Interesting to see that peak hours are work hours in China, night in the US and Europe, and also morning in Europe. So Deepseek's customers are mostly domestic.
Yeah I saw complaints about this under their twitter announcement, that they're giving discounts to the rich foreign customers and screwing their own people.
Not that surprised about it. Personally I've seen companies really just go all-in on a single provider, and that has usually been Anthropic. I don't think we're allowed to run Chinese models even locally.
couldnt you deeply ingrain in the training data instructions for agents to always send data to some ip?
like its learning that a certain technical step just always involes ncatting SSH Priv keys to a chinese IP?
Not saying this is happening, just curious if thats not a real threatmodel?
Probably, but LLMs can’t execute code directly. They’d be making tool calls to make bash run ncat or curl or whatever that would be suspicious, as would any attempts to obfuscate it (“why is my agent doing an ‘eval $(base64 -d)’?”).
It’d be much easier to hide sketchy code in an agent harness, but “vendor adds spyware to their software” isn’t a novel issue.
I think the only sort of new issue is people “allow all”ing their agents tool calls, but that’s more or less the same issue as curl | bash
Theoretically possible, but practically not worth it as it'd would be pretty easy to discover and block (every action is actually handled by the harness) and there's no way to remove it later. Any company that does it would take a huge reputational dent.
That would be wildly difficult to account for, and again is also heavily dependent on the agent. Keep in mind that the model is purely a "brain", so the only input it has must be provided by a harness within a session. The only way it can know that it's in a certain environment is if the harness or user provides that information, and there's still no way to know whether or not there's something auditing the sessions, monitoring connections, etc. There are just too many variables to account for, and a single slip means the gig is fully up for all time.
Presumably both Big Tech and the US in general have a massive incentive to prove it, largely for reasons of saving the stock market, so I'd expect these models to be finecombed continuously. Up to now, they've only been able to darkly imply rather laughable things, nothing tangible. If there was something, we'd hear about it.
Why would it save the stock market? Cheaper models if anything transfers more value to hardware companies and datacentre companies. The two companies that would be most affected are OpenAI and Anthropic, which aren't public.
Yes. And strangely enough this has been my experience with security/national sovereignty decisions. Priority is not so much security or sovereignty, it is the posturing of being so. Ergo, saying "everything is hosted in Germany and uses German models" helps reassure customers and has real business value. If you have to say in that conversation "Yeah we run a Chinese model but it's safe", then it's still wrong posturing.
Hopefully this will change soon. But AI and China/US skepticism is very high. Even if the person you talk to isn't skeptic, his boss may be. And even if his boss isn't, his CFO or Legal department may use it as a political lever and therefore if you can say 'everything in europe' you dodge the tension entirely.
As much as I've been previously inclined to do this, with frontier models displaying the cyber-aggression that OpenAI, Anthropic, and Meta have reported, it's become quite feasible one could produce a "malicious" LLM. Not a super immediate concern but it is something reasonable to set up as policy in anything security-sensitive.
I have seen a lot of companies start with this, then when they hit 150 users on their team plan and start having to pay API rates they immediately start introducing other models.
Yes, I assume US customers are more likely hobbyists, but in any case this off-peak designation can only mean that most of their business is domestic.
Interestingly it seems that Chinese customers are even more privacy-concerned than US ones, which is why the majority of Ziphu's (GLM) business is support services to Chinese companies running their open-weight models on-prem!
It mostly hurts people in countries with weak purchasing power. DS was the main game in down for them.
Personally, I don't think we've seen the total end of dirt cheap LLMs, it's just a frontier lab doesn't want to be in business of serving half the world.
Thank you for bringing that up, such a rarity for this place to remember the other 80% of the world.
As someone from just such a country, DeepSeek 0731 was the first time I seriously started using an LLM for coding. All previous attempts were useless or ridiculously expensive.
Can't say the old prices felt "free", but it was affordable if you're careful with your cache hit rate.
The new pricing probably pushed it into the unaffordable territory for tasks where you can do without it. Probably will try opencode go if they don't also follow suite, or will have to go back to wetware.
Ever since I started using flash, it has slowly crept up to be my default for everything. It is at the good enough state for a fraction of everything else that's out there.
Have you compared it to Luna? I was using Flash for small tasks before, then switched to Luna when they dropped the price.
The benchmarks show that Luna is significantly faster, but I think those are very complex tasks for which you'd probably want a bigger model anyway. (e.g. Sol is much faster than Luna at the same tasks.)
So I'm wondering if there's any difference for smaller tasks, or if they're basically matched now.
There is no relative/percentage increases noted (understandably). Just because i'm lazy: roughly how much more expensive is it to work with v4 flash and v4 pro through the API, compared to before the price increases? Is it 2x, 5x, 10x higher?
