DeepSeek-v4.1-Flash-1017
huggingface.co
huggingface.co
First, it's the tok/s and TTFT from providers. Deepseek blazes through work - at quite good quality - and you don't find yourself waiting for your agents to finish.
Second is the openness. Being able to see the CoT and not being randomly blocked for cyber review. You're never wondering if it's stuck or if it will randomly fail. Combined with the great cost value.. My next subscription will probably be Z.ai
Long-term & software engineering fronts: I hear occasional railing against “US hegemony”. Anyone concerned that if a new tech gives rise to a different country, they’ll become the source of ire instead? (This isn’t my field, at all.)
So I go with feeling, and that feeling says that China is worse. The feeling is partially supported by the fact that China is softly backing Russia in its attack against the west.
Trump's USA is obviously completely horrible, but he is also in large part a product of Russian influence, which again is part of the larger block in which China resides.
And China is not really supporting Russia, they make money off the war and want to keep it going as long as possible so both Russia and Ukraine become weak and more open to their influence. Is it evil? Absolutely, but you can't compare them to USA when Trump is actively doing regime change operations, deporting its own citizens and putting sanctions on the ICC.
Chinese communists want to stay in power at all costs but America wants to expand its power into the whole world. It's not the same level of danger at all.
Trump’s a smelly orange shit but the US at least pretends to have data privacy protections and we can’t legally compel companies to have back doors… yet. I think. It’s very hard to keep up with all the nonsense these days.
In a post-Snowden world where the existence of PRISM is no longer a secret, I'm not sure how folks can keep stating this with a straight face
note that the volume of EU and US support to Ukraine is obviously calibrated to the same goal.
Yes, yes they are.
https://en.wikipedia.org/wiki/China_and_the_Russo-Ukrainian_...
I usually default to ds, and if it gets stuck try mimo but usually just for fire and forget tasks. After a bit I come back and see what it did.
If so OpenCode Go looks good and includes DeepSeek models (yet to try myself).
DeepSeek: ZDR agreement is renewed monthly. The current agreement is valid through October 31, 2026.
https://opencode.ai/go
DeepSeek V4.1 Flash / V4 Pro: Peak hours are 01:00-04:00 and 06:00-10:00 UTC, Monday through Friday; all other hours, including weekends, are Off-Peak. Learn more.
https://opencode.ai/v2/docs/console/go/#usage-limits
Some people would say that you don't need this at all with the frontier llms. But when extending existing software or writing software that fits into an existing ecosystem, I find I still need to offer a lot of guidance. And it's better that the context window isn't flooded with code that was reverted after I looked at it.
Hit the nail on the head. Development methodologies pivoting at the whim of frontier models have been one of the things that have been worrying me as of late.
In a way, that’s the situation today. You can pay an experienced developer $$$$ to do it right, or you can outsource it and pay $$ but pay extra for more layers of review, have lots of unfixed bugs and more tech debt.
You are proposing that instead we pay the frontier model to clean it up. If it can, I expect it to cost somewhere in the neighborhood of an experienced dev because that’s the alternative. That’s the way the frontier AI companies will extracting the value of the work from you.
But there's two ways this could be true: Frontier models costs rise, or (more likely) experience dev costs fall
That's only true if a single company owns the frontier, otherwise the alternative is "a rival frontier model" and the cost will be the marginal price of the compute + amortized training costs.
You're car will have spun off the road and you'll find it harder and harder to control it and keep it on the road because you have less and less visibility into the vehicle, nor will you be able to clearly see out your windscreen to use the car and road metaphor. You'll be driving blind eventually, unless your working on some simple closed toy problems and/or other small systems.
Building large systems means understanding the system. Without understanding the system, you lose control of it.
The LLM can probably help you understand the system to gain/regain some control of it again, but now you're becoming a system specialist rather than someone who's repeating, "fix it" and "make it good" or whatever.
Self-trained people (like me) are particularly susceptible to it and we're heavily represented in the software world. A lot of us either recognise that (or are coached) and invest the time learning the benefits, and limitations of, software architecture. And, importantly, keep reflecting, learning and updating our practise.
I keep going back to the YOLO mode every other year and try out new languages to see if we still need the structure and abstractions. Then VMs came along and all I can say is they're both even more important and now we have an army of virtual "ADHD idiot juniors developers" at our finger tips it's even easier to tell the good and bad abstractions from each other.
They already did the bare minimum allocation to both despite engineering screaming at them.
I highly doubt executives are going to accept slower release and sale cycles in order to provide better support.
If anything, I expect this trend to accelerate. I’m imagining the opposite, a plethora of AI slop with increasingly burned-out engineers.
Sales are out of their control.
If there’s a market for quality and you don’t have it, sales are going down no matter what the PHB wants.
If there’s not, watch out for the vibe-coded competitor.
As long as the API is private or not dependent on by automated systems and you have good test coverage, you can get away with bad code IMO since you can always refactor. If it looks stupid but it works (and can be refactored/fixed/improved later if needed) it aint stupid.
To reach a more optimal solution, significant human steering is reqiured for every tiny decision. It’s the same as how early models needed hand holding on the basics, playing out again in the finer details. Human experts need to inject “eureka moments” from the outside.
And no feedback loop that I’ve tried has been able to conjure an understanding of these finer details that, as far as I can tell, just aren’t represented in the weights.
Modeling the basics of software engineering was easy, since things like writing code that passes some tests is easy to RL. But for anything not easily RL-able, we’ve basically ran out of human generated tokens to distill from.
Without some breakthrough in generalization, I remain unconvinced that we’ll be able to model the finer details of software engineering by looping the existing training corpus back into itself. Garbage in, garbage out.
DS is miles better than GLM in this regard. I wanted to add that as PC is planning to go to GLM next.
It would just loop on things and go down rabbit holes more. DS flash has been so consistently good I've kinda settled on it for 90% of my work.
I still use adversarial review for bigger changes where I want to make sure any issues are caught early, but I genuinely feel as tho if no new models ever get made I'd be fine with just using DS forever.
I like giving precise instructions,”update function xx to support input yy, add tests”. There is no reason for 10 turns and 5 minutes of waiting.
Keeps repeating things I settled as accepted or rejected. But what really gets on my nerves? The language it uses – that aweful, useless typical LLM languages! No AGENTS.md instruction, no per session instruction, no just two-prompts-prior instruction seems to work, hell sometimes instruction regarding that in same prompt doesn't work. GOOD/BAD example along with instruction don't work. That wastes time and zones you out! Not to mention the fatigue of to and fro. DS has been working much better. Hell Gemini 3.1 Pro works so much better than GLM. I don't have extended experience with OpenAI and Anthromic models but the free claide.ai works quite well and better than GLM 5.3 (not even flash). Albeit I must say I rarely use claude.ai for coding, but technical tasks? All the time!
