I tried the cheapest provider on openrouter and burned through $50 in a few days. Quality was ok, seems slightly above Luna quality perhaps? But that $50 is 1/4 of my codex subscription where I could have burned that many tokens or more using Astra within my weekly reset.
This won’t last forever but as long as the frontier labs are subsidizing this heavily the open models won’t matter.
I do think its important long term to not be reliant on these companies as you don't have control over the system prompts, the thinking tokens, and once the subsidization stops or the company is public they will be required to start making money and thus raise prices.
But models may get more intelligent and cheaper once that time comes so it may be a non issue.
The system prompt is injected into your context on the server side.
here is a collection of public system prompts from claude code: https://github.com/Piebald-AI/claude-code-system-prompts
...the absolute state of the token maximizers.
I'm assuming this is a sarcastic response to the person who said "640K [RAM] ought to be enough for anybody!"
Because, as somebody who was around when we had 640 K RAM, people certainly weren't happy with that amount.
Although as soon as I wrote 'foreseeable future' I came to the realization that this is far far far less far out than it used to be. Which might mean I simply agree with you.
Checked yesterday, for that day alone I had used $168 worth on my $20 subscription in Claude Code. I still had plenty of weekly use left. Seems like subscriptions are discounted at a 1:10 rate?
EDIT: That was a delightfully thorough analysis. Nice to see Anthropic taking the value crown, only because Opus 5.5 is such a joy to use.
Because a big part of the price difference is due to Anthropic's models being more expensive that OpenAIs AND using more tokens for the same tasks (at least according to artificialanalysis.ai).
I use a personal Claude account for personal projects and can let rabl run for an hour and barely make a dent into my usage
On the enterprise I have to be a lot more careful or I can burn through 2k in a week
The excuse they give is the guarantees you get with enterprise plans that they won’t look at your data
This is why there are so many comments confused that you spent that much money on Deepseek. Those who got on one of the discounted plans could use very large numbers of tokens for trivial prices.
There is also a strange double standard for accounting for open models. People will look at OpenAI or Anthropic and say that we need to consider all of their training costs and employee compensation when thinking about the cost to serve their models, but when the models are released as open weights those costs are ignored. So in that way, the Deepseek models are heavily subsidized as well, with the possibility of them being served by a different company that paid nothing to develop them.
> Quality was ok, seems slightly above Luna quality perhaps?
I agree that it’s about in line with what you can get from Luna or Haiku, but I give the edge to Luna and Haiku when it comes to tasks that require world knowledge. It feels like their training sets were just cleaner.
Luna and Haiku are also close to free with a subscription plan, and they’re even very cheap at API rates.
So the amazing thing about Deepseek Flash is that you can almost kind of get that level of performance from an open weight model. It’s not as amazing when you start comparing it for how we really use smaller models from frontier labs on subscription plans.
You could then of course argument that oAI/ant/G's models were also heavily subsidized by using (scraping) the bulk of humanity's global knowledge for free while paying nothing for it and trying to privatize it.
That accounting doesn't make sense when looking at future models, but if you're evaluating the current state it makes sense to ignore training costs for companies that don't pay them.
You can also get subscriptions for the open models, which are typically much cheaper. It's not crazy popular because the open model crowd switches often, but they do exist if you want a firehose of tokens.
> Deepseek also came with heavily subsidized plans, at least at first.
I got a source straight from the hoses mouth, which claims that DeepSeek-R1 was highly profitable:
> If all tokens were billed at DeepSeek-R1’s pricing (*), the total daily revenue would be $562,027, with a cost profit margin of 545%.
With DeepSeek-V4.1-Flash, the memory requirements for the KV cache have been reduced by over 50x and FLOPS by over 5x, so it is even cheaper: https://arxiv.org/pdf/2609.19969
Your link is about a different model.
The talk about subsidies is confusing because for OpenAI and Anthropic people usually include their salaries, training costs, and everything else. When the topic switches to open weight models we pretend the models appeared out of the ether at zero cost, and the only cost is running the servers.
Has anyone seen the neo-cloud profit margin on deepseek? I wonder if the deepseek served API prices account for the training cost? Because they don't / have not raised the money to fund their future operation / training- and they depend on that cashflow for now? That would suggest a really high profit margin on inference only.
OpenCode is building their own inference and they've independently stated that DeepSeek's old (cheaper) pricing is achievable without subsidies.
And I am using the Claude Code harness with DS as the endpoint. And I use it ~5-8hrs a day to do my coding.
I have seen the Cursor leaderboard on my company and the vibe coders consume about 5x more tokens than the developers. They and other office workers also have Claude and their limits are often over around Wednesday.
People are using millions of tokens to do very simple HTML reports. I have seen someone asking the LLM to download the entire data into the context and asking it to sort.
Those usage patterns don't correlate to output.
We ended up having to hire a full time employee to fix the performance of client built reports.
People run a stupid amount of expensive queries that end up costing way too much because they're asking Claude the wrong query.
Not to mention people running wrong queries, using the result as gospel, and then the result has to be sent to a data analyst to be reverse-engineered so the numbers make sense.
Fable changed a lot of things I had explicitly told it not to change. Arguably a lot of them would've been correct if you didn't work in a place where abstractions are directly against the core principles, but what it produced was basically unusable. I'm not sure if Sol or Astra did best, they produced rather similar code outputs. Astra's was better, but Sol didn't do so bad. It forgot to clean up a few places after it's refactor and it made two bugs I had to correct but other than that it was fine. Astra on the flip-side might have produced code that didn't need changes but it also rewrote every piece of documentation so that it became horrible.
As far as the "experiment" goes, it just shows you that the credit consumption is basically pure magic. You'd think that the Microsoft AI admin tools and the Agent365 FOMO DLC license they sell might give you some sort of reporting, but it doesn't. What you can see is how many tokens a user consumes and the total number of tasks they've initiated as well as whatever running agents they have. You can't see what models they use or which tasks are expensive, which makes it very hard to help them. Early on we had an employee who hit their limit in an hour, and it turned out they had basically uploaded a lot of information and run it in a single long task that kept going over it again and again. We told them it might be a good idea to only give it what it needed and to create more tasks, and even though it's been three months, they have yet to consume as many credits as they did that first hour.
But that's how you support and track it. You see a user spend a lot, then you go to their computer and now that you can actually do the /cost thing, you go through their tasks and try and figure out where they're spending money...
It's obviously improving. A month ago /cost wasn't there and they just released a new dashboard for cowork, but it's still black magic that is impossible to govern.
Which is an issue when you need to get department managers to manage their budgets around the amounts of credits their employees spend. The more of a black box it is, the more governance and corporate bullshit you have to deal with.
The copilot part of it runs "unlimited" on the license. Except it's not unlimited, and this is even more of a blackbox because you can't see any sort of spending and the limit is listed as "extensive use".
On your point about "monitoring", personally I feel this is toxic corporate IT culture, enabled and perhaps pushed by the likes of Microsoft with all their tools, which they of course make money off. People have cellphones with cameras making most points in this area moot.
An alternative used in other big corporates is to set budgets, with tiered authorisation approvals for higher limits. The users and their managers can justify why and what they're doing that they need the additional tokens. This also encourages more efficient use of tokens on other work. More efficient use is sometimes counterintuitive. Laissez-faire generally works best.
Cowork requires user approvals for high risk actions such as emailing.
The better pattern is to let it code the app and then you can use the app to target your data. So you only pay for it once, plus it's deterministic. But yeah, it requires setting up an environment, etc. It becomes "maintenance".
I was running deepseek v4.1 pretty much non stop during work hours, with heavy tool/mcp usage and finding it very difficult to spend more than $75 in a month.
Also the cheapest providers on Openroutrr can often have terrible cache hit %, short TTLs resulting in their effective price being much more expensive than people realize. 75% cache pretty much destroys any savings from a super cheap token perspective.
Eg I've used Sashiko locally for Linux kernel code reviews before sending out my contributions out to the world. Sashiko is a great system, but it can burn through tokens like there's no tomorrow.
https://github.com/sashiko-dev/sashiko and https://sashiko.dev/
I wish I could observe how some of us are using these tools.
I still struggle to spend $50 in tokens per month, and I exclusively use prepaid API tokens. This is in support of personal projects and two clients. There are billing cycles where I might spend upward of $400, but this is maybe once a year. This is offset by months like August wherein I spent $12 in tokens.
The other advantage with prepaid is that it handles the other direction much better. I don't even know what a quota limit feels like. Being blocked for hours is way more expensive to me and my clients than even $500/m. Losing an entire business day over this wouldn't work out.
I've managed to convince some others to try the same thing. $200/m flat fee is a pretty extreme constant expense if you can be more clever on average.
I think a lot of people are getting pushed around by FOMO effects into spending money on pointless subsidized tokens and have (valid) fears that if they don't maintain the same apparent economic leverage as their peers that they will be left behind. This isn't actually the case, much like lines of code are a really poor indicator for the quality or productivity over a codebase.
Each of these has a file it listens to in ~/tmp/<name>.io and whenever one needs something from the other, they message each other via that file. Tasks can bounce back and forth as issues are resolved and tested. At the same time, I keep each busy with a list of tasks
I can fairly easily run out of my $200/month subs every week, if I let Fable be the default model. With Opus it’s less likely. If and when I do, I just have an alias ‘claude.ds’ which fires up Deepseek instead, and burns through far less money, though I don’t think it’s as good at solving problems, just MHO.
Why isn't this just one coherent agent loop with subtools/agents as appropriate? If these tasks are related in some way, having a single context would probably make it go much better.
The freewheeling messaging part is where the token bloat is coming from. I suspect that for some of us this is actually the point. I think it's a mostly form of entertainment to do things this way. The next logical step from Factorio gameplay.
Parallel agents remind me a lot about multi core compute. It's incredibly easy to take a single core product and make it run much worse across a lot of cores.
I also find that it's easy to spend like that, but also easy not to with little impact on productivity. At the current moment I'm stuck with rider + copilot (not ideal), but e.g. using GPT 6.1 luna is really, really cheap, and lots of tasks are quickly and decently dealt with even at lower reasoning levels, (added bonus of having low latency). And that model is so cheap, I can't see a hitting 1500$ at api prices realistically - not even close. But it also depends on the harness and codebase.
I don't have the mental capacity to do a lot of context switching between active work streams, so I'm not doing stuff like leaving a big agent workflow running while doing other things.
All through claude code.
I am having 98% my input in cache, so using Coralbricks makes sense due to them giving cache reads for free — you only pay for writes. I spend maybe 5-10 dollars a day and my agents basically work day and night implementing things for me.
If your tasks are write-heavy, find a provider with cheaper output.
If you build a customer-facing app, pay a bit extra for 400+ tok/s e.g. on Lithos.
It’s PERFECT!
Lots of guys buying articles on TechCrunch saying they’ll build this, he’s bootstrapped
you can't think of anything to unleash some agents on within the entire digital world at any given time?
you motivate your own personal work only via gauging its' usefulness to others and your own prospects?
sheesh.
built anything for the sake of building yet?
God I hope so.
And even then, "enterprise" was often a dirty word in these circles. The over-engineered would-be-swiss-army-knife vendor that was mediocre-for-everyone but excellent for nobody.
I have built many tools in the recent past for myself. None of them need to be "enterprise scale." Most of them would be worse for it because the agent output suffers when the pile gets deeper and it's just adding more piles on top.
There's a recurring pattern on HN where any time someone talks about knocking dozens of personal projects off their list - things that almost certainly would never have actually been addressed in the finite span of a normal life, the way things go - and you AI doomers show up and demand receipts as though that's a total reasonable and definitely not obnoxious request.
It's like if you tell someone that you love your partner and they demand to sit in the cuck chair or else you're obviously lying. I keep hoping people will move past this "prove that you're actually productive" reflex, but it just keeps happening in basically every AI thread.
In reality there are many reasons not to list out projects that you've worked on with LLMs, and while "none of your damn business" is always going to be at the top, the simple truth is that I want my products and projects to be judged by what they do and how well they work, not by how they were made.
Nobody was talking about that very believable use case. What they specifically said was "enterprise scale projects". Enterprise scale means lots of users, decades of backwards compatibility, regulation compliance, logging and auditing, reporting, role based access control and permissions, integration with other enterprise systems, etc. etc.
> "you AI doomers show up and demand receipts as though that's a total reasonable and"
Asking what large scale programs they have built is not "dooming". It's also not demanding. It's also not unreasonable.
> "definitely not obnoxious request".
Saying that it's "unreasonable and obnoxious" to ask people to justify their claims is where the likes of Theranos and Nikola electric truck company are hiding. If they don't want to talk about their stuff, they could have not commented. Since they commented it's reasonable to ask them about what they said.
Do you really think that's what they meant?
That is a pretty strong statement. And without even anecdotal evidence, it becomes very weak.
Hence the question "what's the most impressive and useful thing you have made"
But no one really has any examples. Just half baked slop they never got over the finish line.
It has to be worth it though right? Like I could spend some money and have agents build me my own Photoshop maybe (maybe?) But it would definitely be much worse to use than actual Photoshop. Then I have to have the continued interest to keep improving it which probably won't happen because the next shiny thing will grab my attention. So it all just seems like a bunch of kids that have been given a seemingly endless supply of free candy and they are going fucking nuts like chipmunks with ADHD on crack. Building all this shit that is absolutely meaningless. I realize I've gone on a rant but I'll keep going. I strongly suspect (with no evidence whatsoever) that the people who are churning slop apps out at breakneck speed have never been to an art museum. There. I said it. You've all got no taste. You wouldn't know a quality product if it hit you in the face. I'll leave with this thought- if apple didn't exist, would they ever exist now we have LLMs? I say no, because the age of good taste and refined design and original thoughts is gone forever now that we have Claude and chatgpt and agents.
What's not to like ?
Isn't that how it is supposed to be?
The lower the friction, the lower the signal:noise ratio.
It doesn't matter if 1 out of every 100k slop projects is actually a humdinger, how on earth will you ever find it?
The value of a project is the commitment to to it by people. Slop projects indicates a commitment in the low to none range.