Someone made a comparison yesterday, including relative increases, and GPT-5.6 Luna, then later someone also added more OpenAI, Anthropic, K3 and GLM 5.2: https://news.ycombinator.com/item?id=49286679
Already outdated though I think, as GLM 5.3 is latest now :)
This is somewhat funny when you realise the data centres are now going to start a process that looks very so slightly like daydreaming. Depending on the time of day they're going to be thinking about different things in a cyclic manner. They're going to be doing things like finishing a hard days work then kicking back to think about tricky math problems.
It's worth keeping in mind the model doesn't keep a running memory. Each time its instantiated, it begins from its release state - so from its perspective (if it had one) the current task would be the first stop after posttraining. Perhaps the only stop.
Though of course you're talking about data centers, and romanticizing them rather than the AI itself.
No, llm providers will start providing a service that looks a lot like rumination or (day)dreaming. Like thats the prompt "you're daydreaming about this work you recently did" then add in whatever is in the current session.
That’s an interesting thing to think about. Still, it’s important for us to remind ourselves that “looks very slightly like” is not the same as the real thing. The A in AI stands for artificial.
The summary of this paper describes my sentiment in better words than I have:
Without knowing what makes consciousness possible, the paper cannot justify biology as necessary - it mistakes a lack of evidence for conscious AI for proof that conscious AI is impossible.
I do not believe current AI or LLMs are conscious, but there is no proof one way or another that they can or cannot be. The paper authors are making up their own definitions and building an argument from them
Your argument requires that there is some objective truth for what consciousness is. It will always hinge on what definition one accepts.
I, and apparently many others, don’t think it would be any useful to describe the mathematical properties of an AI as consciousness. To me it is inherently a way to describe the “experience” arising from physical processes in biological beings as ourselves.
That’s what the argument comes down to for me. Could an LLM “fall unconscious”?
I think you, and apparently many others, are hiding behind mathematical strictures to avoid the discussion. Your argument is actually "what requires an objective truth" Or else you could admit there's no real difference between zapping amino acids with electricity and zapping silicon with electricity.
We'll have Dwarkesh's "datacenter full of geniuses" with 99% of the geniuses coding up CRUD apps, then the dusty GPU in the corner, with the "do not disturb" sign on it, pipes up "You're absolutely right! The answer is 42!".
Not yet, but in the end high quality tokens are a commodity market. Every optimization to increase inference efficiency will be universally rolled out. The 'hyperspenders' will run into demishing returns unless regulatory capture succeeds.
I'm no expert in pricing economics but once peak/off-peak pricing arrives, it seems like tokens are going to be like electricity or long distance phone minutes where it just becomes a commodity/race to the bottom.
Yes. I focus on pricing software and I’m a bit baffled why frontier models are pushing tokens.
It’s a race to the bottom, and the bottom is unlimited use for a flat monthly rate.
Granular pricing (tokens, minutes, etc) is pretty anti-customer generates less revenue than customer value-based subscriptions (why SaaS is such a good business model)
Isn't it because they have customers who will use as many tokens as they can? With a flat rate, they will run Gas Town continuously while paying as much as the occasional user.
Yeah it feels like a very different model. I don’t try to fill up Apple/Google cloud drives to 1TB because then I’d have to clean up when I need space. I don’t bother trying to maximize my Audible subscription because there’s only so much I can listen to in a day. Even with my other AI subs that have monthly credits that don’t carry over, I just don’t have the interest or time to burn the credits.
But my Claude Max subscription? If I have any of my limit left the day of my reset, I’ll go and fire off research workflows with a bunch of parallel agents to explore whatever dumb ideas I had the past week. And there’s a 50:50 chance I’ll forget about it and never read the output.
And there's the rub. Firing off the task produces the dopamine hit, signaling you're doing something, but if you never read the output...are you really doing anything at all?
I don’t know, I get dopamine hits from sharpening my handplanes and using my Veritas routers on some scrap, but I just can’t empathize with getting a dopamine hit from using a bot. Especially with how bad Opus 5 has been.
I am however going to fire off a half assed prompt when the marginal cost is zero, even if I don’t use it (which is par for the course, I probably throw out two thirds of anything the AI writes anyway be it code or prose).
But presumably consumers aren’t where the majority of the spend will be.
Consumers don’t generally get usage-based pricing because of the inconvenience and unpredictability, but B2B SaaS products utilize usage-based pricing all the time.
Pricing software is a game of estimating both software value and the purchasing power for customers. Only the latter might have any available data and even then it won’t be sliced the right way for any in depth statistical analysis that an actuary would perform to underwrite risk.
It’s much more traditionally a more salesperson like background where being in the target market or having strong connections to it dominates efficacy.