GLM's best response: "No excuse. My fault. You exactly asked me not to do exactly that and so did the AGENTS.md. I failed". Love it. Except that if I press I will get that response every 5 prompts.
Fwiw, to elaborate on mine, a small fast 8B-ish model should be able to few-shot simple text style transfer, such as converting one model's complex careful style to another model's punchy compact style. Perhaps also a 2B fine tune. So you might tweak your harness to transfer and present a preferred style.
DS just handles ambiguity and my bursts of detail with much more grace. I'm only using "max effort" and the CoT is reasonable compared to GLM.
This really depends on the provider, it's a very fast model with ours
yes!So it's not just me. In terms of how intelligent they want to be OpenAI is at the top, then Claude. And actually when I was playing with Sol it meant that many tasks it would just try to do something else because it thought I don't know what I want. Even trying to nudge it back in the right direction wouldn't work.
I guess the models just have the cultures of their creatures.
I have a theory that mess in an LLM-generated codebase sort of grows exponentially. Each hack or sloppy abstraction chips away at the coherence of the project as a whole and makes the next feature harder to do. But perhaps more importantly, it demonstrates to the LLM, which is a probabilistic machine despite its impressive capabilities, that quality of the code is low, and it generates more low-quality hacks.
And eventually you reach knots upon knots that you have to untangle yourself because the LLM becomes too confused by the code it wrote itself. As models get better, the problem seems to be getting better. I'm needing to do this more and more rarely, and the tradeoff is still clearly worth it.
But I've seen Fable 5.1 absolutely lobotomized by someone who fills each chat context with half a million tokens of insipid garbage, and I don't see why this couldn't happen when coding as well. I'm not sure a solution to this general problem that LLMs can simulate idiots really well will be fixed any time soon.
Adding unit tests, increasing modularity, reducing duplication, and the like are all things you can tell the LLM to do explicitly. And it is happy to do it.
And you can have coding standards as part of your design criteria. Set and document these early in the project and audit the code at least enough to see they are being enforced.
Agents know these principles already and they are frequently the problem themselves.
Too strongly-insisting on eg DRY can lead to tortured abstractions shoehorning too much together. Agents need to be able to distinguish between incidentally similar code vs conceptually redundant code.
Excessive modularity, eg. militant single-responsibility principle can lead to abstraction hell that is impossible to reason about at once.
Unit tests are obviously good. Coding agents write a copious amounts of them, often baking you into poorly-chosen designs made without your knowledge.
(I truly despise the code Fable generates and IMO it has fewer IQ points than Astra so it's not really in my toolbox these days.)
It's a bummer that American open-source is so bad though.
It reduces the speed and tends to overthink sometimes which is really annoying. For programming, 4.1/high is the bread and butter in my daily work.
I feel like I’m getting good value out of my subscription and they have the cheapest GLM-5.3 API rates I could find in Europe. I moved to them recently from Mistral for the better prices.
They do have a slightly different daily inference capacity allowance, but it’s actually not too bad once used to it. I was forced to up my game and figure out what GLM-5.3 Flash and Deepseek-4.1 Flash could actually do for me, rather than just throwing everything at GLM-5.3. Quite happily chugging along with a nice balance across them now.
I am hoping Melious make the new DS 4.1 Flash 1017 available soonest.
Plus only one country has threatened to annex mine and it wasn't China.
The economic sovereignty of any nation, but especially smaller ones like canada and denmark, is harmed by sending our money out of the country when there are options available in country.
I respectfully disagree. I subscribe to the comparative advantage economic principle, so I'm fine with international trade.
The 'clean power' would factor in a well, but I haven't really looked in to it. I know China is installing a ton of renewable and has a carbon market while Canada's building a new pipeline and building Alberta data centres running on natural gas, so I'm not willing to take on faith that switching is cleaner.
Also, re: power. I said canada has lots of cheap power, clean was your word.
The unfortunate truth behind at least the solar PV boom in China is that it's driven in large part by the exploitation of Uyghurs in the Xinjiang region. One of the reasons they can sustain the pace they're going at is because while nowadays PV modules exports increasingly gravitate towards offshore fabs (Vietnam, Malaysia), domestically all the biggest PV players make full use of modern slavery within Xinjiang's re-education camps.
more on the topic: https://www.shu.ac.uk/-/media/home/research/helena-kennedy-c...
It might be a minor nitpick, but that part is just not how the macroeconomic principle works. Abundance of a good/service within a country is irrelevant to the concept of a comparative advantage - this is counter-intuitive but but that was David Ricardo's entire point. It's not about whether production is more efficient domestically, it's whether comparative allocation of resources between multiple countries is more efficient by specialization within the different goods.
Even if you can produce "good 1" cheaper in Country A vs Country B, if Country A can instead produce "good 2" that Country B cannot, it's better for Country A to produce "good 2" domestically and import "good 1".
Now, I still agree with the original notion. It's unnecessarily risky to use services from "democratically-challenged" nations (especially imperialist ones), when safer options are available. But there's no need to twist macro 101 concepts to justify that view. Modern trade policy takes more into account than basic market efficiency.
As someone who's in the market for Canadian inference (data sovereignty reasons), the pickings are slim for DSF 4.1 man.
(But I'd concede that in the field of genocide China doesn't come up as bad)
[0] https://openrouter.ai/deepseek/deepseek-v4.1-flash
(The badges next to companies indicate their privacy terms, i.e. green/yellow/red)
The "green" badges explicitly say the opposite:
> "Private: this provider does not train on prompts and does not retain prompt data."
I.e. for the example of Fireworks, this is the precise language they use to describe ZDR:
> "More technically: prompt and generation data exist only in volatile memory for the duration of the request. If prompt caching is active, some prompt data (and associated KV caches) can be stored in volatile memory for several minutes. In either case, prompt and generation data are not logged into any persistent storage."
https://docs.fireworks.ai/guides/security_compliance/data_ha...
Without getting too much into politics; over the past several decades, one country is going around starting wars, regime changes and other interventionism, or initiating economic warfare (Huawei sanctions because they were getting too competitive to US companies; GPU restrictions; tariffs; etc), and that's not one administration: it's US policy over the past few decades.
People like to bring up the Uyghurs immediately, but never really have the same smoke for the US. Also, do we include the prison population in human rights? Do we include access to healthcare? What about systems perpetuating poverty like lack of access to public transportation?