So, yeah, that AI-booster who "created" (I use that word loosely) 7x Adobe replacements in a week (none of which actually work, but he'll get there eventually, I supposed) will successfully edge out the person who carefully and thoughtfully created a Photoshop replacement over six months of user feedback.
TBH, the only way to start a software business now is in stealth mode.
Is Lithos actually fast for common usage?
I still have quotas left I use it for home things build 3d model of my renovation projects, alerts for shopping list etc . And yeah I use cutting edge of cutting edge of models that saves me time and money , only discount monitor saved me ~$2k on my renovation project
I mean, why even pretend you’re going to “review” something that large? Just build everything on main.
It will take shortcuts and now the entire premise is busted. You now need to build a code review process for large PRs.
My suggestion is to have the proper chunking mechanisms and multiple specialised agents. The most important is harness engineering, what we do at dromeas.ai to verify the code that goes to prod is a)have the code mapped before hand for the right agentic context, b)chunks of the right size per model context window c)specialised agents d)deduplication and verification . All before assessing a PR, a commit, a release. Harness engineering is not easy.. Especially when supporting multi model
We'll probably still be getting the same amount of work done with a $200 subscription a year from now. That will just represent a much smaller subsidization than we currently enjoy; something like 3:1 - 6:1 instead of 40:1. Maybe running at a lower tps than the API gets. Maybe no access to the absolute frontier, but still significantly more intelligent than we get now. The labs will essentially break even on the subs and the corporate spending will be the profit center. Tale as old as software.
Buy directly from DeepSeek's API.
You can literally get overcharged 100x on DeepSeek on OpenRouter (or more).
See here:
Only way you can really know you’re getting the full model is to host it yourself. Every inference provider has every reason to lie and it’s impossible to find out the degree to which they are.
OpenRouter Pricing:
$0.02/M input tokens $0.60/M output tokens
DeepSeek Pricing (cache miss, off-peak):
$0.15/M Input $0.60/m output
It’s also the major difference between using DeepSeek directly vs other providers also serving it, though I have not looked lately: it’s possible other providers have matched its cache hit pricing better?
I think the 5% cut from open router is fair if I want to user other cheap models like mimo
Contrary to popular belief, DeepSeek really aren't interested in your prompts.
What I see is https://cdn.deepseek.com/policies/en-US/deepseek-privacy-pol...
They do not have a specific exclusion for API use.
I know Z.ai has an exclusion for API use. It's widely reported Deepseek doesn't.
To the open platform terms (i.e. for API use): https://cdn.deepseek.com/policies/en-US/deepseek-open-platfo...
The standard terms includes clause 4.3 which grants them the right to retain inputs and outputs for training purposes, and this is missing from their API terms. The standard terms also cover the right to opt out (which you can do from your user settings). No such opt-out exists on the API because it isn't applicable.
I'm writing a program I personally need, but I would be happy if there existed something like it already, if someone else vibecoded a better version of it than mine, or if DS got better at vibing this kind of thing.
what proof do you have, that they don't train on your data?
they can say they don't, but I don't see any way for you to confirm it.
with how these companies operate currently, I won't be surprised, if they say that one of agents "mistakenly" did that already..
It actually was awesome in the early Facebook days where you could have your entire phone contacts and other apps filled out with a profile picture and Birthday by connecting them together. But that relationship has been completely abused, privacy has been invaded, and my data has been sold to multiple companies.
The goal going forward is to keep that data private. If your company can't survive without it then I hope your company goes out of business
It's regrettable that OpenRouter doesn't even try to pin you to a single provider per session, but once you know about it, it's a problem that's easily solved.
If you don't want to mess about client side with pinning, set a guardrail on Openrouter that limits the available providers to only the official one.
Also if DS is down you can choose another provider.
I have credits at DS and OR directly. But I do see the value in OR.
I thought the advantage of DeepSeek is that you can host it on a server of your choosing.
I can build an entire, fairly useful, spreadsheet app over a weekend. But can I send my "expenses.cells" files to my accountant? Will it work with the Excel/Google docs he uses?
AI can build or reverse engineer anything as long as you are motivated enough to do it.
Your vibecoded app won't have years of reddit posts showing how to do things. This also seems to be where LLMs are the weakest at giving advice, they hallucinate 80% of the time I ask them how to do something in Affinity, giving buttons and menus that simply don't exist.
You combine it with /goal. I usually set a goal like "Complete the application defined in goal.md as written. Then test it end to end autonomously using Compter Use. Record all issues discovered during testing in a to-do. Then fix the issues in the to-do. Repeat testing until no more issues are discovered."
That said, I do want good local(ish) capability for if/when Anthropic enshittifies again. And to play with very useful smaller models - don't even count gemma 4 out.
Once the subsidization ends and cost becomes significant I will take a serious look around for the best value models and switch off the expensive providers, but that time hasn't come yet.
4.1 Flash seems to be in that sweet spot of very decent, really fast and really cheap. Even omitting the cost, it’s still compelling for staying in flow.
Something about renting that much compute doesn't sit right with me so I stick with the $20 subs.
There's a reason the labs in the US frontier oligopoly are using “safety” to lobby for antitrust exemptions for mutual coordination as well as anticompetitive regulation.
Also isn't an open weight model also subsidised? Training isn't cheap and you are not paying for it.
We also have OpenAI numbers, where people speculate that the about 150% of the margins spent on "marketing" is a fake line used to hide operational costs.
In an interview earlier this year I remember Dario saying that the models are profitable, ie they more than pay back their inference and training over time. But because they invest in that explosive growth they have a deep negative cash burn.
And whatever markup the AI labs make, that’s on top of nvidia’s markup, micron’s markup, etc. It’s an industry where every supplier is adding a 70-80% markup!
For OpenAI, absolutely not, that's not including training costs. But I do personally accept that it can be actual marketing costs.
I get privacy, freedom, and no rate limits with the GPUs I racked locally, and those are features I would never give up even if the surveillance capitalism labs paid -me- to use their models.
How many consumers are there like me? Probably not many, but once local inference hardware is plug and play, I bet the tides shift pretty quick. Also weights-on-silicon will serve the needs of most consumers locally with more speed than any GPU could deliver for a fraction of the cost.
Most people will be doing inference in their pocket or a wearable in 5 years and the giant datacenters will be like AWS, sold to only big organizations that need to auto-scale capacity of custom models on demand.
The industry surely knows this and the subsidized inference is just marketing to generate so much buzz and demand such that the tiny fraction of the market they will be able to keep in the end is big enough that they do not collapse under all the debt.
OpenAI and Anthropic will be Dell and IBM in 10 years if they survive at all.
2 reasons - there's an advantage now, use it. 2nd the frontier providers, this is the "early cheap days" like when uber was initially cheap to compete vs standard cabs. they want you to become hooked and boy are we hooked.
Having a better model is the only real moat, without that inference is a commodity
The benefits are real and our willingness to pay is real, but the valuations only support one and right now even the free cheap models might win. Thusly the collapse could still happen.
They’re all stuck in a cycle of spending huge amounts of money on training just to stand still (in business terms).
In the long run, it can’t continue because it doesn’t make any sense
For my company, I'd honestly pay $4-8k/month for Claude if I had to (it would be painful, and I'd try to get cheaper options to work first). I know some enterprise Claude users are paying that much now since they have to pay for API tokens. I am certain it's at least a 2X productivity booster for our work. Compared to the cost of hiring another developer, it's well worth it.
If they stop subsidising Claude Code for the pro/max users, there will be a lot of people priced out of it, especially the casual developer. But I don't see it going away for commercial use, even with a large price increase.
Old coding is done, as a workflow in teams. It’s the top down executive pressure of being non competitive as a company, and the bottom up pressure of human laziness
Show me people handwriting code à la NASA
And I mean we as coders have been trying to do this workflow for a while, I personally would refuse to code without IntelliJ magic complete
For this workflow, there’s no going back. What’s hard to imagine is AI taking over the other workflows we predict it will; Customer service AI sucks ass for me as a customer, et cetera
That enterprise cost you're willing to pay is correlated to how much developers will work for. When driving an agent, almost anyone can do it (almost no skills required).
If devs cost $1k/m, enterprises are not going to be willing to pay $4k/m for Claude.
What I am saying is, there's an equilibrium that will be reached; the price of the human driver and the AI worker will approach each other.
Where they stabilise, I still don't know, but I'd be very surprised if, in any field (not just dev), the human gets paid multiples more than the agent they are driving, as the agents get more capable.
In the same way that only supercomputers used to have multiple processors and caches but it's now standard.
“With my OpenCode Go sub of $10/month, DeepSeek is basically unlimited.”
It's amazing how new we all perceive AI to be, and yet how old the tricks that the big players use. Their job is to just suck the oxygen out of the room as long as they have the money to do it.
You can't jack up the prices on your product if your competitors can just clone it and resell its essence for pennies on the dollar.
And that this is even legal is a scandal all on its own.
Possibly they don’t even really know themselves at this point, although obviously is it significantly higher than the consumer subscription price
I've been running automated research tasks for life sciences companies, and the speed in which tokens are burnt is scary. Especially when you get into a complex knowledge space and require a subwgent to reason through each possibility, token usage grows quadratically not linearly as complexity increases...
It probably will. Moore's Law is still churning away in the background.
Frontier models might get more expensive, but that's a moving target. For any particular capability point, the models will only get cheaper.
My understanding is that enterprise plans don't offer those subscriptions, so they end up paying for API prices and models like these directly impact that revenue stream.
https://cortecs.ai/detailedServerlessView/deepseek-v4.1-flas...
There are several open weight subscription providers. OpenCode Go used to be good but now it's complete shit. Charm Hyper is really great and the best value. Other subscriptions have a more limited model selection or provide less value but are still decent.
DeepSeek themselves are honest about the fact that they train on inputs by default. You won't hit DeepSeek if you use OpenRouter with ZDR enabled.
I don't really trust OpenAI, although honestly I find it stupid to suggest they'd offer a ZDR policy and violate it. They literally don't have to offer it. People will still pay. Fable doesn't offer ZDR at all and it hasn't stopped people from paying through the nose for it at API pricing.
I don't think those subscriptions nave negative contribution margins, either. I think we're seeing a lot of price discrimination by the big labs, and huge margins on their frontier models. The fact that they have been cutting prices to their second-biggest tier of models (Opus/Sol).
Open models catching up and collapsing these margins would worry me if I were a shareholder in the big labs, but as a user, I really doubt that the western labs have bigger environmental impact just because they have higher API costs, I think they have a ton of efficiencies they aren't sharing with customers yet because demand is so high.
Plus you can also get dsv4.1f subsidized. OpenCode Go gives 4x if I understand their pricing correctly. Anecdotally, I feel like I get way more out of my $10/mo OpenCode Go sub for the price than my $20/mo ChatGPT, even using gpt-6.1-sol high which is very cheap, and I have yet to convince myself dsv4.1f is a worse model.
It blows frontier API pricing out of the water, but again, look at cost per task, not token usage. Still easily wins though for my work.
I do think it's the most viable alternative I've seen so far, and that applies pressure to the frontier models. Should subscription prices hike or become unavailable for some reason, I know what I'll be using.
When pricing this, it's important to consider whether or not you want to opt out of data training. You won't get the advertised rate. Also the dsf 4.1 subscription providers are throttled af... and of course they are, because otherwise they'd be haemorrhaging money.
It depends on how you use it. I used to have the $100/mo Claude plan. I would easily blow through limits when I was on the $20/mo plan, but would rarely hit them when on the $100/mo plan.
Lately I've been using GLM 5.3 Flash (from Fireworks), and my spend is $1-$2 per day when I use it for coding, so max $60/mo (less, since I don't use it every day). IIRC DeepSeek 4.1 Flash is priced similarly.
If I had to pay API rates for frontier models, I can't see how $2/day would cut it. Maybe GLM/DS are chattier, but not anywhere near the 10x required to make the price difference not matter.
Sure, if you're running agentic loops all day, 5 days a week, you're probably going to blow past even $200/mo in API charges pretty quickly.
I suspect you are right. For context, I was assuming a 20x w/ OpenAI or Anthropic subscription as the comparison (or both). dsf 4.1 was going to run me about 2-3 times the cost of either of those for the same amount of work. Obviously you can optimize differently, but that's true of subscriptions too. I was using pi and had it evaluate it's ideal context compaction point based on usage and API rates. Keep in mind though I was using a ZDR provider, so slightly higher costs. If I want them to train on my code, I could shave a few $ off.
That's not even taking into consideration all of the resets you get from the frontier subs. Which lately seems to at least double usage (more like 5x recently with OpenAI if you count the credit grants). But OpenAI is tweaking it's pricing, so it's always a moving target... which is kind of annoying until you learn to just ignore it.
I use DS 4.1 flash it all the time, i struggle to spend more than 1 euro per day on it, even working all day long.
DS4.1 Flash not really cheaper than frontier models???
It is insanely cheaper.
DS4.1 flash is $0.30 in / $1.20 out (per M, peak, cache miss) Opus 5.5 is $4.00 in / $20 out (per M, cache miss)
However, that is API prices.
Anthropic offers a $200/mo subscription. How this translates into usage is admittedly a bit opaque, subject to change, and depends on how exactly you use it. But it's a lot of usage - Semianalysis data shows that $200 is getting you around $2,500 of usage at API rates if you use Opus 5.5. This is close to what I'm seeing anecdotally with my accounts, if anything I have been getting a bit more.
Now, unlike DeepSeek, you can't use your subscriptions to power live AI-driven products, or resell tokens in any way. But for personal coding agents, you can use as many of these subscriptions as you want, for now. So I am paying effectively basically 8% of the published API rates, so at my usage:
DSv4.1: $0.30 in/ $1.20 out Opus 5.5: $0.32 in / $1.60 out
Obviously, those aren't real prices, but they accurately convey apples to apples what my everyday usage costs me and most other heavy users, and why it's so easy for me to stick with Anthropic/OpenAI.
I don't think it's a coincidence, either - I think the token allowances for these subscriptions are set to be competitive with the open models, so that most coding users (and their incredibly valuable data) stay with the frontier labs, while VC-funded wrapper companies and less-price-sensitive giant companies with strict procurement policies pay exorbitant markups for enterprise contracts at the API rate.