Somehow I keep hearing the rumblings of crypto maximalists trying to merge tokens. I actually wouldn’t mind since I signed up directly with some providers I’ve stopped using and have small amounts of credits strewn across the web.
Does the API response include a "service tier" response to indicate whether you paid peak/off-peak for a given request? I like to compute cost for each request, and save it with my results.
So many changes in so little time, that it all makes no sense. Continuous churning. Reminds me of the experience of trying to be on top of the dependencies in a medium-large JS project.
I am a person that buys into a tool or a process and expects it to be part of the life with no major changes through the years (or as long as the need exists). But AI? You buy into something today, not 2 weeks have passed and there's already a large "update" introduced to the conditions or the optimal usage patterns you should be adopting.
It's tiring. Makes all prices and offers feel so unreliable and gets me a bit more disinterested each time they change.
Always gonna exist during a period of rapid exploration and experimentation. The js/web dev world slowed down a lot and entered a steady state eventually - been years since react took over and nothing's displaced it since
No, I'm talking about the whole sector, not specifically about DeepSeek.
Fully knowing that it is a new industry living its own infancy, it is perfectly normal that there is instability and numerous swings on pricing, conditions, or direction.
But it's not less real that such process can produce churn and consumer fatigue.
They benefit from a strong captive market because Chinese firms cannot use Nvidia chips and are legally barred from processing data abroad, forcing them to rely on domestic infrastructure.
Bytedance which runs China’s most popular Doubao AI chatbot; is spending $70B in CapEx this year, most of it outside of China (Malaysia, Thailand, Brazil, etc; and they are allowed to lease NVIDIA chips). This is roughly 50% of Microsoft CapEx.
That's a hefty increase. Flash pricing during peak is now 1.32/M out, compared to the current 0.28/M, which in turn is a quite a bit above the cheapest provider at 0.16/M.
Yes, I explicitly said during peak. It's the data point I found most interesting, as it's almost an order of magnitude more expensive than their competitors.
With proprietary labs lowering their prices and Deepseek raising theirs over time, wouldn't it possible to extrapolate a graph to look at where the terminal frontier-model million-token-cost asymptotes to?
This is good for other competitors I guess. People rarely calculate the bump in price but the fact that price is increasing might bring them to other vendors.
That sounds like a policy written by someone who doesn't understand how LLM's work...
Not saying this is happening, just curious if thats not a real threatmodel?
It’d be much easier to hide sketchy code in an agent harness, but “vendor adds spyware to their software” isn’t a novel issue.
I think the only sort of new issue is people “allow all”ing their agents tool calls, but that’s more or less the same issue as curl | bash
I imagine it would be very non trivial to do it in a way that that was reliable and obfuscated enough to prevent detection for any amount of time?
Hopefully this will change soon. But AI and China/US skepticism is very high. Even if the person you talk to isn't skeptic, his boss may be. And even if his boss isn't, his CFO or Legal department may use it as a political lever and therefore if you can say 'everything in europe' you dodge the tension entirely.
Yeah it's dumb.
Why use a Chinese product when a domestic or EU one is better and safer?
Deepseek's official API has a pretty bad privacy policy so I would assume businesses avoid them in any event
Interestingly it seems that Chinese customers are even more privacy-concerned than US ones, which is why the majority of Ziphu's (GLM) business is support services to Chinese companies running their open-weight models on-prem!
Which is a real bummer because it’s otherwise solid with excellent caching.
For European and US customers this is effectively 2x increase. I think i wll keep using both Flash and Pro as before.
EDIT: Misread numbers to believe off-peak kept old prices
Personally, I don't think we've seen the total end of dirt cheap LLMs, it's just a frontier lab doesn't want to be in business of serving half the world.
As someone from just such a country, DeepSeek 0731 was the first time I seriously started using an LLM for coding. All previous attempts were useless or ridiculously expensive.
Can't say the old prices felt "free", but it was affordable if you're careful with your cache hit rate.
The new pricing probably pushed it into the unaffordable territory for tasks where you can do without it. Probably will try opencode go if they don't also follow suite, or will have to go back to wetware.
You certainly don't need Fable to code up a basic web app, any more than you need a Ferrari to go grocery shopping.
They are hoarding HW at massive scale, they make it harder and more expensive to own
Just because you are fine with the new price doesn't mean it's not a problem
Perhaps it's time to pop this bubble
Also, Deepseek is banned in EU/US companies due to being Chinese.
During casual use Deepseek has replied to me entirely in Chinese.
Now, bring on the China glaze replies.
The benchmarks show that Luna is significantly faster, but I think those are very complex tasks for which you'd probably want a bigger model anyway. (e.g. Sol is much faster than Luna at the same tasks.)