To pre-emptively be clear on my stance or potential bias, I stand by no country in the world. I was born in Iran, and my parents left when I was young to the US because they believed in the American Dream. The irony being that the dominos of why they left were initiated by the US and UK. And long before Trump was elected, but certainly immediately afterwards, I decided to leave the US once I had the means. (Also because of the lack of public transit, the $100k medical bills after an accident, being punched by a homeless person, and having my possessions stolen repeatedly and the cops giving me a :shrug:). I moved to Japan, where I experienced blatant racism for the first time. I moved to Hong Kong (and tbh no real complaints so far), but I am technically subject to the freedom of speech constraints on China.
I have no allegiance to any country or specific people. My allegiance is to all living beings.
And I don't care if they're reputable, I will look at the methodology and make my own conclusion either way. Appeals to authority are not a substitute for critical thinking.
If they actually said something, it would be much less effective.
> We're not talking about a few % difference here.
How may freedom percent is it again? Do you lose 1% or 20% for failing to allow foreign ownership of utilities? How many percent do you gain for stopping Russian misinformation?
I'm not going to discuss it further if you don't have anything substantial to bring to the table.
Seriously - appeal to authority is a fallacy, unless it's an appeal to an authority that I happen to agree with? It's hard to take you seriously.
Right now, you're basically asking people to trust Human Rights Watch because you trust them. But if people don't trust you, why would they trust your word on what sources are legitimized?
No lessons to take from the US.
PS. https://en.wikipedia.org/wiki/Foreign_interventions_by_the_U...
I know the US thinks only its citizens are human, so "The United States has been involved in hundreds of interventions in foreign countries throughout its history, engaging in nearly 500 military interventions between 1776 and 2026, with half of these operations occurring since 1950 and over 25% occurring in the post-Cold War period” is not probably part of the reputable storytelling
edit: wording
Yes, people have put thought into this. This is why I trust institutions and not random posters on the internet.
Just look at Russia - it's score is highest in the 90s - the most corrupt and destitute time in its modern history, also happens to be when it was deemed somewhat friendly to US and friends.
It's kinda like meeting someone on the street who's protesting about animal cruelty in slaughterhouses, slyly handing them a chart showing them the nutrition facts of beef, and then walking off without another word as though you've settled your case.
Like, I get the point you're trying to make, but I hope you're aware it's a non-sequitur in context?
Your hope should have been that amelius was not aware that it's a non-sequitur, because that is correctable. But then they kept posting and removed all doubt.
https://www.prisonstudies.org/world-prison-data/highest-lowe...
https://www.theguardian.com/us-news/2022/sep/27/slavery-loop...
In Arkansas, agricultural work accounted for 17% of all prison labor assignments in 2021. https://www.prisonlegalnews.org/media/publications/Captive_L...
17% of prison assignments might be 3 assignments for all I know.
I found a number in your document: "...1.2 million people incarcerated in state and federal prisons, over 65 percent work."
That's 780,000.
And there's 163 million working folks in the USA.
Prisoners power .4% of the economy.
Your whole argument about the percentage overall economy is nonsensical because most of the US economy is not actual productive economy. It's ephemeral stuff like service industry, entertainment, and software development. Obviously, you're not going to force prisoners to go on Joe Rogan podcasts. But when it comes actually producing things, 17% of the industry being done by slave labor is obviously very significant.
> oh please, the US economy literally runs on slavery
Also
> 17% of the industry being done by slave labor is obviously very significant.
That's not what the stat says. It says 17% of the prison labor did that job, not that 17% of the labor for that job was prisoners.
Here's the quote for ya:
"...agricultural work constitutes a greater percentage of work assignments, such as in Arkansas, where 17 percent of job assignments for incarcerated people were in agricultural work in 2021."
But tell me again how I'm nonsensical.
> Have you ever wondered why Manus founders gave up billions of dollars from Meta and “voluntarily” moved back to Beijing?
This country wouldn't be such a shithole if we were able to bring the wealthy to heel. I suspect most people on earth see China as the more competently governed country. Understandably, this forum tends to select for people who view private investment positively, but this is clearly insufficient by itself to produce functional societies.
boy oh boy, if you think the US doesn't do anything in the shadows. The last 100 years of world geopolictical history would beg to disagree
Assange, Snowden, Manning, just three of many and the massive things that came from that? And god knows what Musk & friends are up to.
Talk about being completely brainwashed by the US narrative and oblivious of recent history.
Only thing is they aren't clear on the SOC2 certification. Am I being naive?
Thank you. I've been shouting this very obvious thing from rooftops
I work in silicon valley and I feel everyone around me is suffering from China-BAD psychosis and dismiss Chinese releases
Wait wut? You do realize that they are two different companies, right?
Just to test, will GLM – hosted by an infra provider other than Z.AI – also stop answering if I asked controversial questions from the pov of its country of origin? Or third party infra providers can perform some unhinging on the model before hosting? Or actually the blocking happens even before the question reaches the LLMs in that country?
(Note: my paid LLM access at this point are only via DS and GLM subs - 90-95% usage; and a free Gemini Pro access. I hope that adds some cred that I am not trying to take potshots at the their country of origin).
It is very capable for a lot of tasks, not only coding like the other open models.
It doesn’t have to be better, it just had to be nearly equivalent. I’ve been doing a lot of vulnerability work lately (trying to get ahead of the bots finding exploits in our code) and the frontier models feel like a chaperone at a church dance: get too close and they show up with the ruler and enforce the safety margin. Using deepseek and glm and qwen, I’ve gotten nearly equivalent results without the constraints. I still use frontier models for things but outside of a company environment, I’m happy to spend 1/10 the money, get the same results, and not have try and trick the AI into working.
American folks really have a distorted sense of reality and few have their own mind, they listen to influencer noise and once self thinkers pick up the quality of things like DS, they will echo the statements.
Dont assume that is how your model decides...
https://arxiv.org/abs/2305.04388
I can't tell if this is a really human thing to do, or if it's just a sycophantic thing to do when your master likes a good story.
I love COT and gives me a good idea about the thought process. Sure it doesn’t work always
Both OpenAI and Anthropic hides so much of that, that you don't really know if it misunderstood something or you got something wrong, until all the turns are complete or you directly see tool calls that implicitly tells you something is wrong.
You don't need to read any papers to see this effect concretely when developing together with LLMs.
It's only for long running/very difficult cases that the differences are here, and they are small.
Wait... you just spent 3 paragraphs singing the praises of DS... and then you say you're going to subscribe to the one that makes GLM. ?
