tell they mean 30k-250k loc", so thank you for the clarification.

Sounds fascinating! A blog write-up about your platform would be a fun read, if you're up to it

For sure! I'll be doing a Show HN at some point, just want to feel a bit more confident about certain aspects first.

Makes sense, I'll look out for it! although of course most Show HNs these days get lost in a deluge of submissions.. you might actually be better off omitting the Show HN when you submit it..

I too have Strix Halo. Two of them. The model have been awesome for me too.

With unsloth's Q3_S quant + kyuz0 'llama-vulkan-radv-performance' toolbox, I am getting 280+tps (batch and ubatch at 2048) for PP and 18+tps for TG. I really only need 256k context so it all fits.

If I go down to the Q3_XXS quant + dpsark + 'llama-vulkan-radv-performance', I can get about the same PP and 25+tps for TG with draft set to 2 or 3. Fits about the same as above.

Edit: I did notice the 25+tps quickly degrades down to 20+ after the first few hundred tokens.

>even if it's not SOTA

And, probably 99.99% of people using LLM probably don't even need SOTA anyway.

At least on these benchmarks, it seems to be pretty handily scoring up with the SOTA from 6 months ago?

Hear hear. IQ tokens to cheap to meter upon us. So many things changed since last week. Now I've had Prime agent session grinding into its 20-th hour still not giving up. Been using opencode-go since Go sub appeared. What made a difference was deepseek-v4-flash and mimo-v2.5 showing. Very similar middling models ~300b so light on the gpu. 1M context and hybrid archs - so one can actually make use of that 1M (don't grind to a halt like others). In OMP I have one the primary (default), the other one as /advisor looking over the shoulder and nagging. On opencode-go in credits counting they are the bottom-2 in cost, cheaper by 200-350 times than than the top-1. Last week with deepseek-v4-flash-0731 another jump - now it's closer to the top models then to the middle. Now I don't even need the /advisor probably. Still left it there it's sometime amusing the models back and forth. :-) DeepSeek offer /v1/responses api now with flash-0731, so setup Codex to use that too. I'm loving this :-)

I would not recommend DSV4F (even 0731 edition) without an advisor. On its own it’s an absolute drunk intern in my experience, but with an advisor model watching like a hawk when it gets stuck in loops or goes down boneheaded rabbit holes, it’s fine (and very cheap). I’ve been using GLM-5.2 as my /advisor but might try just a second DSV4F instance.

Hard to make big predictions, but it sure looks like at least this level of capability is going to be available in the open and relatively cheap to run.

The 'floor' has gone up: today's model a bit behind SOTA is like model releases that were blowing people's minds a few months ago. Compared to, say, DS R1, this is far out stuff.

This, Luna, and (if it's good in practice) Laguna S are also fast and light not just cheap. And, as happened before, DeepSeek's first but other open model makers likely follow.

And a small, fast model taking small steps is...fun? More like working with code.

It's very impressive and I'm running it locally on 2x DGX. Non thinking mode is very responsive. Thinking mode has some latency but can be switched on when needed. Both are really good

Agreed. With less than $10 on the DeepSeek API used, I’m somewhere near half a billion tokens over the past week or however long it’s been since it came out.

I’ve found it to be very capable. I’m using it with pi as well and some custom extensions I’ve put together over the past few months and it’s pretty crazy having it do what I need it to a vast majority of the time, do it fast, and see that it’s used like $0.12.

> I'm thinking about having it automatically filter and re-rank my social media feeds so I can steer the algorithm instead of the other way around.

I hadn't really thought about this but AI may well be the technology that disrupts and ultimately destroys social media.

The value proposition of something like FB or IG is, as we know, the network effect. The platform gets to extract value from user generated content. I believe that users should own the platform, a bit like the Wikimedia Foundation, because they're the ones that create value. Federation is a popular belief on HN and I've come to believe that's simply the wrong solution to the right problem.

Anyway, how these social media companies make money is by optimizing the feed for engagement. People know it too so you see people trying to build an audience by rage baiting. And then more time spent equals more advertising revenue.

But what happens when the AI can simply slurp all the posts and then filter and rank them? It destroys the engagement and advertising model. And I'm not opposed to that, honestly. It may be on eof the few good thing sto come out of AI.

> It would impress me if someone could burn that amount with "normal" usage. Even when running multiple sessions.

What's normal usage? I mean, Kimi is already really keen to spin of lots of subagents, and DeepSeep can probably do the same?

Yes, though I did that even with Claude (on my employer's token budget). The agents are great at doing the gruntwork of chasing down the reproduction of flaky tests, too. They need some hand holding at first, but the guidelines are usually re-usable per project. (Claude specifically needs to be told to really concentrate on reproduction, and not eagerly start fixing the flake: if you don't have a reliable reproduction, you have no clue whether your fix actually fixes anything.)