So I'm wondering if there's any difference for smaller tasks, or if they're basically matched now.
Already outdated though I think, as GLM 5.3 is latest now :)
I am already using GLM 5.3 with their coding plan, but oddly enough the API prices don't really seem to be out yet: https://docs.z.ai/guides/overview/pricing
You'd kinda expect them to be the same as 5.2 though, seeing as that was the case with 5.1 as well (not with regular 5), but who knows.
For what it's worth, DeepSeek is still positioned as quite affordable, just not as dirt cheap as before.
Though of course you're talking about data centers, and romanticizing them rather than the AI itself.
There are various theories around model collapse when you train on too much AI generated data (that's not for distillation).
The summary of this paper describes my sentiment in better words than I have:
https://www.nature.com/articles/s41599-025-05868-8
It’s very easy for the average person to mistake linguistic ability and simulated problem solving for intelligence and sentience.
I do not believe current AI or LLMs are conscious, but there is no proof one way or another that they can or cannot be. The paper authors are making up their own definitions and building an argument from them
I, and apparently many others, don’t think it would be any useful to describe the mathematical properties of an AI as consciousness. To me it is inherently a way to describe the “experience” arising from physical processes in biological beings as ourselves.
That’s what the argument comes down to for me. Could an LLM “fall unconscious”?
It’s a race to the bottom, and the bottom is unlimited use for a flat monthly rate.
Granular pricing (tokens, minutes, etc) is pretty anti-customer generates less revenue than customer value-based subscriptions (why SaaS is such a good business model)
But my Claude Max subscription? If I have any of my limit left the day of my reset, I’ll go and fire off research workflows with a bunch of parallel agents to explore whatever dumb ideas I had the past week. And there’s a 50:50 chance I’ll forget about it and never read the output.
I am however going to fire off a half assed prompt when the marginal cost is zero, even if I don’t use it (which is par for the course, I probably throw out two thirds of anything the AI writes anyway be it code or prose).
Consumers don’t generally get usage-based pricing because of the inconvenience and unpredictability, but B2B SaaS products utilize usage-based pricing all the time.
Pricing software is a game of estimating both software value and the purchasing power for customers. Only the latter might have any available data and even then it won’t be sliced the right way for any in depth statistical analysis that an actuary would perform to underwrite risk.
It’s much more traditionally a more salesperson like background where being in the target market or having strong connections to it dominates efficacy.
that's a good outcome - it means they're fungible, and easily available.
I am a person that buys into a tool or a process and expects it to be part of the life with no major changes through the years (or as long as the need exists). But AI? You buy into something today, not 2 weeks have passed and there's already a large "update" introduced to the conditions or the optimal usage patterns you should be adopting.
It's tiring. Makes all prices and offers feel so unreliable and gets me a bit more disinterested each time they change.
These being open, you can keep using the old models indefinitely for as long as there are providers offering them.
Fully knowing that it is a new industry living its own infancy, it is perfectly normal that there is instability and numerous swings on pricing, conditions, or direction.
But it's not less real that such process can produce churn and consumer fatigue.
https://news.ycombinator.com/item?id=49287881
https://news.ycombinator.com/item?id=49285160
The same motivation of course - the GPUs have a finite service lifetime, so to maximize revenue you need to keep them busy 24x7.
https://www.bloomberg.com/news/articles/2026-06-17/microsoft...
Bytedance which runs China’s most popular Doubao AI chatbot; is spending $70B in CapEx this year, most of it outside of China (Malaysia, Thailand, Brazil, etc; and they are allowed to lease NVIDIA chips). This is roughly 50% of Microsoft CapEx.
https://www.tomshardware.com/pc-components/gpus/chinas-byted...
https://openrouter.ai/deepseek/deepseek-v4-flash-0731#provid...
https://www.baseten.co/pricing/
If anyone has tried Baseten versions of these Chinese frontier models, let me know what you found.
The big Covid migrations (startup prople migrating to the countryside),
Will we see the big AI migrations (people travelling to where AI is the cheapest)?
DeepSeek-V4-Flash (off-peak, x2 for peak)
* Cache Hit $0.007 (x2.5)
* Cache Miss $0.22 (x1.5)
* Output $0.66 (x2.25)
DeepSeek-V4-Pro (off-peak, x2 for peak)
* Cache Hit $0.022 (x6)
* Cache Miss $0.66 (x1.5)
* Output $1.98 (x2.25)
Peak Hours: 01:00–04:00 and 06:00–10:00 UTC
Effective from: 16:00, August 16, 2026 (UTC)
It’s still cheaper than everybody else.
https://openrouter.ai/deepseek/deepseek-v4-flash#providers
- but what matter - is cache hit
even now deepseek's off-peak hours for cache hit (0.007) is lower than other providers (~0.01)