(I do tend to agree with you, though I tend to favor GLM-5.3-flash over DS 4.1 flash.)
debugging reasoning traces more effectively seems useful to find prompt issues writing custom harnesses even if it runs on stronger model later
The Fable is the worst offender, and it is not purely Anthropic's fault.
Anthropic, whether Opus 5.5 or Fable, frequently will review for security, and the classifiers fire mid-way while it's fixing something. Authentication and credential related code, including implementation, also trigger safeguards.
Earlier today, I hit what appears to be a sticky "reasoning_extraction" block when attempting to reproduce a LLM paper at small scale, and I asked it ~"tell me more about the idea [X] you considered but dropped". I'm not sure how it works or how long it lasts, but Opus 5.5 has refused to do technical LLM work for me now, even in completely new sessions.
Is DeepSeek: DeepSeek v4.1 Flash 1017 any better?
please don't subject yourself to this. z.ai subscriptions are not a good value. just pay API rates, or use a subscription like opencode go.
No, I don't care about license provenance for all the components of a 30 line script that i'm using in my house, for myself to sync up some folders. No, i'm not going to find and archive the licenses for all the previous projects leading to this for future legal disclosure.
Yes, I own the DVD for the movie i'm transcoding. No I don't want to talk about the legality of rebroadcast.
Deepseek (and most chinese models as a whole) don't care . oAI and Anthropic models want to make it point #1.
... which is extra hilarious knowing the laws broken to create the damn models in the first place.
there's a literal meme about this floating around the harness/AI chats for the past few months[0]
[0]: https://i.ibb.co/SwH4Sgwq/796779070-28483090404651613-920066...
I always find DS or Qwen to be very spot on on audit changes or plans for just a few cents.
DeepSeek feels like an excellent sparring partner. The others feel like mind readers.
I don't have to worry about any complex context engineering / multi agent stuff if I am using DeepSeek because the sequential stuff is fast enough. I don't have to bother about touching a million context length because its cheap enough.
Where it seems to lag behind is 3d, computer use, motion graphics, design but the initial demos seem like the vision model has improved substantially.
I had a bad experience with MiMo from Xiaomi. I had it trigger unsafe content blocks while fixing visual bugs in a completely vanilla space game it just wrote from scratch in the same session. It also got triggered for changes in some other benign greenfield app.
Also z.ai doesn't offer Deepseek models.
And I am not sure why you are so worked up over it -- even meta-commentary wise, you have been shitting on everyone in this thread and your comments are in general very inflammatory. Stop that.
1. They, and many other experts, have been saying the same thing since well before the current paradigm existed. 2. There is nothing about regulation that is required to give them an advantage; it’s a fallacy to suggest “regulation = advantage” 3. It would be preferable to argue the facts (is it or is it not dangerous? Why or why not?) than conspire as to why they’re stating what they and plenty of others have been saying for decades
1. All those who have been arguing this - many of them non-technical - are part of the same ideological movement.
2. Technically, there isn’t. In practice, majority of the time regulations benefitted those who wrote them.
3. Not sure if this is a criticism of the comment you are replying to or of the p-doom crowd. We would all appreciate facts stated instead of science fiction tropes repeated.
Look, “a lot of people say this” is a Trumpian fallacy. A lot of people say a lot of crazy things. It says nothing about the factuality about what they say. How many are this lot? Who are they? What do they know? Why are they incapable of expressing it without so much emotion? How come a big portion are non-technical?
In fact, their core ideology actually stemmed from Yudkowsky's arguments.
> 1. They, and many other experts, have been saying the same thing since well before the current paradigm existed.
Some experts sure. "Some experts" say lots of things.
However, this is actually much to their advantage. If you were a frontier AI company, a legitimate concern to use as justification for your regulatory capture is exactly what you want. That is, after all, generally how regulatory capture gains public approval.
AI safety was also why OpenAI had to charge per-token for GPT-3. It must've been such a struggle for them to make that difficult decision. I'm sure Sam Altman spent a lot of nights crying to sleep over that one.
It's good that we never got open weights for a GPT-3 class model, right?
> 2. There is nothing about regulation that is required to give them an advantage; it’s a fallacy to suggest “regulation = advantage”
It's difficult to outline the number of different ways that regulatory capture can and will benefit OpenAI and Anthropic.
If we limit things to "Fable"-class models, then nobody competing with Anthropic/OpenAI is ever allowed to outdo them. If you literally legally aren't allowed to make better models, you can never charge the "premium" of having the current top-tier model. You have to compete by being cheaper than Anthropic instead, which is a lot less enticing from a standpoint of trying to become profitable. Take a look at the per-token pricing of Fable real quick. Yeah. Being able to charge a premium for the model that does the cybersecurity thing is literally their most important asset.
If Anthropic really is making a profit now as rumored, then they need regulatory capture as fast as they can possibly get it. Once Anthropic and OpenAI prove that AI is a viable investment, entities with a lot of capital will suddenly become even more interested in re-allocating it towards taking a slice of this pie.
> 3. It would be preferable to argue the facts (is it or is it not dangerous? Why or why not?) than conspire as to why they’re stating what they and plenty of others have been saying for decades
OK. On the factual end of things, AI safety experts have been raising the alarms for over a decade and so far essentially none of their predictions have come true.
The rationalists, the main group pushing the AI safety rhetoric that dominates the Western AI sphere and is virtually absent in China, which appear to be going entirely off the deep end at this point in time, put together their best minds to produce AI 2027, which of course like most predictions started to fall apart a bit before hitting the one year mark. Predicting the future is hard.
I have made this point before but I will make it again: I think AI safety experts deserve at least as much skepticism as Ed Zitron in their claims. They've been making similar dubious claims about AI capabilities and an incoming singularity for a long time now, and so far the best we can say is that maybe it will eventually come true.
Maybe. Or maybe you've read too much goddamn science fiction.
They seriously underestimate how ready and willing Americans are to use Chinese providers. There’s a ton of spite
Last time China pulled this shit it shook portfolios for a week
I get this is Hacker News so we have to hate AI safety, but you should read more on this topic rather than blithely casting the x-risk viewpoint as fringe. The last hard data we have on the question is a late-2024 AI Impacts survey [1] of 1,580 authors at top AI conferences, who were offered $50 for answering a survey on the topic of the future of AI progress. The median respondent assigned a 5–10% chance (!) to "extremely bad (e.g. human extinction)" outcomes.
> On the factual end of things, AI safety experts have been raising the alarms for over a decade and so far essentially none of their predictions have come true.
Devastating. When I wrote my AI alignment PhD thesis [2] in 2022, and predicted a 70% chance existential risk by 2072 (which I later came down to about 1/3 on), you're right that that prediction hasn't come true yet.