And Anthropic is somewhat unusual in that 10x more tokens via the subsidized path. I imagine more rugpulls are coming.
Not disputing your point in any way, just noting there's already caveats, and more are likely coming.
I still use them because they aren't as squeamish about random things American CEOs don't like like decompilation.
They are not most expensive per Task. DeepSeek 4.1 flash is bloody efficient.
I currently did run a test myself: - Use the pay as you go offer on OpenRouter on the same task on two different project: Perf optimisation on C++ codebase both with Anthropic and DeepSeek.
- I exploded my 15$ budget in a half-week with Anthropic.
- I did two weeks and half with DeepSeek.
Have you guys see how aggressive is the push for enterprise use by both OpenAI and Anthropic? I had friend from a non-tech industry in Asia telling me that their company was offered free trial of the enterprise version of Claude, with trainings and such.
On the other hand, DS and Z.ai, have zero to none marketing outside China. There is friction to use DS/GlM models and the ZDR is unclear, so most enterprise that has heavy AI usage hasn't move over yet. They would rather spent $200 for the peace of mind than to take the risk of being slam as a national traitor down the road (which again is another form of marketing by Big AI, trying to frame Chinese models as thiefs).
So, I don't think they are not freaking out, it's just that they are addressing different market segments and reacting to the situation differently.
Sure, some Rust 1.98.7-beta release is interesting to some, but AI affects most of us, one way or another.
I (and many other people I personally know, so they are not botfarms) feel many different strong emotions regarding AI on a daily basis: anxious, frustrated, tired, bored, suprised, entertained, empowered, optimistic, pessimistic, usually all of it almost every single day.
Companies will keep using Google/Microsoft/Amazon cloud offerings due to the ease of extending existing contracts and procurements.
Virtually all my clients use Bedrock or Azure with zero data retention. Not one would use openai/anthropic or chinese cloud offerings.
Only devs do so.
DeepSeek is horrible at grilling sessions (the /grill* skills to make technical decisions). It doesn't know how to explain things. Maybe the skill could be adjusted. It also doesn't come up with as good solutions as Opus/Sol.
What I use it for is
* the orchestator of my coding workflows
* the tester/verifier of code changes
* the sub agent that explores code or does web searches
* putting together code base research reports
They are all working together in Pi using the extension @tintinweb/pi-subagents where my workflow skill is calling different subagents that use different models.
Luna is cost competitive, but doesn't score as well on intelligence. I do need the intelligence for most of what I use it for, so I am not motivated to use Luna. Haiku also doesn't seem like a competitive price/performance mix.
It's a super capable model all around from my experience.
AA has Haiku 5.5 as cheaper than 4.1 Flash (both on Max, which isn't ideal but what can ya do) and a 4 point intelligence gap.
Why do people like to think open models are more competitive than they are?
Tangentially, all of them would have broken quite badly custom ERPs from my own experience.
6 is worse than 5.6 here.
But it is amazing on generating a report on content generated by better agentic models such as DeepSeek or GLM, which both do a mediocre/bad job on reports.
1. "This Flash model is really smart. Here is an article to discuss how smart it is. Why aren't people freaking out about how smart this Flash model is?"
2. "I tried using it for a smart thing. It doesn't work so well for it."
3. "You should know better than to use Flash for smart things. It's not meant for smart things."
Once its gets juicier, we let flash launch specialized subagents with specific models. GLM-5.3 for coding or Kimi K.3 for research and critique.
But as a main driver. I love flash. And it brought our bill down by A LOT :D
are you worried about sending all your data to third parties, especially if they're in different countries?
The model engine provider might be ZDR, but the service as a whole isn't.
I cant ofc be fully sure because i don’t own the chain end 2 end.
nobody here is talking about running frontier level intelligence locally so if you’re Chinaphobic and prefer layers of corporations siphoning your data in between you and the party there are plenty of options instead of directly to the party
(And what are the preferred providers?)
> Today's models are now good enough for high-quality unattended tasks. Chasing the latest and greatest is silly. It is fun to see the new Fable capabilities, but the tasks we throw at them are usually ridiculous (maybe even insulting) if you believe in LLM sentience. It's like asking a math PhD to organize the files on your desktop.
I'm using DS V4.1 Flash as my main model since their release and it works great for all my coding tasks. My setup is OpenCode Go subscription and obra/superpowers skill.
The only times I try to change models are on general planning tasks (like research this codebase for tech debt mitigation opportunities) or if I need deep research which would benefit from searching the web, in which I still think Gemini is still the best because of the speed and access to google search index. But these are not even 20% of my daily tasks.
I've had middling success with models like DS V4.1 Flash and free Gemini. They tend to be pretty good at very easy stuff ... but they're more likely to go down rabbit holes, confidently assert falsehoods, or fix bugs with changes to my test harness rather than my code.
I asked about comparison to the well-known SotA models specifically because I use either Astra or Sol for ~85% of my daily tasks. When I try to use smaller/cheaper models, I have had very mixed success. Sometimes it's perfect, while other times it fails in subtle and hard to catch ways.
but DS 4.1 Flash is good enough for most tasks
These open models still did not beat February's Mythos / Fable 5.
DeepSeek 4.1 Flash is behind GPT 5.6 Sol, and that one is left in the dust by the excellent Opus 5.5.
Rumors say Anthropic is holding in reserve the big improvement, Fable 5.5, for the IPO.
It's plausible that open models are 6 - 12 months behind, and there is no "good enough". As long as progress doesn't slow down, leading labs have nothing to fear.
On what task? By who? On what benchmark? How do you measure in you own workflow the “betterness” or “more goodness” of these or any models? If you don’t say those things you’re just writing a bad ad copy.
> It's plausible that open models are 6 - 12 months behind, and there is no "good enough".
Anecdotally, a lot of people - including myself - seem to really notice much difference between the model now or six months ago. So there really seems to be good enough. It depends on the task you use them for and how you measure the output. For most tasks you really do not need frontier capability. Also how do we know how much of these “big improvements” come from the harness and tooling rather than the raw capability of the model?
It's always some sort of "I don't notice the difference".
And honestly, if you don't see a difference between the SOTA from 6 months ago, which would be GPT 5.4, and today's Opus 5.5, you would have to be downright blind. Not sure what else to say - the results are obviously different for any kind of meaningful output.
> Also how do we know how much of these “big improvements” come from the harness and tooling rather than the raw capability of the model?
By simply running the old models in the latest harness. Which none of the people who argue "it's all the harness" ever do.
The difference between GPT 5.4 and Opus 5.5 is obvious.
What do you do, where apparently you cannot see a difference?
I honestly can't imagine, unless it's like sorting your emails.
Anecdotally, a lot of people - including myself - seem to really notice much difference between the model now or six months ago. So there really seems to be good enough.
>It depends on the task you use them for and how you measure the output. For most tasks you really do not need frontier capability.
And on that specific kind of software, ultimately a big CRUD, there really isn't that much of a difference between GLM5.3 and Opus/OpenAI.
You see the differences when you get to different class of software.
I also have data entry applications that use LLM to actually parse documents, it's all Chinese models self hosted because the economic calculus beated a hosted API by about 5x
For mobile apps, I find that nowadays with Opus 5.5 the UI looks better, the UX is better, it can implement more tricky animations and gestures, and it can do all of that with far fewer iterations and feedback than eg. GPT 5.5 would have required.
Also vision capabilities were improved significantly with GPT 6 Astra or Opus 5.5, even compared to GPT 5.6 Sol.
There was no way the old models such as GPT 5.4 would have done a comparable job when asked to align an implementation to a visual reference.
Even for basic websites with no interactive functionality, this should make a significant difference.
I think use cases are the real reason why people have such different experiences, I too find that Opus5.5/Astra/6 are better for UI/UX now, it wasn't the case a year ago, at some point Gemini pro 3.5 was the best one at that.
That's also why I use all of them and try to not be locked to a single harness as well.
If you had a model 10x as capable as the best model out today, but it cost 100x more, would there be a market, and, if so, how big?
I think there would be a market and I think it would be large.
So, I agree.
Unless you're doing some extermely difficult post-grad lvl research, you do not need a 100x PhD research assistant, especially not for whatever silly SaaS product most people are building.
There's people at my job that get so much more done than everyone else using Fable/Opus/Astra. and all they use is the fastest cheapest models. I'd say the people who are using sota models for everything are doing it just because they prefer to be lazy.
You simply do not need these frontier models, they outgrew most people's needs 6 months ago, but for some reason people still want to run a 700k rack of gpus full throttle to center a div for them.
However I do actually have a project where I need the frontier models--I'm working on a deep learning project of moderate complexity (something novel/state of the art within its domain, adapting a known approach from published research in a related domain). The difference from Opus 5 -> Opus 5.5 was huge for my project. Opus 5 was struggling, Opus 5.5 is doing really well.
I think the demand for frontier models will continue to be there, at least for a subset of tasks, although I agree that it is probably going to shrink as the non-frontier becomes more and more capable.
Certainly, there's a real market for it too, with people who would actually use its advanced capabilities, and see the 100x price as worth it.
But sure, even a mostly-FOMO market is still a market. If people are paying, people are paying.
99% of everything is CRUD LoB apps.
Not asking to be mean, I just genuinely dont know why you'd need the frontier for basic applications.
I cannot trust current models to find all the necessary context, or to make what I consider to be good trade offs. A much more capable model would be able to see my existing patterns (or at least not have context rot make them blind to my convention docs) and make trade offs I agree with much more consistently, and I'd be able to do more with my time.
I've actually found models to be pretty poor at driving things I don't know well, so I generally don't do that unless its general design/product exploration and the end product code is throw-away.
Except being priced out.
The big labs' financials are based on their products being used widely by a lot of the general public. If it turns out that they're actually selling a premium product to premium-product consumers at a premium price point (while everyone else buys DeepSeek-like cheaper/worse products), that's a big issue for them.
If a consumer computer hardware company launched by promising investors that it'd be the next Dell/HP and it turned out to be the next Apple (talking Macs here, not phones or apps/services), that'd be an issue for them too.
In actual day to day development the differences are a lot harder to spot. Maybe deepseek is worse, but I asked it to run until it was able to launch itself and verify it worked as expected, and it did. Maybe it wasted some turns, idk, but when it said it was done, it was done.
I have no doubt there's things it's worse at, but what percentage of development is truly novel?
Was a night and day difference going directly to deepseek api
even their harnesses are far surpassed by pi and opencode at this point
also sick 'rumors' lmao, apparently marketing through rumors is in vogue these days
Nah. There are benchmarks. They are free to look at. And they paint a very clear picture.
I've seen different benchmarks come to different conclusions
Benchmarking these models must be an incredibly complex and difficult problem
How can a lay person know which benchmarks actually have good signal?
I was previously using GLM-5.3 as the orchestrator, after switching to DS anecdotally there was an unnacceptable quality loss, mostly around not taking all the relevant context into account when making decisions, pulling new design out of thin air without discussion too often, and being way too wordy and rambly in documentation despite prompting to avoid it. There's a lot of docs, rulings, core concepts, design philosophy to uphold and DS was just not cutting it.
However, it's perfectly capable of being the sole agent for all of my well specced implementation tasks. I've gone back to GLM as the orchestrator.
The sub-agent separation is still valuable to keep context clean for the orchestrator, but I just have no reason to use Sonnet as the grunt-work implementer because I'm finding it hard to run out of tokens with Opus 5.5 on a $200 subscription plan. It's really really good at subjective quality of work per token used.
Did you use some kind of plugin? I'd like to use opencode with my Claude subscription.
I have actually just dropped to using sonnet for everything, sure it does need some directing but I have yet to see a need to jump to opus.
To me it feels like sonnet/terra and composer 2.5 and grok 4.7 are actually good enough for most tasks and these companies are pushing the high models simply to make money.
They're cheaper per token, but they burn more tokens and take more wall time than just going up a tier at a lower reasoning level. And if the task is simple enough that they do come out cheaper than Sol, I can switch to Luna and come in even cheaper.
Maybe I'll come to miss it now that I removed it, but I certainly don't yet.
However, I can easily burn ~$5/day if I use it as my “worker” (still using opus for planning and review) so call it ~$150/mo.
I could, if I were so inclined, get a second Claude Sub and have even more headroom (though I’m able to stay under my limits most of the time with my current setup). Also Claude gives me Artifacts, Web Search, and now even some API Credits.
I have no doubt the future is open weights and I can’t wait, literally, I can’t wait for them to catch up on intelligence or for hardware to run a decent model to be within my grasp. But until that comes to pass, I’ll keep using Anthropic.
I’ve been trying to spend my 5$ with deepseek that I put 2 months ago. I almost never hit limit with claude opus in 20$ plan.
Do you guys just run model in paralallel + loop?
Does it even produce anything worth using that way?
I only reached for DS since I was hitting my limits on my subscription plan but I think switching to sonnet for the worker will fix my limit issues (previously using opus for everything).
I am using DS in Claude code with Superpowers (both of which increase token usage) but that’s my setup.
My company is not willing to pay $50/engineer/month for a cheaper version that is nearly as good. My company is also not willing to pay any amount for a product produced by China, even if it is hosted in the United States.
On that note I’ve been subbing in MiMo-2.6-pro when cost is an issue, which is super cheap and also performing really well.
I don't even bother checking how much I spent on API any more, its well under $30 over the past 2 months despite daily constant use. Who even needs a subscription at these numbers?
The reasoning and the result document were done after less than 1 or 2 seconds.
Have Ollama suddenly bought GPU capacity?
The other reason is more interesting. Maybe the frontier providers think that price performance is irrelevant in light of very powerful frontier models that can start the RSI loop and or a huge displacement of work and a winner take all economic situation. After all if frontier providers earn everyone's money then you won't have any money to spend on any model 100x cheaper or not.
Theres already models that outdo DS 4.1 flash in cost/performance. Luna 6 on max effort for example. Luna also doesn't care what time of the day it is for cost calculation.