> The beauty of intelligence at this cost (even if it's not SOTA) is that it opens a whole bunch of new use cases. Test failure in CI? Have the bot automatically propose a fix, its cheap enough that you can discard it w/h issues. Test coverage too low? Auto generate tests on CI for every pull-requests! Monitoring server logs, continuous security audits and investigating every received exception now becomes possible.

I don't think this is the win you think it is. It's amazing that this is possible, but it introduces so much human overhead that you can drown in reviews and it can effectively slow you down more than a quick check and fix yourself.

The models need to get a lot more consistent in what they can and can't do before you can automate this stuff and only check the things you know the model isn't good at

I try to use all the intelligence I can, which fable, opus and then second tier models.

I am not sure why you wouldn't want to use the SOTA models unless speed is a concern. Otherwise you are leaving quality on the table.

Are you using it via the official DeepSeek API, or via a different model provider? If the former, it's worth noting that their cache read prices are one tenth that of every other provider ($0.0028/M vs $0.028/M), so folks who want to use a sovereign inference provider with a zero data retention policy likely won't see anywhere close to the same value.

Worth mentioning also that DeepSeek is the only provider in OpenRouter that was disabled-by-default until I enabled a setting: Allow paid endpoints that train on request data.

And software keeps getting worst.

The analogy I like is that building software is running a Michelin restaurant. The moment you scale, the chef is just writing cooking books and is absent, and you move into franchising, you will be amazed at the bottom line revenue scaling, while customers will be progressively appalled with the food...

Not that I disagree, but the average software before AI was more like a McDonald's. I have genuinely seen companies who were writing code a lot worse than what AIs produce nowadays. Doesn't necessarily mean that their software is better now, but my point is that before AI, I don't think that software compared to Michelin chefs.

Could you go into how you run two instances that speak to each other in an implementer / advisor role in parallel? I’ve been looking for this sort of orchestrator / worker solution where there’s constant feedback and nudging between the two.

Sure! It's https://omp.sh/ with the advisor mode enabled.

You can probably implement something similar as a plugin for your preferred harness. From a technical perspective I think it just sends the output w/h the thinking and tool trace to another model and asks it to double check everything (exact prompt must be somewhere in the OMP repo).

Thanks I’ll give that a go.

Would you run a less costly model as the supervisor given it’s consuming a lot of text and may have a simpler task to do like “make sure the implementing model doesn’t start over-engineering things”?

Does auto generating tests even do anything helpful? Don’t they just sort of tautologically say the code does what it does at best or do something completely ridiculous like test and implementation that only exists in the test file at worst?

We have an extensive description of _how_ tests should be written and they're reviewed by a human. All the AI does is fill in the boring middle part.

You should try with reasonix. It is designed to maximize cache hits. It seems less featurefull in its current state than open code and omp but I am getting a 99% cache hit with reasonix making my costs insanely cheap.

> Auto generate tests on CI for every pull-requests!

this seems like such a bad idea

Depends on the prompt I think. If it's just "Generate tests plz" then I agree, but if its

"If this PR adds any new endpoints, ensure that there are functional and integration tests. If there are not, please investigate the feasibility and appropriateness, and create functional tests using the guide found on our wiki for guidance https://www.ourdevwiki.site/how-to-make-functional-tests" then maybe it could add some value.

But that very much depends on the specific system. Some tests are obvious, some not so much.

Running DeepSeek with Pi as well, any plugins you recommend running it with ( e.g. native browser for snapshots, etc. )

What's the best harness to use with it?

I've enjoyed using https://omp.sh/

Seconded. I love OpenCode and Pi, but omp is my daily driver.

What's good about it? I use OpenCode and it does what I need, basically.

Spawning subagents via orchestrate and using /advisor are both super valuable. Haven't really unlocked the full capability of omp yet, but hoping to over time.

omp - current top, after using codex claude opencode pi that I still use too

The pricing was awesome, but deepseek just sent out emails warning of a large price increase.

my experience is the same, but deepseek is planning on increasing prices soon, which will make it a lot less attractive

it is funny when people say i am struggling to spend money.

>Test coverage too low? Auto generate tests on CI for every pull-requests!

Terrible use-case.

Terrible comment.

These posts have to be Chinese bots, these models are all trash. Used it via OpenCode for an hour, cost me one hour of my life. It is for anything complete trash.

I've gotten a lot of good work done with deepseek models. Like with any generic harness there's some tuning that has to happen. I used open code for a while, but I've landed on pi.dev as my go to since its easier to tune and has better deepseek integration. iirc open code is quite bad at utilizing cache and doesn't have a lot of ways to specifically tune the harness for a particular model.

I've found it to be pretty good so far.

(1) you used opencode (2) what provider did you use. openrouter is trash because they shit up the model serving. no max effort and horrific cache utilization, on the order of 50-75%, absolutely garbage. beware

What should we be running deepseek on besides opencode? I chose it because I heard good things. Also provider is directly through deepseek credits.

I use the Deepseek API and pay peanuts. Very satisfied.

oh you used opencode go?

harness: omp.sh

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