While I agree many specific worries didn't materialize (like AI being like a "literal genie" it's hard to specify goals to), many key worries have (Hugging Face rogue AI swarm scans as cribbed from rationalist AI scenario writing).
[1] https://aiimpacts.org/wp-content/uploads/2026/09/ESPAI2024.p...
[2] https://arxiv.org/abs/2206.11831, top of page 4
You're saying regulatory capture gives you no benefit by just refuting an absolute with 0 context. There's nothing to even debate here. Ok... most people disagree?
Yeah I agree with point 3... Stop engaging in vageries and say something of substance with examples, so people can actually have a conversation with you without us doing all the legwork.
OpenAI and Anthropic aren’t even slightly worried about DeepSeek.
May this kind of worry find me…
It's quite hard to have the valuation of Anthropic if they are limited to US.
The model makers are bricking the models with their system prompts, and even the tools are tamper proof.
You can only use it their way. They think we’re stupid.
And their way is the shit way, the babyproofed way, while they talk to you about their unfucked models hacking out of their labs and hacking into other labs, and reminding you you have zero rights.
Fuck them twice, then once more… This is about compute. Who owns compute. China is saying fuck you all we can scale things bigger than you can imagine. And they can! They have so much more energy! Theyre closer to commercial fusion than us too. Meanwhile our guys are trying to… regulate? Lol
Training and serving LLM's is massively parallel. Your bottleneck is either in energy or chips. China will catch up on chips fully decades before we catch up on energy. Arguably, this is already happening: here in the USA, we have lots of chips, but we are seeing our electricity bills skyrocket as more datacenters come online. We are powering those datacenters with natural gas turbines, not solar, wind and nuclear. Right now, to purchase one of those, you are on a 5-year waitlist. There's your timeline!
China is invested, from a regulatory, cultural, workforce, strategic, and technological standpoint, in an energy abundant future. We are not invested -- and in fact, actively sabotaging ourselves -- on all of those fronts.
Once they acquire chips, we lose, basically permanently. Look at what Huawei is doing recently and what is on their roadmap to understand how fast this catching-up is taking place. I'm sure that David Sacks would say that our innovation motor is pulling their copycat caboose along, as our monopolist tech companies steam ahead towards the frontier. I am not so sure. They are already ahead of us in many technologies. Speaking of technologies, they're building natural gas turbines in house, starting in 2025.
> Literally zero, but that never stopped a good conspiracy theory
Are you living under a rock and never read any of Dario's blog posts?
Let me give you an example, here: https://darioamodei.com/post/policy-on-the-ai-exponential
Section "1. *Regulation* and public safety" (emphasis mine), then under it:
> AI companies that develop advanced AI models must have strong security standards that protect their model weights
You can't "protect model weights" if the weights are openly available, ergo this is an explicit call for regulation to make open weights illegal.
Presumably you'd want the AI companies to protect their weights to keep them from falling into the hands of bad actors. But the US can't make open weights illegal anywhere except the US, which does very little to keep them out of the hands of bad actors (most of whom are outside of the US).
He's completely transparently campaigning for regulation. It must be very frustrating to him that the Trump admin is completely non-responsive. He probably thought Anthropic would have the whole market stitched up and water tight by now.
1. He's made many posts about how he thinks "dangerous" models are bad (https://www.anthropic.com/news/position-open-weights-models)
2. He writes posts proving Chinese open weight models are "dangerous" (https://insightsintegration.com/anthropic-warns-of-advanced-...)
3. He talks about about how he wants stronger govt regulation of AI (https://www-cdn.abcnews.com/Business/exclusive-anthropic-ceo...), and explicitly mentions he does not trust China at all and doesn't want them to have Mythos.
Do the math: 1 + 2 + 3 = he wants to regulate/ban the Chinese open weights.
Of course, Anthropic and OpenAI will then write posts about how their own models are dangerous - but that's okay, they're the good guys.
There are no choices here which do not carry risk. I haven't seen a single convincing argument against these points.
We also put responsibility on humans to act responsibly. In theory, it's absolutely insane to allow human beings the ability to walk into a street with automobiles, because obviously they will be killed! But we don't ban them from walking there, and we don't ban cars, because it's pretty uncommon for people to walk directly in front of a moving car, despite there being obvious danger.
Ultimately the biggest risk of AI isn't the AI. It's what human beings do using the AI. Just like with hammers, knives, drills, chemicals, cars... we regulate the human's actions and create laws to direct their use, rather than regulate the items themselves.
When HuggingFace was in the midst of defending itself from OpenAI's automated hacking spree, they were blocked from using frontier models by Anthropic and OpenAI for "safety" reasons. HF had to resort to an open model to defend itself - and DeepSeek distinguished itself in that role: the second fact from that episode OpenAI would have you soon forget.
They are showing this new version scoring significantly higher across multiple coding benchmarks, beating Sol 5.6 (6.1 is the latest) across 10/11 benchmarks.
I am going back to DeepSeek for writing code. I was trying out using Sol and liking the result, but this should close a lot of that gap. I use Opus/Sol for an implementation plan and code review also use them for grilling. DeepSeek is also running verifications.
i let 4 different agents review the same dirty code base (200+ files changed, c++, game engine):
- deepseek v4.1 flash via deepseek API: took 20m to find one p1 issue that was relevant and a p2 issue that was irrelevant
- glm 5.3 flash NVFP4 via TP2 DGX Sparks: took 1h 5m to find a p3 documentation issue
- gpt 5.6 sol: took 5m30s for one p1 and one p2 issue. both relevant
- gpt 6.1 sol: took 4m30s for two p1 and one p2 issue. both relevant.
Worth to mention: none of those findings overlapped.
I let deepseek v4.1 flash and gpt 6.1 sol both plan fixes for the summary of the findings. in this particular scenario: gpt 6.1 sol planned to increase a buffer size from 128 to 256 to fit the 191 elements that blew past a test scenario. it included some headroom. deepseek v4.1 flash on the other hand decided to make it exactly 191 to satisfy the test, for example.
The TPS decode on deepseek is impressive. I never had a second / feeling of being left aone and though "damn this thing is working". Despite all my efforts, it took ~4x longer in a review, found less and planned worse than GPT 6.1 Sol. Wall clock time. I don't have token counts for comparison. If I were to grade those models from 1-10 (10 = top tier GG), I'd give deepseek a solid 6.5 and 6.1 sol a 7.5-8.0 maybe.
This read like a conversation you were having in your head -- unfiltered.
The lower the number the higher the priority, e.g. if production is broken someone might say "This is p -1" (higher than your highest priority).