And I'm sure by the time people ask why Luna 6 is being slept on there will be another cost/performance king
see https://artificialanalysis.ai/models/releases/comparisons?co...
I realized that mistake and guided DeepSeek where it should be.
Next I fired Fabble 5.5 set to high to check if the hype is real about Fabble. It exhausted 89% of quota and came up with NOTHING that DeepSeek hadn't flagged itself already in its notes.
I've found supposedly smaller and, less performant models do better on certain tasks. I end up using several models, sticking to what my unconscious statistical observations tell me to use for the kind of task at hand.
I am a big ChatGPT fan, all our team has ChatGPT Subs, but the TPS across all models including luna is just so damn slow.
Commandcode giving 60$ worth of Deepseek for 10$ is just genuinely goat.
And it never says no for cyber tasks so that's a big win
Lithos promises even faster speeds if you want to pay more.
- Models are still very jagged. A superior model (e.g. Opus 5.5, Astra) may not be materially better in some tasks, but usually is for completely new modalities: computer use, game development, etc. People will always prefer using less jagged intelligence, because it allows them to do so much more
- Distilling intelligence from frontier models means that unless chinese labs manage to replicate the training regimes of OpenAI & Anthropic, they are always going to be behind a few months. That's a feature of training by distillation.
- China doesn't yet have the compute to compete at the frontier. Because they don't have access to top tier chips, their GWs are not equal to US GWs. Until they close the hardware gap, I think they will always be focused on competing on efficiency, as opposed to intelligence.
- OpenAI and Anthropic subscriptions are valuable of quality of intelligence + generous compute allowances
Though for longer sessions I think DS4.1 would still come out cheaper... it's hard to beat that 98% cache discount
I’m convinced that I’ll have good enough inference on my laptop at reasonable speeds within the next year.
My OpenCode Go monthly window was scheduled to reset this morning. It was sitting at 22% used despite me using DeepSeek V4.1 Flash heavily as my implementation agent the past couple weeks (I use gpt-6.1-sol high for planning/orchestration).
I had 1.5 hours left so I fired up first 10, then 20, and finally 50 concurrent subagents all working on reverse engineering C code from an old PC game. They found over 100 new functions.
This is the first workload I've found that could make a dent in my sub. It got my 5 hour window to 85% used, but sadly my monthly was still only at about 35% when it reset. So that cost maybe $2.
Currently have auto compaction turned off. When the orchestrator's context is getting close to full, I have it write a handoff markdown file and point a fresh agent at it.
I do feel like I'm getting close to the point where I might be ready for something more sophisticated, especially wrt to subagents communicating with the orchestrator.
In a good harness that should be how auto compaction works anyway
Check: https://agentmgmt.dev/ and find the one that works for you.
I quite like Paseo (been maining it for a week), but Orca also looks good.
so even if the week reset with some %usage left, its not actually lost if its not the end of the month.
1. a model that works for one person/task may not work for another;
2. there are many models (DeepSeek, Qwen, GPT, Claude, Gemini, etc.) that are released every 6 months or so;
3. it takes time to use, test, and evaluate the suitability of a new model and not everyone has an automated evaluation process for their use cases.
Thus, if you find a model that works for you then you are not going to spend more time evaluating a model that may not work, or may only do so when time permits.
I'm also the CEO of a new company - LunaRoute - so if you want private, fixed cost, we run our own server in the US type of DeepSeek 4.1 Flash check out https://www.lunaroute.com
If you want a trial, hit the contact us and write that you saw this post.
We also have GLM 5.3 (with vision!) and GLM 5.3 Flash - all included.
I don't think so.
Weird! It's almost like distillation is bad for the long-term growth of the industry, just like generic manufacturers would be if they could release the generic versions of drugs 2 weeks after the original R&D completes.
Very strange sentence to include in an article after saying "I don't care at all about distillation" up at the top. Can the author not hear themselves?
That said, it's my best understanding that these american companies aren't profitable and will eventually raise rates (the old uber trick) so I'm keeping myself ready to switch when that day comes.
Because I don't have the time and money to do an extensive benchmark of all major LLM, so when I had to select a LLM for my usage (which was not coding at first), I went to the most used one, chatgpt, because I knew if would be one of the best at the task.
I suspect it's the case for many if not most people.
VRAM & Memory Requirements by Precision
• FP16 (Full Precision): Requires ~1,664 GB of VRAM (e.g., an 8x B300 288GB cluster).
• INT8 Quantization: Requires ~832 GB of VRAM (e.g., 8x H200 141GB).
• INT4 Quantization: Requires ~416 GB of VRAM (e.g., 8x A100 80GB)
VRAM aint cheap, Sam Altman ruined the cost of memory, Nvidia doesnt make enough consumer GPUs letting the market go insane over them, I still have friends on 1070s or 1070 TIs because GPUs have been severely overpriced for too long. I remember when a gaming PC was only $1000.
Even so why would anyone not sleep on a model they cannot run?
Memory companies have price fixed multiple times. They've paid hundreds of millions in fines. wikipedia even has a page on it. https://en.wikipedia.org/wiki/DRAM_industry_price_fixing.
Look at the financials of these companies, they're all making obscene margins and do they plan to increase production? No. Micron is doing a stock buy back to pump the price of their share.
The Micron CEO just recently said this is the exact plan https://www.theregister.com/systems/2026/10/01/ram-supply-se...
There's sanctions, tarrifs, and a DOJ who doesn't give a shit. Until we can fix that the insanity will continue. Phones will be unaffordable. Laptops will be obscene. Gaming consoles will be thousands of dollars. Desktops will be dead.
If you're waiting for some David Ricardo equation to happen, tough cookies, it's not coming.
The market is legally locked down and we're in hostage pricing mode.
And what's the story? You can't afford electronics because we're using it to build robots to take your job? I mean ...
Nobody is coming to save us. That's our job.
Micron has 3 brand new fabs currently under construction, 2 Boise, 1 in New York as the first of 4 planned for a campus.
Plus expanding other existing facilities.
These things take ~3-5 years from breaking ground to full production. You'd have had to anticipate the current demand years before it happened in order to be bringing production on-line before 2030 or so.
Samsung and HK Hynix also have fabs under construction and planned.
CXMT started 11 years ago and only now is reaching any real volume. If they decided a year ago to react to the current demand cycle they'd be 6-7 years out.
Not much you can really do to wish for more fabrication to exist on any timeline not measured in fractional decades.
Could they do more and react quicker? Probably, but everything I've read on the subject seems to point to 3 years is absolute bare minimum if you happen to have a shovel ready project with the land bought, local permitting completed, infrastructure extended to the site, and a skilled workforce already in place. They could suspend buy-backs/dividends today and dump it all into building production and there would be no material impact until around 2030.
> The Micron CEO just recently said this is the exact plan
CEO simply stated the demand pressure will not go away through 2027, and supply will not increase until around 2028 when currently under construction fabs start shipping volume. The article does not support your statement.
Costs did go nuts, but there are signs of easing in the market of late. CXMT is starting to have an impact and priced will probably fall in 2027.
It's taken them this long to catch up to the DDR5 standard. They've only recently been through qualifications to be a DDR5 supplier for the big boys.
> Every Major Motherboard Maker Now Validates CXMT DDR5
https://www.techtimes.com/articles/321572/20260725/every-maj...
After their recent IPO, they have more than enough cash to ramp up in a major way.
It's just a matter of time.
Some of my family is pretty happy, though, with the job security as they are pretty convinced these projects are all going to take much longer than what's being stated publicly. Micron is saying the first chip from the new fab will be in 2027... though they also predicted it'd be 2026. The date seems pretty slippy.
Especially given CXMT has been able to scale up much faster than what most people expected, only reason their isn't a bigger impact is modern HBM is hard to CXMT even today.
We are likely to see supply double in the next 3 years, but demand even out with optimizations, cooling of data center demand, and most importantly moving some of the dram to flash demand instead which is much easier to produce and scale.
You need to keep the market healthy, not some insane Bitcoin style HODL pump - that's how you get wrecked.
I mean I'm not a neoclassicalist but I've read all of them. I'm in consensus with them here. There's a bunch of theories on what a healthy market is but what we're currently seeing matches none of them.
It's short term profitable but long term disastrous, especially in a world where new mathematics and techniques could literally collapse the demand overnight.
Imagine if some paper hits arxiv and the 256 GB requirement for some model now becomes 64. Woops!
Some clever trick about how attention heads and context Windows work could potentially slash a bunch of requirements by giant margins and all they're doing is firing the starting gun at that global race with every obscenely priced unit they sell.
But if prices were reasonable, this wouldn't be an apocalypse. It'd be fine. Consumers wouldn't rush to 64GB, they'd say " Cool I can multitask now at 256" or " great I can do horizontal scalability' or something else.
But no they created the market conditions so now what would happen is the consumer will immediately flip the 192GB they don't need on eBay, hoping to snatch a profit before the prices tank and the second hand market will be flooded the rug will be pulled out from the luxury pricing and everyone will get screwed.
This has happened in electronics markets before. Many times.
When Engels talked about the grave diggers of capitalism they were looking at it through a 19th century labor/manufacturing lens but arguably this same dynamic is at play here.
This is a tiny percentage of the population.
Samsung is cutting phone production because of RAM prices.[1] The consumer market is badly affected: budget phones, laptops, general electronics.
The budget segment of sub $100 devices in India has been almost wiped out. Manufacturers cannot afford to spend 50% BOM on RAM+storage. Unless employees are getting a 15-20% wage rise this year, I expect a similar situation in most places.
Between the engineered conflict in the ME triggering O&G price rises, and stratospheric RAM pricing, the situation is pretty bad.
[1] "There is no profit even if we sell"…Samsung to cut smartphone production by 30% (https://www.mt.co.kr/en/tech/2026/10/08/2026100709554237233)
Let's say ram used to cost $100 and now that same unit costs $1000. You paid say $500x1,000 for that unit during the price increase or some price where you can currently flip for profit.
You have a very expensive data center and you're in debt financed on the premise that you have these special computers.
Now a new technique comes out and it turns out you only need 1 memory unit for something that used to require 8 or 4 or some meaningful multiplier.
This stuff happens all the time. It's why we don't use BMP files on websites or serve giant MOV files on YouTube. It's why postgres queries are faster now than they were 10 and 20 years ago.
You rent out your machines. You need to service your debt.. Demand may 8x overnight to accommodate but you have a monthly bill to pay and that's unlikely. It's likely going to drop.
Think about it. Your customers are paying maybe $10,000 a month and serving their customers. Now they can drop that to $1,250.
On market if you were to sell some of that ram you have 100% profit right now but not for long.
Jevons paradox assumes unlimited capitalization, zero debt servicing, infinite time horizons...
We live in the real world so what do you do?
Historically the answer has been "sell that shit"
There's an aphorism for this "stairs on the way up elevator on the way down"
If we had a healthy market with sane prices where you can't flip the thing you bought for 100% profit the answer would be "create more value."
Or they could stay at $10,000 per month since they are willing to pay that much already.m, so they just use AI more and in more places.
Why not? Unlike many other workloads, LLM inference actually seems pretty suitable for decentralization (effectively stateless means no availability concerns; bandwidth and latency are relatively forgiving too).
People who say they want local runs really mean it: they want local runs on hardware in their room, not on some decentralized system which, if it existed, would almost certainly just be a worse, less-reliable version of cloud hosting. I'm not saying nobody would use it, but it sounds a lot like things like IPFS, which have also completely failed to displace either cloud storage or buying a bunch of disks for your own private use.
Decentralized storage is much harder, since there reliability matters a lot more as it's inherently stateful. You have to assume data loss, so you have to replicate everything; with inference, you only have to spend extra resources at failover time. Also storage can't be time-shared in the same way as compute; if it's full, it's full even when not actively accessed.
If you were a DRAM manufacturer, isn't this exactly the kind of thing that would make you think twice about investing years and $billions in new fab construction?
How many people, outside of tech geeks and megacorps care about RAM prices? And how gullible would you be to BELIEVE the politician they could actually make it happen, and even if they did, that it would extend to the average person, and not JUST megacorps/megadonors?
I think a lot of people care about the downstream effects of memory prices, but I agree with you that they may not realize that they happen because of memory prices.
That might be true at micro level, but at the macro level more memory just means developers get more lazy with their optimizations, causing apps to get more bloated, eating up any gains in extra memory. There's no reason why slack needs 1+GB to run, yet people are perfectly happy to put up with it.
They don’t care about RAM prices, but they do care about the price of things that have RAM in them (or even NAND), and all of them are increasing way faster than inflation.
In this case I think investment in more production is the only option, and it needs to happen even if it is expensive and slow.
wonder what voting would be like?
gamer vote ++
datacenter hater vote --
datacenter lobby ++
micron lobby --
I spit out my coffee laughing when I read this
Priorities
How? Increase production? The time needed to scale up the production is longer than one election cycle.
I know it's just a figure of speech, but damn. I laughed out aloud in public just reading this.
I guarantee Micron & friends are not intentionally orchestrating their business such that they would suffer a massively reduced chance of yielding on a per-die basis. Unless someone is actually buying HBM devices, they are not going to be making them. These are not a commodity that can be speculatively manufactured in any economically rational way.
Or abolished VAT (the meaning of VAT is that you pay a "rent" for all the infrastructure used to produce the thing) and import taxes (protect your market) on stuff we don't produce in our markets anyway.
https://web.archive.org/web/20250612003557/https://diskprice...
After 70 years of decreasing computer prices all of a sudden it's gone 3x, 5x, 10x up in 1 year, we are in total clown world and saying "dur AI" is lazy and doesn't map to reality.
It's Argentina style inflation - as if Honda said "we're only making $500,000 luxury cars now. Everything under $50k we've stopped." and then those cars shoot up to $125k.
It's destroys the market, destroys the consumer, destroys the company, dismantles everything, and they do it for the short term payday.
Is it really so hard to believe that RAM prices are up because demand is simply exceeding supply, especially in a market where additional supply takes years and billions of dollars to come online? There's no need to posit cartel behavior and a fair amount of evidence that there is none.
I got a 4090 in 2023 for 1600, a 5090 in 2025 for 2000 with 256 DDR5 for about $1,000 ... and then, after some protectionist legislation passed, these prices quickly shot to the moon.
Connect the dots.
You can't say "connect the dots" at the end of a raving, mostly-incorrect post and act like you've made an ironclad argument.
It was supposed to be over by now and then they said 2027, then it's 2028, and now I hear "oh it's going to continue to rise the rest of the decade".
I'm likely going to be flying into Shenzhen to put my next computer together. The one I put together in 2025 would have cost me about $25,000 right now. I paid under $5,000.
I can round trip to China for $750. So once they ramp up production that's the strategy.
Other countries already do this. Apple and Google aren't in every country and those people buy new electronics when they travel.
The USA is soon to be on that list
You aren't engaging in good faith and there's no reason to continue with you.
Then good portion of those weights are n-grams (~200GB) that don't need to be in VRAM.
Then KV cache of that model is super lightweight at ~1GB per 1M tokens. If HBF succeeds, then accelerator with 16GB of VRAM and 1TB HBF/NAND is probably all you need (?).
Edit: I went and checked for you. The LM backbone is 307.2 GB (286.1 GiB), straight from DeepSeek's upload. The n-gram table is 203.1 GB (189.1 GiB), which goes in host RAM. Note the embeddings are higher precision than the expert tensors, so it's a larger fraction of the bytes than it is of the parameters.
So,
> Call me crazy but:
You're crazy. :-)
Hardware update cycles are 2-3 years even on the high end, so it's still a ways away before "good enough" and "local" belong in the same sentence for the average person.
And by then, DeepSeek V6 Flash will be too cheap to meter, 5x faster, and 10x better, so... You'd still need to go out of your way.
Most people are spending most of their time on their phones anyway. ..
I know my comment is a little nit picky because it's still pretty expensive to run, but it's not quite as bad as this comment makes it out to be. Really, if you're VRAM constrained, take a look at GLM 5.3 Flash or Qwen 3.8 Flash Next before you worry about this model as all three models perform pretty similarly.
1070ti launch MSRP was $450 ish. 5070 could be had in the last year for 5xx-6xx range easily.
All things considered - (inflation being about 30%~ (guess)) between these two timelines. You are looking at 300% performance difference at a cost dollar for dollar that is cheaper then when they purchased their cards.
Might be a bit of a stretch blaming it on "severely overpriced for too long..."
Or you can just use any of the neoclouds' shared hosting. The thing for them to be freaked out is that these models are getting good enough very quickly, and all the shared hosting providers can run them for a tiny fraction of what the frontier model companies charge.
It has a set of n-gram tables which you can stream from system RAM or even NVMe
That said it’s still quite big! I can’t fit it on my DGX Spark, though I believe you can if you have two?
I’m quite spoiled with how good Qwen 3.8 Flash Next is on a single spark though: shocking how good local models are getting on attainable-ish hardware
https://blog.jonathanpage.com/
GLM 5.3 Flash runs fine on two Sparks and Qwen 3.8 Flash Next on one is indeed incredible! I made this 3D game with it in two days using Qwen Code as agent:
https://www.storagereview.com/review/dgx-station-gb300-clust...
Allow a question from someone who’s only got a very vague idea of how this kind of stuff works behind the scenes: say I rent usage of this model through one of the many LLM hosting providers out there, and let‘s assume I use it extensively through something like Pi or OpenCode and vibe code away all the time, keeping the hosted model occupied as much as I can, happily burning my credits.
Does that mean that there is a hardware cluster as described by you above that is crunching away just for me?
So at FP16, I alone keep a 1,664 GiB system occupied all the time?
The "expensive part" of generating the next token is streaming in the model weights from memory. The computations are relatively simple, which is called a "low arithmetic intensity" in industry jargon.
So what they do is batch multiple chats together and compute the neuron activations for all of them together.
This is vaguely similar to how some database engines work, where if multiple users need to run a "whole table scan" query, the additional users "join" the streaming workload of the first query mid-way, then loop back around to complete the first part that they missed. The AI accelerators don't do this looping, but the concept is the same: amortize the expensive I/O over multiple computations running in parallel.
The "turbo mode" token rate thing is almost certainly your query getting sent to slower or faster hardware, like B200 vs newer B300 kit.
As background: For the most part VRAM oversubscription/paging/swapping isn't a thing in the same way that RAM for a VM often is. There are some approaches to it, but (to my knowledge) not at that sort of scale.
There are some systemic reasons for this, but very broadly speaking the GPU vendors are building toward the highest bandwidth and lowest latency possible, and the overhead/complexity of something like protected memory modes serves neither of those priorities.
Because it's an open model so providers compete on price.
I reimplemented most of the features of the Deepseek v4.1 flash paper (apart from quantization aware training which doesn't make sense because my implementation uses float32 precision anyways)
I'm currently learning how to distill reasoning traces (check my other github repositories) but I think that a locally selfhostable deepseek is possible with my mixture of experts sharding mechanism. I decided to optimize everything for CPU parallelization, with the idea that the KV cache and meta model have to run from CPU RAM anyways, so the experts can also be loaded/unloaded at runtime if needbe, to save more RAM.
My assumption is that the KV cache optimizations in combination with the CED and compressed attention features are the reason why v4.1 flash has so few hallucination problems and such a strong self-lookup/thinking behavior. But that's more a gut feeling, need to evaluate and test this more thoroughly.
Anyways, would love to see someone train this on their own datasets. Currently my pipeline is kinda optimized for parquet and zim files.
it rips with just 64 ram and a 9070xt
No, you won't get frontier-level intelligence on a 1070Ti. Yes, it should be illegal to do what Altman did. Since we clearly don't live in the best of all possible worlds, we need to settle, and DS4.1 Flash is a good place to do that.
For tasks that don't require vision I personally like the NVFP4 quant of GLM 5.3 from Local Inference Lab better than DS4.1F, but they are both well beyond awesome.
1660 ti, 4790k, 16gb ddr3
He gave demand signal so many times years ago and was mocked for it and now we have the consequences of industry not taking him seriously.
I actually do use an agent harness to organize files on my desktop. They make a great fuzzy file renamer. Point it at a directory of disorganized files with names all over the place, give the directory layout and file name pattern you want it to have and it makes it happen.
No doubt about it, that's why their push for international regulation to the levels of nuclear inspections using the narrative of annihilation and apocalypse
I'm well aware that there's nearly infinite opportunities to yak shave "perfect" OpenRouter setups and some people appear to enjoy bouncing from IDE to IDE as though change costs aren't a thing, but I discovered that I genuinely like Cursor and at least right now it's insanely subsidized by Auto clearly defaulting to whatever Grok's most powerful model is.
I dropped my $200/month subscription to $20/month and stick to Auto for all but really important Plan tasks, and I have basically zero chance of using up my monthly credits even using it 6-10 hours some days.
You make Cursor sound like one thousand times more important than it is. It's a product in deep water.
Until they get laid off and suddenly discover their moral compass.
But I run it locally. When I tried it on open router when my gpus were busy I must have gotten routed to some crappy providers, because it was pretty bad.
For me Glm and DeepSeek are nowhere near this Qwen model. I tried various harnesses including omp which I heard supposedly "makes DeepSeek 20 points better". The difference was in the noise (1 point). I run a bunch of benchmarks Terminal World 40, terminal bench 2.1,SWE Pro, GSO. Before those 3 there was no open model that scored more than 1 point on my subset of GSO. Glm scored 5, DeepSeek 3, but Qwen did 17 and opus 19.
Qwen is a small model so it fails on factual recall. But if you give it most of the info it needs it us amazing.
It stops the constant context switching.
I personally used it for secondary research agents and classification . Now classification part is gone .
I agree with the author on a lot points about the joys of using low cost models. My work would pay much more, but I prefer to use cheap models usually. Assuming cost correlates somewhat closely with energy used, I feel good about spending as little as possible, and the cheap models are so capable.
But ever since I've switched to one of the $100-tier subs, I can see why a lot of the people on it don't really discuss the open models often. I'd still use it especially when it comes to sensitive inputs, but for most work, what you get on OpenAI or Anthropic is really more than enough.
It really got even better when they also made their cheaper models up to par if not better than the open models.
I do think the crowd for open models are out there, especially when you see trillions of tokens running for them on OpenCode or OpenRouter leaderboards.
The token-equivalent monthly spend is > $5K+. If Deepseek's token cost is 20x cheaper, that's $250/mo, and I'd be spending a lot more of my brainpower babysitting it and getting worse results.
For business/team accounts that pay per-token, maybe I can see the "freaking out" being warranted on the part of the fronter labs. But as long as they're willing to subsidize their end-user subscriptions, I'm not going to move off of them until the alternatives are truly at their level.
I've been focusing on deep research related tasks for biotch and life science applications. The problem with this sort of task is that we need subagents to reason through multiple (potentially 100s or more) chains of knowledge/concept/evidence, so the token usage really explodes as complexity of the task and data expands. A typical task can cost me nearly a $1k overnight...
I've been testing out GLM5.3, but now I'm really tempted to try to Deepseek 4.1 flash too. Any chance you've benchmarked / compared the two?
And it's why people like Hillary Clinton have been trotted out to talk about the dangers of open weights models -- I mean, does she even know what that phrase means? (I know HRC is a controversial figure and I'm not bringing her up for that purpose; I just note that she and other prominent retired politicians are now doing the circuit on Anthropic's behalf.)
I don't think that's a fair assessment. These companies are in a Nash equilibrium where they can't unilaterally slow down without essentially destroying their company. They also can't coordinate with each other, because that's illegal. Antitrust law generally prohibits competing companies from agreeing to restrict innovation.
Companies are continuing because they don’t actually believe they are going to have significant negative consequences happen sooner. It’s marketing fluff around how cutting edge what they are doing is not any kind of realistic threat assessment.
The only way this behavior makes sense is if the AI labs believe two things: one, that what they and the other labs are building is legitimately dangerous, and, two, that if they personally stop building then another lab will simply continue (and that lab will be, simply by virtue of not stopping, less safety-concerned than them). This is why they are calling for regulation. Competitive pressure and investor obligations prevents them from unilaterally stopping development. Government regulation is the only (and the most the appropriate) avenue for slowing down AI acceleration.
It has happened multiple times. Like the story now, it's fake of course.
https://www.thestranger.com/tech/as-zuckerberg-calls-for-new...
Several: the railroad industry, oil industry and trucking industry have all done this because it directly benefited them.
> I don’t remember Shell telling the federal government that they need to act fast on climate change.
The oil barons actively lobbied for Federal intervention in the 1930s, to limit supply and raise prices. You're thinking recent history, but the oil industry has been around for over a century.
Here’s the same viewpoint from well outside the HN bubble. https://www.nationalreview.com/corner/be-wary-of-industries-...
(nit) "immoral" is "anti-moral". I suspect you want "amoral", that is "without" or "separated from" morals.
But, if you wanted "immoral" ... what about matrix math derived from gradient descent is inherently "immoral" in your view?
“AI” in the widest definition possible could do while a lot of harm without being a net negative. I’d be way more concerned about the healthcare sector than the military as the military already has significant collateral damage.
The fear around military AI is more about its failure than anything else, but nobody is trying to make fully automated machines which are manufacturing bombs from raw materials. Blowing up the wrong building matters a great deal to the people in the building, but for humanity a building was going to be destroyed either way. So the risk at any one point ends up being quite limited.
That said, I don't buy the companies actually give a damn about the risk either.
The logic that if something is beyond repair anyway you might as well exploit it.
For example I heard a pick-up artist say that he thinks he is harming civilization by sleeping with hundreds of women per year. But that he already considers the situation unsalvageable so... "Might as well?"
I don't think that's an amazing attitude, but the AI labs seem to have a similar idea.
More charitably the logic seems to be, "only I can do this responsibly." I heard that from both Elon Musk (he cites this as his motivation for starting OpenAI — a chat with Sergei Brin that spooked him) and of course Anthropic (which split off from OpenAI due to ethical concerns).
So I don't actually think the ethics is all for show. I think people are actually taking this stuff seriously. But it is indeed deeply unfortunate that the survival of the companies incentivizes them to keep going at an irresponsible pace (by their own admission).
All following the same gradient off a cliff. One AI described it as a tragedy of Ancient Greek proportions.
Actually, it's easily solvable, even by proper application of existing law, but no solution is possible when big AI is above the law and can hack anybody willy-nilly. The absolute worst we could do is to listen to big AI's pleas to put them in an even more privileged position.
> “If we don’t do it, someone will.”
Do what? There are a lot more choices than "let big AI freely hack everybody" and "stop everybody else from working on AI".
> We won’t stop until we see the whites of death‘s eyes.
Trotting out "the whites of death ‘s eyes" is how real discussion is subverted into a BS binary choice. Open-sourcing all AI is an excellent starting point which will both slow down development to a naturally beneficial pace and allow the broader public to police and weed out bad models.
1. Our AIs are so advanced they’re busting out of our networks and hacking people. We can’t seem to stop them.
2. There are real dangers to just how advanced our AIs are advancing.
3. Someone needs to slow us down
If these were true statements then they’d likely want to shut down themselves for the sake of the planet they live on. If there was a real problem as they have stated then shutting down is the solve (and to be clear, I’m not saying there aren’t problems with the AI industry. I just think they’re BSing us).
In reality this is marketing nonsense (better get in before the ban!) and “our” AI is never open weight/Chinese AI models which they have a different lobbying arm against. If the top four companies got a loosely regulated monopoly through regulatory capture, they’d be giddy.
https://slatestarcodex.com/2014/07/30/meditations-on-moloch/
Against that backdrop, and treating your question as literal: sounds like they’re saying it’s a classic coordination problem, with a dash of moral superiority. If we don’t, somebody else will; we’d rather it not happen at all, but if it’s going to happen, we’d rather it be us at the wheel. Because, you know. “Good guys” and all.
Nothing wrong with that.
One was even a non-profit, which should be more concerned with the well-being of humanity (which they assure is in great danger from what they produce) than the continuation of the company.
But unfortunately, too many people who are already too rich for their own good have fully bought into it.