P0 is "do this immediately", P1 is very important, P2/P3 are like "nice to haves." P100 would mean something like "we will never do this"
At 3am,
P0 = get people out of bed and fix this now.
P1 = we can fix this in the morning.
P2 = we can fix this before CoB today.
P3 = push it into the current sprint.
P4 = put it in backlog.
This is why using multiple model families is important. Don't limit yourself by being a stan for this or that company.
today we can use some anecdata based on our observations, this also helps shape the dimensions we want to eval the agents-tasks on
I too am thinking about creating some benchmarks that I can understand myself. The LLM 'cheating' by finding known issues through some sort of history or online source is a path you want to protect against. Then again, you DO want them to be able to look online for people having similar issues.
Yet to be obsolete.
- For some reason choosing only a subset of issues.
- Different LLMs actually learn different things.
I suspect the former since they do this for me and it's extremely annoying sometimes
But try it on high instead of max if possible. max is known for being overly verbose to the point of being detrimental compared to high at times.
For example Simmons svg pelican took 11 minutes on max and 1 minute on high. And they are comparable.
I recall the difference being minimal in 4.0.
New model comes out and someone says, "it can (or can't) draw ducks on bikes" or some esoteric thing. But it's not enough statistical power to claim anything other than a personal anecdote.
I got a flat tire in downtown today. Therefore, driving in downtown causes flat tires.
For another, the dependence on a random seed is just one example of how you can get different behavior. You could just as well get different results due to e.g. a tiny difference in prompt wording or punctuation or whitespace.
If a model passes some test on 70% of random seeds, and fails on the other 30%, then knowing the rough probability of success is much more useful than knowing one random sample of the outcome.
And empirically this kind of probabilistic success is quite common, because many LLM benchmarks report "pass@k" success rates that are much higher than the "pass@1" success rate on a single attempt.
Tiny variations in the computational environment, such as slightly different set of processes running on the system as well as tiny differences in core temperature or voltage - among many other factors, can cause computations to complete in non deterministic order relative to each other.
This meaning one run eventually calculates a+b+c, whereas another does a+c+b, yielding slightly different results. Those cascade down the chain, resulting, finally, in a different set of produced logits.
Notably, similar effects can and do affect pure CPU calculations on multi core machines, and as a matter of fact even on a single core depending on the architecture when considering eg pipelining, branch prediction etc.
Thus, real world matrix operations are not, in fact, deterministic.
Now, one could argue that all of these are negligible, but that is an empirical claim dependent on the problem at hand.
This is not true. Non determinism only arises via sampling, and to a much lesser extent, CUDA kernel launch subtleties.
Were you using the same setup to call these models? was it pi/opencode or was it codex/claude etc etc.
Because I've started to see larger and larger drifts in the results depending on the agent it self rather then the model.
Deepseep-v4.1-Flash-1017 is a minor update. A major update is something like Deepseek-v5-Flash.
FWIW, GP didn't write "a bad copy" but "bad copy", referring to the marketing sentence quoted by OP, rather than the model that was just released (as I presume "a bad copy" sounds, maybe like it's called that because it's [again, presumably] distilled?). "Copy", especially in a marketing context, is a written message.
An example when using a less frequent term actually reduces specificity.
The CCP requires extensive certification for new major releases, to make sure a model doesn't do anything they don't want it to.
To get around this, Chinese labs only bump versions when they have to and usually claim it's a minor update.
Then on the other side, they're obviously trying to market to the tech crowd...
Would highly recommend it, it's IMO the only serious Western reporting about China.
as good as Gordon Chang.
The problem here is quite often brainwashed people never realize that they have been brainwashed. Sometimes they trust, promote or even defend the perpetrators. You have my deep sympathy on this matter.
There is no such thing as an unpoliticized publication, but one can still read, taking into account their biases. The point is to extract the information while discarding the ideological interpretation. In fact, this exercise is often easier when the ideological tilt is as obvious as the capitalist liberals from the Economist.
Forget about their predictions or their moralizing, but they have actual Chinese reporters talking to actual Chinese people on the ground instead of doing the usual Western thing of white MBAs treating China like some alien civilizational threat.
Western media doesn't have correct informtion source about China. This is because Western favors Web ecosystem and most inforamtion live in the Web, but in China everyone use Apps.
Also, DS is an excellent model.
These are not mutually exclusive.
This is just my headcanon though.
Much like Qwen 3.8 Next; which is qwen4-exp in a lot of the code/patches, there's usually a tick-tock cycle with LLMs. That said, it's hard to know for closed frontier models.
DeepSeek delivery deliberately uses confusing version numbers.
- Reasoning effort = 1 is now actually usable and produces 2.5x less tokens than old weights, without intelligence lost.
- Overall 20% less tokens at each reasoning effort.
- Same intelligence and same tool-calling pattern.
More details in blog post https://sference.com/blog/deepseek-v41f-1017-benchmark
There's something to be said for the speed with which it gets word done - I take fewer "it's compiling" breaks, and lose my train of thought less frequently. What's crazy is that the speedy work it does is pretty much always correct - which is a reason why I don't feel the need to spin up claude-code and "ask the expert".
This speed & good-work combo is the DS signature IMO
https://github.com/computerex/dlgo
If you want your 5h usage to runout in one call feel free to use OpenAI
Now we have: a ton of competition in different inference providers, very low costs, and speeds reaching 1k tok/s in certain providers.
We can just get a box of compute even in Germany, and run the exact same model here without anybody stopping us.
This is why "tokens per second" alone is a deeply flawed comparator.
This is also why discussions of Chinese models focus on tokens per second- they struggle to compete on intelligence so they dramatically increase effort levels to compensate, meaning they require massive total output tokens.
For example, flash 1017 isn't benchmarked yet, but 0731 required 15.4M tokens to complete the artificial analysis benchmark, and Sol 6.1 high required just 2.8M.
Sol 6.1 uses 2.8M at 64 tok/s = 12h 9m
Flash 0731 reported 149 token/s at 15.4M needed = 28h 42m
And in my example in the first line, scenario A (30 t/s) completes 6 minutes faster than scenario B, despite 10X slower inference.
The best way to answer this question: [Pareto frontier or cost/per task curve](https://api-docs.deepseek.com/img/v4.1_261017_perf_cost_en.p...)