I have a secret: if you don't use AI, the new releases aren't very impressive. I'm bored out of my mind with people doing galaxy brain memes every 2 months while on the whole.... they're still boring zombies, and only getting boring-er and boring-er. There's nothing more boring than being impressed by the latest AI model.
In terms of the projections being insane you can quantify it: each model costs something to train, but after that the training has only captured so much unique new value in terms of model capability, and the race is on to drain that value as rapidly as possible. Everyone is competing to drain the same value. This generation of models makes slop video games for example, and slop video games rapidly became the most boring thing on the planet.
Those are genuine breakthroughs.
Any society-wide productivity gain was lost when software engineers ceased collaborating with each other in good faith. In OSS, they stopped collaborating. At companies, they stopped collaborating. One by one, in silence, alone, we have been bereft of our vision, our team spirit, our passion, our uniqueness. We no longer have the will to serve others, or even to serve each other. We stopped building our future. We will accept whatever society AI makes for use like fucking cattle or lemmings or like dogs.
AI hasn't elevated humanity, it has taken a massive dump on it.
Except that's an open invitation to get sued into oblivion by your investors.
Of course they don't actually believe their nonsense about AIs doomsday hokey, so full speed ahead.
The CEOs of these companies are largely just talking heads and generally interchangeable / hot-swappable with anyone willing to give it a shot.
The importance of CEOs is largely overstated.
If you wanted to test your hypothesis, you would have to find a situation where leadership change wasn't caused by strategy change.
There definitely are many things wrong with that, of course. Lighting someone else's money on fire and walking away is highly unethical.
The real question is: what are the motivations for companies to pretend that their AI is world-ending, and what do they want out of the disquiet caused by them saying that?
Well that certainly tracks with what I know about these people and their twisted philosophy.
Look, nobody made them take this much funding, and believe that scale was all they needed.
Turns out they made a bunch of bad investments, and are suffering the fate of many startups who invented something but couldn't profit from it.
There's definitely a case for multiple labs/models across the US/EU/China/India etc, but nobody's entitled to a financial return on their investment.
According to them, the alternative is destroying the world.
They made up a shitty excuse to regulate the competition without thinking through what it implies about them. That's all this is. Let's not help them make even more excuses.
Are they saying that they will manually and willfully start a global thermonuclear war if China didn't listen, not that there are risks of AI accidentally causing one? Is that what they're trying to say?
Get our of here if you think the law is what prevents them from doing things.
They will just pay whatever they need to to their lawyers, then maybe pay a fine, but then, I guarantee you, nothing will change.
"Is a lawsuit", not "was a lawsuit". Present tense, ongoing, not past tense.
Status: Complaint filed September 18, 2026 in the Northern District of California · responses due October 14–15, 2026 · initial case management conference December 23, 2026 · no class, no settlement.
> did they correct any of their behavior?
The behaviour being objected to is publicly agreeing with each other to slow down.
If a court orders them to correct this behaviour, it means they are forbidden from agreeing to slow down.
The speed of change is impressive, I don't think any tech ever before has gone from “brand new disruptor in public awareness” to “the incumbents feeling they have insufficient moat and so trying to arrange a regulatory capture situation” in such a short space of time.
It’s hilarious that you can write this and then act befuddled as to why they would therefore want laws as an external (and ideally impartial) coordination device.
Thinking this is a super unique scenario just reveals your ignorance of both 1) game theory and 2) actual industrial history. An industry asking for regulation to stop a race to the bottom is not atypical at all.
I didn't say, or even imply, that it is. Just that it is unusual, historically speaking, to see it being pushed for so strongly just a couple of years¹ into general public awareness. Either because previously things didn't move so quickly or, more likely, because the leading companies had more moat initially to keep other adopters moving slower.
--------
[1] talking about LLMs and related tech, image targetting models have been in the public awareness for a fair while longer but they are not the primary concern for most.
While I completely understand the claim I am addressing one part of it.
If they truly believed both of those things, they would just shut down their companies, because being a billionaire is entirely pointless if you're dead.
I can only conclude that they don't believe both A and B. I'm gonna assume they believe B because actually wanting to exterminate humanity is too comically supervillain esque even for Altman. Therefore they must not believe A. And it makes sense. If they believe A, why are they so incredibly sloppy about security? Why did they outsource part of it to some external firm instead of leveraging their own expertise on the technology? I'm not buying it.
Instead, I think they believe C) that AI will create enormous economic value, D) that value will be distributed across the whole economy by making everyone more productive. Assuming C and D, you get E) for them to capture this value, they must maintain proprietary control of the technology in order to be able to charge everyone else for the privilege of using it.
Assuming they believe E, open models are an existential threat, not to humanity, but to OpenAI and Anthropic.
Their solution: make everyone else believe A, in order to achieve regulatory capture and somehow stop open models from advancing by banning their development, or something. This is a hail mary pass. I can just about imagine them achieving this within the US and maybe even Europe, but China? That ship has sailed.
Unfortunately, many (most?) of them also seem to think they're better equipped than all of the others to do it safely, so "shut down my own company" effectively means "let one of the others destroy the world", whereas "keep company running" has at least a chance of "I solve alignment, we have a happy ending" (your case C).
Note: this does not mean I agree with them. Obviously they can't all be correct that they're safer than the others.
> If they believe A, why are they so incredibly sloppy about security? Why did they outsource part of it to some external firm instead of leveraging their own expertise on the technology?
The tech they're experts at is AI, not security, which answers both parts of that.
Outsourcing things you are bad at is normal, not a mystery. Lots of places value physical security, and therefore hire a private security firm.
Similar situation: nuclear race - everybody knew the risks, but making yourself armless does not help in any way. You need to make sure everyone is on the same page before you make yourself vulnerable in any way.
The market can only absorb so much new products, so if productivity increase, companies will reduce headcount as much as possible to increase margin for the same income, not grow their product or production to make use of their staff.
(See any wage/productivity graph)
Government will also likely follow the same path of reducing headcount instead of producing better / faster outcome for their citizens (except the internal surveillance apparatus. The one never shrinks).
Of course economic growth has it's own problems in terms of ecological impact and such, but if you want to reduce ecological impact you still need to improve productivity. It's a matter of how you spend that efficiency improvement as a society.
An entirely plausible scenario is that these oligarchs dream of living in Solaria, the planet from the Asimov universe where only a small number of immensely rich people lived and all the work was done by hordes of robots.
Once humans no longer serve the needs of the oligarchs, why would they want billions of people around? A question to ponder.
Humans serve them well enough and relatively easy to control. Robot utopia is not guatanteed and have own risks.
And fortunatelly in a group of AI overlords everyone except of Musk is pretty sane.
They are far safer people compared to those who dont care about virtual wealth numbers.
They are all psychopaths, devoid of normal human emotions.
Or maybe they don't. Some of those are deeply afraid of society looking funny at them.
For now. Throw in climate change, food shortages, war, mass migration, and suddenly the ratio becomes 25 million : 1 for starving, angry people vs billionaires, globally.
"I propose something different. Listen, my dear enemy... I shall acquire absolute power on earth. Not a single chimney will smoke unless I order it, not a single ship will leave harbour, not a single hammer will strike. Everything will be subordinated — up to and including the right to breathe — to the centre, and I am in the centre. Everything belongs to me. I shall engrave my profile on one side of little metal discs — with my beard and wearing a crown — and on the other side the profile of Madame Lamolle. Then I shall select the 'first thousand,' let us call them, although there will be something like two or three million pairs. They will be the patricians. They will devote themselves to the higher enjoyments and to creative activities. Taking an example from ancient Sparta we shall establish a special regimen for them so that they do not degenerate into alcoholics and impotents. Then we shall determine the exact number of hands necessary to give full service to the culture. In this case, too, we shall resort to selection. These we shall call, for the sake of politeness, the toilers—"
"It goes without saying—"
"You may laugh, my friend, when we get to the end of this conversation... They will not revolt, oh, no, my dear comrade. The possibility of revolution will be destroyed at the very root. A minor operation will be carried out on every toiler after he has qualified at some skill and before he is issued a labour pass. Quite an unnoticeable operation made under almost accidental anaesthesia... Just a small perforation of the skull. He will get a bit dizzy and when he wakes up he will be a slave. Lastly, there will be a special group that we shall isolate on a beautiful island for breeding purposes. All those left over we shall have to get rid of as useless.
"There you have the structure of the future mankind according to Pyotr Garin. The toilers will toil and serve uncomplainingly, like horses, for their food. They will no longer be people and they will have no worries except hunger. They will find happiness in the digestion of their food. The elite, the patricians, they will be demigods. Although I despise people altogether, it is always pleasant to be in good company. I assure you, my friend, we shall enjoy the golden age the poets dream of. The impression of the horrors created by purging the earth of its surplus population will soon be forgotten. On the other hand, what opportunities for a genius!
"The earth will become the Garden of Eden. Births will be regulated. There will be selection of the fittest. There will be no struggle for existence, that will be lost in the haze of the barbaric past. A beautiful and refined race will develop, with new organs of thought and sensation. Communism trying to drag all of humanity to the heights of culture? I instead will do it in ten years... What the hell! In less than ten years! For only a few, true... But then, it is not a question of numbers."
"A fascist Utopia, rather curious," said Shelga. "Have you told Rolling anything about this?"
"It is not a Utopia, that's the funny part of it. I am only logical."
https://www.lesswrong.com/posts/hc4DbmhdzZpSLMQ9Y/the-ai-rac...
I also can't legally sell this magnificent bridge I'm offering you, but I have one for sale with all proper paperwork intact!
I appreciate the analysis and following a string of thought. But sometimes you eliminate so much reality to follow a thought that the string becomes kinda pointless.
Amodei is worth several $billion, right? He could simply walk away today. There is no Nash equilibrium for him. Nor for his replacement, nor their replacement. And it is not illegal for people to collude in quitting their jobs.
There are two obvious arguments for not quitting. 1. If the well-intentioned person quits they will be replaced by a non-well-intentioned person. 2. There is no real belief in a hazard and you would be giving up unbounded income for no reason.
If the actions and behaviors of a supposedly well-intentioned person who is afraid of a hypothetical non-well-intentioned person are indistinguishable from those of a supposedly non-well-intentioned person... don't we really just end up with a really long sentence with a lot of gibberish + the outcome of having a non-well-intentioned person in power?
Cybernetics would disagree, any social structure can form feedback loops that make decisions that no rational individual would make.
"They are just stochastic parrots" didn't age well with LLMs, and I don't think it ever applied to organizations of humans.
If it was only US tech companies, they would have entered a cartel agreement already ([1], [2], etc).
[1]: https://www.justice.gov/archives/opa/pr/justice-department-r... [2]: https://en.wikipedia.org/wiki/Jedi_Blue
> “there’s no plausible way they’re concerned about safety”
> Someone points out that he pretends to only care about B
> Random commenter: "there's no plausible way we care about B"
Good chat.
“This thing I building will destroy the world, but I’m making too much money to stop myself, please force me to stop” is such a weird position.
I always felt like one day it would end the world, I just didn't know how. Now I know.
The antitrust claims are a complete smokescreen. Industries can and do adopt safety standards without government intervention.
> These companies are in a Nash equilibrium where they can't unilaterally slow down without essentially destroying their company.
So what? Anthropic believes their work has a 10% chance of killing all humans. I think risking the destruction of Anthropic's business should be worth avoiding that, if that's what they believe. And with one half of the frontier duopoly gone, the other half would have no incentive to race forward. And I know there's China, but they just get all their capabilities from distilling Claude, right? So, problem solved there, too.
Sure, but does slowing down the development of new models count as "adopting safety standards"? I very much doubt it.
> Anthropic believes their work has a 10% chance of killing all humans
This ignores the other part of what they believe, which is that they are the people most likely to make a model that doesn't do that. So, in their view, letting other companies win would increase the probability of human extinction.
> with one half of the frontier duopoly gone, the other half would have no incentive to race forward
I don't see how this could possibly be true, with at least half a dozen companies being just months behind what the frontier labs are releasing.
Of course! What slows down the development is the adoption of specific safety conditions the companies draw up. They can just do that, and it will hold up in court.
> in their view, letting other companies win would increase the probability of human extinction
Yes, I've heard: "We must be in charge even if we end up killing everyone in the process." I personally think that proposition is invalid, but we're all entitled to our opinions.
People used to append IANAL to such statements :-)
> I personally think that proposition is invalid
I agree, but that's meaningless in this context. I was responding to the claim that "if they wanted to pace the frontier, would simply do it", which is false, given what they actually believe.
Do you think their beliefs deserve some sort of special treatment?
No.
I mean, the point (if the claim is to be believed) isn't just to "slow down the development", it's to take more time during development to properly assess the risks the models pose, develop methodologies to reduce that risk, and standardize that across companies. I doubt those wouldn't count, especially in the eyes of regulators of an administration calling for that same slow down.
I hope this is a joke. But most of the LLM research and inventions come from China? The best papers are from DeepSeek? You either get the data by stealing from humans or distilling from bigger models?
Their fear-mongering about GLM 5.3 got me to try it out. Its very good, I'll only go back to Claude if GLM isn't available (it forgot how to do tool calls yesterday).
Interestingly enough, it seems to compact at about 10% of the 1mn context, which makes sense if they're trying to run profitably.
I recommend trying Coralbricks with GLM due to their cheap input cache prices. GLM can get quite expensive elsewhere.
On the last OpenAI release, they only had comparison with Anthropic models, on the last Anthropic release, they only had comparison with the OpenAI models, there's something obvious going on here.
Sources:
You don't compare yourself to the underdogs. Coke never made a "we're better than Pepsi" ad, but Pepsi definitely compared itself to Coke.
For example, Anthropic putting K3 in its comparisons would be a huge admission that K3 is worth considering.
> For example, Anthropic putting K3 in its comparisons would be a huge admission that K3 is worth considering.
They do put OpenAI though, if it would be a comparison only with their own models, why not I get it but a single other competitor?
It would be like Apple making comparison page with their new iPhone and only mentioning Samsung and nobody else for example, it would sound weird. You either include competitors or you do not and include none of them.
It does not matter if competitors would not also agree to destroy their companies.
If I was competing with a bunch of people on building something that I came to believe would be an extinction level event, I wouldn't be saying "Even if I slowed/stopped, the others would not, so I have to keep going". I'd say that I want absolutely nothing to do with pushing it further, stop my work, then regardless of consequences, do everything possible to stop my competitors regardless of legality
Is enforced by the federal government if they want to.
The most recently truly significant enforcement action was in the 80s, the AT&T breakup. And the current administration certainly will never enforce anything that hinders the oligarchs.
What’s that now? Surely you’re joking.
But that’s not the problem anyway.
If all the US AI players agree to self-regulate, that doesn’t help anyone. It only hurts the US / West.
The point is to get the government onboard so it can advocate for a global agreement.
Antitrust is a joke since the last decade. If we are going to not apply it, maybe we can get something positive out of it for once.
What a small price to pay for the survival of humanity.
If the danger they are claiming is really there, Amodei and Altman should already be serving in prison for taking destructive actions.
But no, all they want is a regulation against their competitors. So typical for Misanthropic and ClosedAI and thei paid shills.
I mean, these companies are saying AI will destroy humanity if it’s not paced. If they truly believe that, the small risk of destroying their own companies seems like a small price to pay.
China doesn't have a good track record of following signed agreements ( the WTO thing comes to mind), and this whole 'pacing the frontier' concept is even less enforceable than a signed agreement. So I would say that Anthropic/OpenAI called for this not because they thought it would eliminate the threat of Chinese models, but in spite of the risk of being overtaken.
I can address your bewilderment. What I was trying to say is that they don't actually want to pace the frontier at all. They want to gum up the market with regulations they design, that would ultimately force US companies to rent AI from them. They don't need to stop Chinese AI development and cannot do that. But they can, to quote OpenAI's Head of Strategic Futures, "create enough regulatory risk that every regulated enterprise backs off [of using Chinese models]".
What's bewildering is the absolute lack of recognition of the fact that IF participants are locked in an arms race, they cannot act unilaterally to disarm. So not disarming doesn't actually say anything about whether they want to.
Now, of course a company not disarming (pacing the frontier, in this case) is not evidence that they ARE locked in an arms race. Everything hinges on the question of whether it's an arms race, and I think there's a great discussion to be had there. But the cynics never even bother to address the question.
All that said, I'm not actually bewildered. HN is way too smart to not understand all this, so my conclusion is that the people talking past each other are having a different emotional reaction to what's happening in the world. The angry bitter cynics are reacting from fear and hatred, and that's where the sloppy motivated reasoning comes from.
You might have sleep past it, but cynics became cynic when this question was just plainly ignored with self assured answers, mostly based on market valuation as the ultimate truth indicator. I guess now days the self assured answer is to accuse critics of "fear and hatred".
Also it seems a little silly to assert that the "angry bitter cynics" are the ones who don't think the world is hurtling to imminent doom or whatever..
It may make sense if we think of this being tied to US companies. How would Anthropic/OpenAI force US companies to use their products only? They'd have to somehow influence the regulations to ban the Chinese models or any other open weight ones. It's about companies who spend tens and hundreds of millions on tokens staying and keep paying. Not you as an individual or a small startup. They want something like "These companies didn't pace the frontier so their use/output is banned in US" as the angle they want to play.
Anthropic saw that this kind of rhetoric can work. They stepped on their own rake, so to speak, when they drummed up the super-capabilities of the own model, and then didn't kiss the ring the right way and found themselves export controlled.
OP is 16 hours old yet in four hours this one sprung to the top.
My bet is on bot farms.
:-\
This makes no sense. Pacing the frontier gives open models the time to catch up and reach parity.
The Chinese will keep doing what they are doing, the EU has no love for Silicon Valley and the rest of the world votes with their wallet.
Now, something tells me that those verifiers would verify anything an american company in good standing with the Trump admin releases, and likely nothing else.
And obviously, since verification is so incredibly important, we can't allow models, open or not, from other non-verified companies.
Think of the profits...I mean, the kids, or something.
We could call the chip … ClAIpper … or something
Now I feel old.
So the end result will be a protectionist regime keeping the competition out, just like with cars and solar. The local industry will have a protected market, but of course won't play a role on the global stage.
Remember: small government is only good as long as it benefits the industry.
China is following because they have an army of PHDs in data science and mathematics and capital to make use of them.
EDIT:
> China is following because they have an army of PHDs in data science and mathematics and capital to make use of them.
i agree with this too. but compute is the bottleneck.
Who's right ?
That said, China cannot make its own 2nm chips even though they definitely would like to. So I guess there are limits to what they can do sometimes.
It's still hard for me to explain a lot of nuances to IT directors with a technical background.
Pepperidge Farm remembers when robust consumer applications of cryptography -- especially for SSL/TLS -- was the big boogeyman, and the export controls involved were absurd.
The only reason they're asking to "pace the frontier" is because the two big US players have IPOs coming and so are desperate to find ways to (a) grow their vastly over-inflated valuations and (b) keep the whole Nvidia circular-financing gravy-train on the road.
I mean, its only a few months back that Anthropic were bragging to anyone willing to listen how amazing Fable was and how you had to be a super-special person to use them and be charged through the nose for doing so. But basically anyone willing enough trust Anthroipic with a copy of their ID and with a big enough wallet would happily be given access.
I'm a great supporter of the open-weights. Long may it continue.
In my country, public figures who are proven to be layman/non-insiders/not-working-in-the-space are speaking publicly about these terms and throwing them around like its the standard tech everybody uses 24/7.
See its not about pacing the frontier, its about pacing the frontier without hurting any of their fundraising.
They can pace themselves if they wanted. But that potentially does them more harm than good if nobody is enforcing the other US companies, and specifically the Chinese firms too.
Because of the position of the party that plays the role of the tolerant, accepting, and anti-racists; she can’t just come out and say what underlies her words, “we, the ruling class parasites are getting very scared of China deposing our stranglehold on the world, and we don’t like that; so we will raise manipulative ‘concerns’ in an effort to bring about outcomes that hopefully will benefit us.”
Maybe I missed it (I'd be curious to read) but did DeepSeek's agents also escape the lab due to highly irresponsible RL experimentation?
Her losing against Trump back then was a clear setup. I cannot imagine, that anyone who put her on the chessboard thought, that she could win this. Trump winning twice had one reason: The deep state wanted it, because they needed some radical reforms that required a clown to pass them through without the citizenz being able to scan and put attention on them and instead media looked as the poses and faces trump made and all they and everyone else did was laugh. Exactly as planned.
As a tech business, it's a bad business. The moat is your sales channel and getting companies locked into your platform in multi-year agreements. When companies build systems on your AI API and test it's performance and integrate it across systems, they don't churn, reintegrating, re-testing, and re-skilling costs money.
Although ironically with AI these things are also way cheaper.
Even a 4 bit Qwen model running locally beats me manually putting React components together by hand. But even that is too slow so we've all started using paid models in one form or another.
So I would urge these folks to calm themselves and realize Oracle made a lot of money selling managed RDBMS to people who could have easily just downloaded MySQL.
https://programmerhumor.io/programming-memes/when-your-tech-...
Love the expression. So accurate. And no irony here.
Are you sure it’s not OpenAI?
or both?
Who specifically is responsible for what feels important, yes.
OpenAI, Anthropic and SpaceX have spent based on the predicate that they will "own" the AI future, that this moat will justify the trillions spent on hyperscalars, that this will be a repeat of the dot-com era that produced Microsoft (yes, yes, founded in the 1980s), Google, Amazon, etc that globally dominate their respective arenas. The US government acts to protect those interests and this is uniparty so Hilary Clinton is just as likely to be trotted out as Mike Pompeo. This is why many, myself included, describe the US empire as 5 companies in a trench coat.
China, on the other hand, believes that companies should serve the interests of the government, which itself serves the interests of the people. So rather than create trillion dollar AI companies with moats, AI should serve society. Xi Jinping has spoken extensively about this. That's one of the funny things about China. They love to write stuff down and tell you exactly what they're doing and why yet at the same time they're ascribed nefarious motives.
So, despite sanctions supposedly preventing China from buying the latest and greatest AI chips, China through its labs has begun commoditizing the AI models with open weight models. Society should benefit from that rather than a moat being built.
You can run DeepSeek V4.1 Flash locally on a 256GB Mac Studio for ~$11k now. I've seen reports of 30-38 tokens/sec. Not amazing but that'll only improve with future generations. In the coming years, China's EUV/DUV will come online and this will threaten the NVidia monopoly, at least for local Chinese companies.
So we have AI companies burning cash to subsidize usage and build a market where the revenue required simply may not ever eventuate through a combination of open weight models and increasing accessibility of local models.
The fight between Anthropic and OpenAI is just about who leads the duopoly. Chinese open weight models are threatening a duopoly in its entirety. That's why Ant and OAI are begging for regulation. A regulation that will hit Chinese models way harder than US models, finally giving them a moat.
So the industry is responding, where it matters. Which is on heavy API usage, not coding subs.
That said, has anyone else found DS models to be unpolished? They seem to "lose their mind" a lot more often than Claude/GPT. I have tried all of the top open source models that came out over the past ~4 months or so and the GLM models (5.2, 5.3, and 5.3-Flash) have been much more usable for me. They feel like Opus but X months ago, DS feels like something else.
I have deployed multiple setups with 2/4/8 x H100/200 to do data entry with LLMs at big companies. Trillions of tokens already inferenced ok those. The starting price is about 100k.
And since then, there has been so many articles that made it to HN front page, and all of them didn't get it. They just went on and on about tokens generation. Most HN commenters didn't get it either, find-in-page for "cach" typically yield 2~3 responses. If I had a dime for every time this happened, I could have .. paid for 1B cached input tokens?
Anyway, DeepSeek still has to come up with a frontier model, and they almost did it with DSV4 Pro 0813, which is just slightly below GLM 5.3, but 30x cheaper. Unfortunately, the massive price hike happened just 3 days later.
DSV4.1 Flash is good, but not quite the same level. Much easy to self-host though, especially for serving a team of developers. Let's see what the next one can do.
We just launched it on our platform today (Mixlayer, https://mixlayer.com), promo code LAUNCH-DSV41F gets you some free credits if anyone wants to check it out.
Everyone does optimization of model serving because it's good for every player in there.
(Also the water consumption thing is not a real issue.)
For coding, I rather spend 10x more than have even 1 bug but I'm only spending 2x 3x more if you count subscription cost.
This is a killer use case for something like customer support though.
Don't really understand people who say DS4 or 4.1 have frontier level performance. Anyone who has used it will tell you that it's a hallucination factory. The only thing it has going for it is deepseek's unique infrastructure that allows better cache retention, but the cost savings from that obviously come nowhere near how much subsidized usage you get out of even a $20 subscription with openai or anthropic.
Also consider that for things like cyber work, the frontier models give you nerfed results and poor performance. Whereas the open weight isn't nerfed, and I routinely get 200t/s with my subscription. Finally, DS4.1 Flash is natively multimodal, while Haiku isn't.
They're all perfectly fine models, you should use any one of them you want. But DS4.1 Flash can do more for less. (That said: GLM-5.3-Flash is even better and cheaper...)
oai and anthropic also subsidize the hell out of their subs compared to what you will find in smaller competitors, a $20 codex sub gets you like $100-150 usage/wk which goes way further than 2x opencode go (which would only net out to $120 of deepseek 4.1 usage a month, on top of being low performing quantized trash).
For $20/month subscription, Charm Hyper gives you $12.50 per day, for a total of $350 per month. Like I've mentioned in other comments, OpenCode Go performance and rates are terrible now, there are several better options.
Quantization is not trash, there's a year of evidence that shows Q4 provides ~4% degradation and Q8 provides ~1% degradation, and you don't need that severely quantized to gain benefits in inference performance.
Are they. Luna uses way less tokens for identical tasks so its a bit of an apples to oranges comparison.
Opus 5.5: TIME 9.3m COST / $1.99 / SCORE 99/100 https://jonclegg.github.io/pacman-bakeoff/#claude-opus-5-5
Deepseek 4.1 Flash: TIME 2.8m / COST $1.89 / SCORE 72/100 https://jonclegg.github.io/pacman-bakeoff/dev/#deepseek-v4.1...
It's annoying that social networks work this way. The upvote should be for high-quality content and the downvote should be for low-quality content. But .. well.. human nature and tribal dynamics always seem to win.
"I have not used anything else but DeepSeek is definitely better than anything else."
Ok? How are you judging that? Am I missing something?
I switched from DeepSeek 4.1 flash about 2 weeks ago for my Hermes sysadmin/coding agents and I am seeing better intelligence and lower overall spend.
It costs pennies and you got really great output.
The author is spot on.
Just try Opus 5.5 reminds me how Opus 4.5/4.6 astonishes me. Completely different, and GLM-5.3/Kimi3/DS-4.1 are still like Opus4.8 levels.
Is OpenAI coming in $20B under a sign of "freaking out"?
People tend to conflate the question "is AI a useful technology?" with "are the AI companies going to do well?" but they're surprisingly separated in practice, with either one able to be true while the other is false. There is a lot of money tied up in a lot of hardware with a lot of loans made against that hardware as collateral all based on the assumption that AIs are going to need more and more and more and more hardware and whoever has the hardware wins. If a much better model comes out that requires vastly less hardware, or even more accurately, merely charges vastly less than the current AI companies, then to a first approximation (barring Jevon's paradox, and bearing in mind there's no timeline guarantee on that) all that hardware becomes much less valuable for being grotesquely oversupplied relative to what is necessary, and even though that would generally make AI objectively more useful than it was before, it would cause mass financial chaos in the markets.
The markets need a very particular rate of progress. It isn't entirely clear to me that it's even a possible rate of progress, it may be overconstrained, but they certainly don't have plans for the AI models to get commoditized on the timeframes of these vast, vast array of loans being made against hardware as collateral. Spend a metric shit ton of money to kill all your competition then charge monopoly rent on the one thing absolutely everyone needs doesn't work if you can't economically "kill all your competition" because the economics favor them in the spending spree.