Sol 6.1 High: 50 intelligence, $0.32 per task, 4.4 min per task, 13k output tokens per task
Sol 6.1 Medium: 48 intelligence, $0.21 per task, 2.9 min per task, 8k output tokens per task
Deepseek 4.1 1017: 45 intelligence, $0.26 per task, 4.8 min per task, 89k output tokens per task
If you look at the actual pareto frontier across the full benchmark, Sol 6.1 remains firmly on the pareto line, while deepseek remains firmly under it, just as it was before https://i.imgur.com/N72p5NF.png
This result is exactly as I expected, and seems to run counter to the effusive praise of this thread. Sol 6.1 Medium is producing higher quality output at a lower price with nearly 10X less output tokens! And this is not surprising.
And finally, Mimo V2.6 Pro continues to look like a stunning release, clearly smarter, cheaper and faster than deepseek v4.1 1017
It's a bit like obsessing over patch releases of some software after a major upgrade already solved the original problem long ago.
And then when I try a smart model, like Astra medium and it solves it (I had a firmware bug in my new laptop where I couldn't charge through USB-C) on the first try (altough with a repo already having lots of information about the hardware/firmware/debugging programs/traces), I realize how much time I'm losing on the few examples that the less intelligent models are just not able to debug and solve.
I was really hoping this would be solved since openai said that gpt-6 could paint brushstroke by brushstroke but it’s still shit
Is your laptop something like framework which has open source firmware?
I(and many other people) have a similar issue with usb-c charging not working consistently. I've been meaning to try one of these new smart models to help me debug the issue.
I had to reboot my laptop a few times though until I got to this, but it was a fun experience anyways.
At this point open source isn't as much advantage as long as the firmware is not encrypted :)
I'll try handing it off to one of these new frontier models.
What about "good governance"?
Does it have zero hallucinations? Does it reflect thoroughly on every single idea it uses, verifying its solidity?
Depends on your use case. But if you do software development with a harness such as oh-my-pi, your cache rates are probably somewhere above 98%. Coralbricks has free cache reads, meaning it's much cheaper than the Deepseek API rates.
Lithosai is more expensive especially in the ultra tier. It's if you want extremely snappy and fast responses, which is important e.g. for a personal assistant where every second counts.
If you generate a lot of output, you're better off with the Deepseek API rates.
Outside of major players like Amazon/Google which have compatible offerings available, I believe together.ai, fireworks.ai, and deepinfra.com are, as best as I can tell, US-based (if that matters in your situation) SOC2/HIPPA/ZDR. Each of them offer serverless model hosting with caching, which is going to be the cheapest overall. Most of them also offer private AI deployment which is more expensive but your entirely own instance. Some of them more heavily quantize their models (deepinfra) so take that into your workload considerations. I included deepinfra because they tend to have some the new models sooner than others or have models the others don’t (like mimo). IMO, together > fireworks > deepinfra.
However, you’ll have to keep in mind that for true ZDR you give up caching which increases your inference costs, so it depends on how “Zero” you need the zero to be and on what timeframe. If you just don’t want your data being trained on, normal use is fine imo. If you are working on something actually sensitive (like, because of NDA agreements and such), then they might not be the right choice and you may be looking at more expensive offerings or building a machine.
One note: you have to pay attention to the source of the inference. Many (similar to open router) offer models that are pass-throughs to a providers API, but they are pretty clearly marked. Just make sure the model is marked as serverless when evaluating new ones. This is where a proxy comes in handy so you can prevent your LLM from accessing a different models from the specific ones you’ve vetted.
Like if we needed PhD intelligence for productivity tools and SaaS platforms, why weren't we hiring PhDs to build things before.
99.99% of people do not need SOTA ai.
How?
I am not saying OP is not telling the truth. I just really want to understand!
Anyone who tells you they have a “team of subagents” and “code reviewer” agents and “project manager” agents doesn’t know what they are doing. That’s how they burn thousands of dollars in a day.
I think I’ll trust the experience of the people in the labs who have unlimited token budgets and are running experiments continuously over some anonymous rando slinging mud on the internet.
Also my own experience, which says having code reviewer agents is one billion percent useful. That comment alone completely disqualifies you as a reliable source of info.
This is how one of my claude code session looks like right now, one prompt from yesterday:
◯ lean-tracks ▰▰▰▰▰▰▰▰▰▰▰▱▱▱▱▱▱▱▱▱ 7/12 · 18h35m · ↓ 3.1m tokens
◯ lean-tracks ▰▰▰▰▰▰▰▰▰▰▰▰▱▱▱▱▱▱▱▱ 6/10 · 17h47m · ↓ 2.6m tokens
◯ lean-tracks ▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱ 0/1 · 4h49m · ↓ 275.9k tokens
◯ lean-tracks ▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱ 0/1 · 4h30m · ↓ 395.0k tokens
◯ lean-tracks ▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱▱ 0/1 · 3h50m · ↓ 490.3k tokensI created a skill to better manage agents, including a way to keep the caches valid, and it has decreased my usage considerably. I now struggle to use my 100% each week and I hit claude hard. I have a reset expiring in 5 days and I'm trying to burn down the tokens (68% with 3d 8h left) quickly so I can use my reset before it expires.
Token efficiency just means understanding the way the system works. Best person to help with that... my old friend claude.
Claude: Confirmed: 195M of the 212M cache tokens (92%) come from full re-writes after 5+ minute gaps, accounting for 77% of relative cost. Now I'll check the existing wrappers so the skill builds on them.
Can you invert things. What’s deterministic and what needs “thinking”?
Can you create a script that runs things chaining together the deterministic parts with calling out to Claude for glue that needs “thinking”.
Maybe doing bogus small requests to prevent the cache from expiring?
I mean in the API you can set the cache expiration time right? But I guess claude code can't do this
Awfully new old friend.
I don't actually know the real mechanism, I came across this serendipitously.
Buid alone takes 4 minutes. Test suite heavily parallelized runs in about 2 to 3 minutes, infrastructure dependencies fully containerized using podman, no external API call.
And this is without mentioning the 5000 frontend tests (angular), including Playwright end-to-end tests wired to the backend (about 3 to 4 minutes run without the e2e tests).
And this is running on a dev machine with 64gb of RAM and an NVMe disk.
Some systems are just massive, and even if you run focused tests, it's easy for a change to have a large blast at least requiring a large rebuild, and at times to rerun a larger set of tests.
However where i think you have a point is that it tends to run tests quite casually where a reviewer would have been enough to catch mistakes.
IMHO, the real problem is the 5min cache way too small. They should up it to 1 hour.
I think the tests problem is really having an impact for me. Hadn't considered it before so thanks for flagging.
- How long do you reckon it would take a human to do the task?
- What percentage of features/ feature-extensions you add would have been done at all if you didn't have agentic assistance? Is there a tension between llm productivity gains and 'because we can' feature additions?