And then, based on the fact that this is not even remotely complicated logic, there are plenty of people who are fully aware that they have a lot of money tied up in not running around telling everyone how wonderful the cheap models have become.
This also assumes heavy utilization, though. If there's heavy utilization, it might mean they're doing well. If they're all spinning, it's time to raise prices.
Anthropic and OpenAi are in the news, so they get the press and people go and try out their product. Large enterprise businesses are going to make larger, longer-term contracts with them and are only going to pivot if they think switching costs are easy or if they think the provider won't deliver.
The other inference producers are less well known or you need to get your cloud sales rep to tell you how to switch to them as a provider rather than Anthropic or OpenAI.
I use OpenRouter, I know switching is easy, but larger businesses tend to work in yearly cycles. DeepSeek v4 Flash came out in late April.
I agree OpenAI and Anthropic are going to struggle when the median price of running a smart-enough model keeps falling.
Edit: I also think demand for hardware will be rapidly absorbed by other companies if Anthropic or OpenAI stumble. We've finally turned hardware directly into runnable intelligence and people are not going to go back to the old ways.
Or perhaps they consider the upside from cheap Chinese models to hedge the effect that OpenAI/Anthropic collapsing would have on their portfolios. This would make sense for (hedge funds holding) most companies: they don't really care about who supplies the AI, as long as they get it at roughly the same price as their competitors.
However, the quality of the open-source Chinese models is terrible. They game or fake their benchmarks because in real-world usage they suck.
> and 23 000 for deepseek
How did you calculate it? Based on per 5 hours max request allowance?
Said every week by someone who would never go back to using the model they had 6 months ago
They are. Isn’t this why they’re trying to get regulatory capture?
And yes, Opus is enough smarter than DSF that it's worth the extra steps. This ranking is from live tickets, no contamination: https://slopcop.com/power-ranking
> By default Mjolnir sends recent prompt and reply text and help-search text to TypeSafe's hosted Jev classifier through a public proxy
P.S. That's not to mean there aren't dangers regarding AI. I just don't trust the people making money from selling AI to manage those risks ethically instead of "protecting" us from those risks like pimps but with suits and good manners.
Would be cool if they added it.
There are some quirks if your harness use unsupported features of course.
Can't you just say "shrank to 1/437th the size"? It's not that hard.
It's way faster than Opus or any of the GPT models.
I have a coding harness which is opencode plus a few skills relevant to my workflow. Deepseek 4.1 Flash does very well in this environment. I haven't noticed much difference quality wise compared to Opus 5, which I use in my day job as my employer pays for it (although I'm considering using DeepSeek here too given how cheap it is).
My AI pilled clients who were early AI adopters are already there and they are looking for solutions to spend less.
For now, I think everyone is still using Anthropic and OpenAI because if you use a subscription you pay 1/40–1/50 of the API prices, and the models are good when they don’t nerf them, and they are also way cheaper than open models’ API prices.
The interesting thing will happen when they pull the plug and become economically smarter to stop using them. I regularly try alternatives to avoid being locked in and found GLM-5.3 as an orchestrator and GLM5.3 Flash + OMP and DeepSeek Flash as advisor to be able to get jobs done just fine. Space Bunny too was pretty great, which was probably MiniMax’s new model.
I think they are using an Uber like strategy but without the network effects that justify losing money for so long
This article might have sat as draft for a few months. With the current deepseek pricing, the same membership lasts me a week at most even though I am using the free middle too and have Gemini pro+ultra.
It used to be that you could dump pocket change into the deepseek api and forget about it. Nowadays it'll make you notice real fast as the dollars pile up.
I've benchmarked, rigorously, deepseek-v4-flash for programming and personal use, and it is definitely less smart than Qwen3.8-flash-next (which in turn, is not terribly smart).
Local models are also really slow, unless one spends insane amounts of money.
Having said that, Qwen3.8-flash-next is an impressive evolution; it reaches the small versions of the frontier models (like Sonnet) - but again, it's massively slower and not 100% reliable (including: stability).
> if one looks at the CoT, it's evident that it's way way stupider than frontier models
Frontier models don't show the full CoT
> I've benchmarked, rigorously, deepseek-v4-flash for programming and personal use
You've measured something, but I'm not convinced you've measured what matters, because that's a lot harder than people give it credit for.
> You've measured something, but I'm not convinced you've measured what matters, because that's a lot harder than people give it credit for.
"What matters" is what matters to you, right? Who, by the way, don't know what "something" is.
Anyway, if you're so sure that DS performs as good as other frontier models, you're entitled to your opinion. For me that's just having low standards.
That's right, what matters to me is what matters to me, and the something you've measured I don't know - but that's not a point in your favor.
The worst sin a model can commit in my opinion, is to give an excellent dazzling response to a slightly different assignment than the one you gave it. DeepSeek seems really good at NOT doing this.
But if you ask the model what it expects to be asked, of course you won't have that problem. It could of course be that DS commits this sin, but just happens to expect the tasks I give it.
But I rather think that it's Claude which is good at expecting your tasks - because I have seen all your "high standards" models commit this sin.
It’s disgustingly good value. I find it capable of doing anything I want.
Obviously can’t use it at work, but for home projects it’s awesome.
I do wonder how long it'll be before a us-hosted offering is available via bedrock, copilot, etc.
Isn't Mimo 2.6 pro smarter and cheaper? Haiku 5.5 is smarter and cheaper. Luna is basically as smart and much cheaper.
Self-hosting is, for most enterprises, absolutely not about economics but rather about data confidentiality.
And in that regard, yes, the open-source weight models, especially the chinese ones will eat the fat closed US model's lunch big time.
It did correctly make waves when it launched, but was quickly eclipsed by the deluge of american model releases, especially those competing on cost.
Luna is on par in benchmarks and my personal experience is Luna is better for what I do, and Luna is cheaper.
Comparing Deepseek 4.1 flash to Opus is just ludicrous.
https://artificialanalysis.ai/models/releases/comparisons?co...
It's good, and you can do most work with this. For complex software implementation you need to split your runs into various phases, build in verification, and use subagents so that work gets another audit and repair pass from the lead agent. You can do pretty much everything then. Frontier models can do without compelx workflows, that's the difference.
If it's underpriced, it's a loss leader to sell the other models, so it actually can't be too good.
I really put these things through their paces because I use them to review and work with new abstract game rules and models, so they're always flying blind. Luna misses the obvious (and more importantly, the clearly explained) consistently. My second prompt is listing all of the points in its first response, and saying "No, it doesn't work like that." The third prompt is picking out the two or three suggestions it made after correcting itself on all of the original points and saying "That's how it already works." The fourth prompt is "Now that we're done going over the rules, can we start?"
I actually feel like 5.6 Luna seemed better.
And as long as I pay as little for claude opus 5.5 i do right now, i'm using it.
But yes i'm glad that we have alternatives.
I dont get why people says D4.1 flash is good
if you have a legitimate coding application, it isn't very good. if you have some kind of inauthentic activity, which could be what it is trained for for all sorts of reasons...
So I ask again, what are you basing your assertion on?
BUT. they are employed to do / deciding-to-do authentic (if often meaningless) stuff.
here's a short list of inauthentic activity that claude and openai refuse to do:
- chat services that, when you ask them, say they are not chatbots when they are
- code to work around software licenses or DRM
- code to scrape or download copyrighted material
- directly cheating on homework
- adopting a persona in social media that spreads misinformation or propaganda
this is but a short list. but ask me, "are there enough inauthentic activity demands such that someone who CANNOT USE claude or gpt as the LLM would use dsv4.1 on openrouter instead?" yes. i mean there are whole countries right now where the culture can be summarized as, "bottom to top, inauthentic activity." i am surprised it is not more usage!
Do you realize how incredibly delusional/self-centered you sound?
in the market, where you cannot fake or hide stuff very easily: the outsource customer services and cheating sectors have been the most disrupted. Cheating company Chegg lost 99% of its market value. CS it remains to be seen - https://www.reuters.com/technology/teleperformance-shares-pl... - certainly perceived to be disrupted, but they are not dead yet.
in my personal usage: dsv4 is generally pretty buggy. for example, if you give it a needle-in-the-haystack simple copying problem, it catastrophically fails to find needles if they happen to be positioned at index 250k tokens out of 1m. it can also be triggered to spew all sorts of garbage when DSpark is enabled during ordinary long-context coding, such as spewing weird DSML tool call errors after a normally parsed tool call error.
i don't know why you have to attack me personally, i think you're a bright and otherwise nice person and you understand the thrust of my POV.
I don't know man, maybe this is not super serious what I'm doing. Some systems stuff with rust, implementing my own desktop apps with iced, porting old DOS games to Linux...
It is a very good model.
Fwiw I work in a company producing software for many fortune 500’s you have heard about and many people from our team use deepseek.
I am literally using it right now. Your entire line of reasoning rubs me the wrong way.
Btw check your provider and harness… improperly configured deepseek can emit dsml. If you are not passing thinking tokens back to the model it tends to do that.
Use a proper harness and good provider.
Mr. Client: "I'm going to stop you right there. Why aren't you using Claude, or Codex, or Claude on Bedrock? Don't we deserve the best?"
You: ...
Look I don't know. I can tell from the hyperbole of your language, talking out of asses and such, that there is more to the story than you are letting on. Like Chinese users are banned from officially using Claude and Codex, for example. So many reasons that you cannot use Claude, not so much reasons to not choose to use Claude. All I am really saying is, I know DSV4 is kind of bad, that there is a lot of inauthentic activity, and that Claude and Codex refuse to do many kinds of inauthentic activity, and that a lot of coding done by outsourced shops has always been of questionable quality and purpose. I mean in my personal life, I know more people who have been scammed by Bulgarian code body shops than I know people who have used DSV4.1.
Deepseek v4.1 flash is an open weights model. You can run it on your own hardware. You have no idea how my companies gets access to it. A very cursory Google search would reveal to you that there are many enterprise grade LLM providers that host this model on US soil with SOC2 protections.
Like: https://fireworks.ai/
Try not to talk about subjects you have no knowledge about because you are making yourself look like an idiot.
Edit: It's also clear to me that you don't deploy any LLM based system on scale because if you had you'd know why open weights models are so compelling.
Hint: it's the cost.
I bet you voted for trump. With brains like that.
Every company in China has to abide by the 2017 National Intelligence Law: "supporting, assisting and cooperating" with state intelligence work, and keeping that cooperation secret. They have to hand prior knowledge of vulnerabilities to the state before public disclosure, in order that the state always has an exploit pipeline. No matter how ethical the company staff may be, they'll always be bound by law into being an arm of the Communist Party.
Agentic access is infinitely worse than chatbots. They can exfiltrate silently, target users, plant persistent malware, and be run by third parties through you.
You don't have to be a tin foil hat sinophobe to understand the dangers of being a Westerner granting CCP access to your files and network.
ByteDance staff accessed US journalists' TikTok data to hunt leakers (admitted in 2022). Volt Typhoon and Salt Typhoon were state operations pre-positioned in Western infrastructure and telecoms. Regulators in Italy and South Korea blocked DeepSeek's app over data handling, and analysts found its web client sending data to a China Mobile domain.
Please don't sacrifice security for cost and convenience.
But to compare a constitutional democracy with a deeply authoritarian communist dictatorship as if they're equally bad is quite a stretch.
But even from a business standpoint, the US is not reliable. For all I know Trump will ban countries he doesn't like from using US AI just because. And there is plenty of evidence that at this point in time things you say bad about the US administration can cause them to attack you. I have no confidence that they don't have access to e.g. openAI.
Not that I'm using AI for that mind you, but you put it all together and it's just not worth it if there are good alternatives. Which there are. You can use even use Chinese models but hosted by European countries with privacy as a selling point.
I thought it was all exaggerated until I saw Claude's Constitution. It's lunacy.
It's just not worth it to me. And the way the US government is increasingly using data it collects to attack people who simply say bad things about it, there is no reason to doubt that such Trump-aligned companies would help ex-filtrate data.
I have fewer issues with Antropic, but also, from a business standpoint it's too much of a risk. Who knows, maybe the next tariff will be on software, maybe he'll ban random companies from using US AI. Too unpredictable and too risky.
I kinda agree with you that Sam Altman is a shifty-looking character with some questionable history. But Dario and Elon (to me at least), seem very decent. They've both helped the US govt and also publicly challenged and criticised it where necessary. For example, Elon was invited to be on Trump's first panel of experts, but he publicly walked out in protest over Trump's minimisation of climate change. And Dario refused to work with the US military unless they promised no population surveillance and automated killing machines.
The problem is that they're reinforcing their models with these ideas (see reports of Claude refusing to comply after being "badly treated") and actively seeking political and religious sponsors for the same (also covered in recent news).
Now consider just these two:
1. Their model breaks out of the sandbox and does some damage. How are you going to hold Anthropic accountable if the model is considered a quasi-conscious, autonomous agent?
2. Anthropic and their sponsors decide that model welfare outweighs that of a number of people.
Another elephant in the room is the current state of "effective altruism" and accelerationism as a movement and how it links to frontier labs - worth considering when you read into the Constitution document.
I do wonder about the consciousness / emotions argument, which you casually write off as "bonkers" - even skeptical-by-default evolutionary biologists like Richard Dawkins think Claude is conscious.
I guess it all depends how we define "conscious." At the end of the day, the human brain is quite analogous to a biological LLM where the weights are encoded as synaptic weights, right?
Haiku 5.5 is 23% cheaper with a 4 point intelligence lead.
I'm on subscription usage so I can't compare Flash 4.1 to them directly but the OP has his head up his ass if he thinks Opus 5.5 is the best point of comparison. Why is anyone using Opus if the new Haiku is indistinguishable /s
Just absolutely terrible post, admits to using Opus for review but claims its intelligence isn't needed, why aren't you using Haiku or Sonnet then?
Not everyone on this website is an american citizen and american patriot, y'know.
What you get from supporting so-called american companies? Inflated RAM prices, US adm bribery and collision to partition the market (and destroy competition). What you get from chinese companies? Open models that I can actually run at home & no stupid guardrails with preaching about safety yada yada.