- Are many of the tasks for throwaway v1 features, exploratory tries?
- How speced out is the work you give it, eg did this task have pages of specs, how long did the spec take to write?
- How deeply will you manually review the work and how much time will it take? Will you read all the code / review at a high level and interrogate with llm / have the llm review and self iterate?
I do now add all the major features I want, and write a task list where I add a few more minor tasks each week that just come to my mind. I need to write them down before I forget. I often ask Claude to implement several different implementations/algorithms and compare them. There are plenty of trade-offs, which usually end up in building configuration profiles, so the application can work optimally in multiple environments.
The specification is actually really large, and has grown over time as I've been learning and new ideas has come to my mind. I do not review/audit the code, it's massive would take me months, no chance! I do however use code inspection/audit tools like TSAN, ASAN and more. I will also spend time on full code reviews with the coming Fable 5.5 when a release is about ready. I do however review behavior together with Claude, and this is also where the specification grow, because this and that should be possible, but not this and that. User stories also helps.
There's no reason to blow through tokens this way, there's a 99% chance nobody uses the output (except maybe themselves).
That’s the “how” we’re wondering about.
What exactly are they doing for you that's giving you $200 of value in such a short timeframe?
Maybe you're not being ambitious enough with these things? They are at their most fun when you spec out sprawling architecture documents and watch 1+8 astras buzzsaw through it.
I'm only at 260B for the year, though. I'm sure someone who has used more will be along shortly to chime in
E: The other subthread talking about tests is correct. Expensive models should not waste a single second waiting on tests or validation. If you cannot assign a single cheap model to be the test bitch, then just tell them to frontload everything and clean up later as a single wave.
> Have you shipping anything?
Yes, hundreds of PRs and shipped new features to 3 different platforms (day job and 2 side-business apps/platforms I’ve been developing for many years, pre-LLM). Those new features have gone out to customers and been used.
> Do you have income from this?
Well my day job for one, and I’ve been able to more easily sell to new clients with my side business. Without the changes I’ve shipped I would have been able to land a couple of the clients I’ve signed on recently.
I don’t think OP’s message implied they are never finishing a project, maybe they edited it?
In addition to the 3 income-generating projects, I have a number of other “just for me” projects. Some are mature now and only need occasional development when I want a new feature. Some are in flux as I build them out (like my “software factory”).
At this point I’m limited by compute and tokens more than anything else.
EDIT: but thanks for a useful answer to OP's question. As a programming dabbler I also find it hard to imagine how to push much past the 5hr limit on $20USD subscription.
It’s paid for itself though, at least from my side business income.
It is like putting a jet engine on a shipping container and wondering why it needs to be constantly kept in afterburner to at least scrape on the road while smarter users put wheels on the shipping container.
How people spend more than 200 usd monthly is beyond my understanding, are you guys so inefficient with the usage? or I am the opposite?
I tear through my $200 subscription because I often have 3-4 agents running simultaneously, exploring performance optimizations, building features, monitoring production rollouts, or investigating production problems/customer issues in parallel.
The downside is that I do spend most of my time reading the agent's output these days.
I would be worried about your methodology when the subscription party ends. Make no mistake, one day, the party is ending and the people who are dining out on approaches that burn massive tokens are in for a rude shock.
At the beginning of the month I realized I had more tokens and more capabilities on Opencode at 8$/month via DS 4.1 flash, than I had on a 200$ account in June with the american providers.
Literally both more tokens and capabilities than Opus 4.8/GPT 5.5.
Even if you pay per token, you're still unlikely to outspend american providers.
And southern Europe isn't exactly the poorest region around.
If you can get away with paying that much to a swe in Italy then why aren't Italian tech companies ruling the world on the back of ~free labor?
There’s also zero stickiness to models and most companies now have their own internal routing utilities so devs and users can just keep the same API endpoint and move between models with a single line of code swap.
Unless something changes with the big labs and fast, they look pretty f*cked.
You’re assuming other model creators can’t build these mega models. Evidence is they’ve simply decided it’s a fools game to do so and they’re focused on where the use cases are.
It is more complicated than that when the valuations and business plans are contingent on those companies AI having complete dominance at every level.
If companies only use frontier models for occasional difficult tasks, not only does that affect volume, it basically elminates stickiness and incentivizes companies to instead of using their products continuosly, find ways to move from the expensive stuff to the cheaper stuff.
They made very bad bets on scaling, which has cost ~10B scale in wasted capital allocation, but it is manageable. Their purchase commitments for future compute capacity are absurd nonsense, but all of that is just shtick for retail investors, and they will reshuffle the deck as needed to adjust.
Probably a pretty decent model for 90% of the usecases!
I've been doing a lot of hobby work with GLM 5.3 Flash and I can count the amount of times that I need a stronger model on two hands.
I have access to unlimited tokens at works and Opus is great but also often overkill if you know exactly what you need.
A very complex task tends to cost like 10$ max. It's comparable to a subscription with enormous flexibility.
It's a steal.
Of course I do certain things like use omp and do a compact picture snapshot after 256k tokens automatically. My tasks rarely go over a dollar...
I've ported a few games, wrote a cosmic desktop app, iterated in my own agent software, use the model in our home speaker system, use the model in my phone assistant. My monthly bill to coralbricks is about 14 dollars.
If it's mission-critical code, do a security review with GLM 5.3.
Otherwise go manually through the code and ask for changes if needed.
Works really well for me.
Since making this (obscure/hidden) change I enjoy Codex much more.
Really? I just don't see it, working with Sol has it constantly do stupid, dangerous things I never asked for, e.g. "I’m sorry. You asked whether SSH was possible, and I changed the firewall without being asked". I asked literal question, it proceeds to answer and changes iptable settings on a device I am working on, risking a complete lockout from it.
It is genuinely dangerous and has to be looked at all the time. Fable, Astra and Opus 5.5, but also Deepseek 4.1 are ahead of it. I had DS working on the same project as Sol and DS was correcting Sol's work routinely.
I tried Claude legitimately for a few months, on max
That was during the great anthropic compute/gaslight crisis (ahh good times..), Claude then enthusiastically bricked my MacBook and I had to get gpt to unfuck it
MiMo 2.6 Flash is the most capable model you can currently run on 2x Sparks without resorting to a quantised version of something.
Are they as good as opus 5.5? No.
Are they about on par with opus 4.6? Yes.
The long run tasks i run seem to be about the same level of quality, though there are obvious differences in model styles, token budgets, etc....
I don't think there is a reason to care about any of it, just get a few dollars on a few openrouter or similar platforms and just use the models for now, wait for cheaper models that are good enough for you to become available for your hardware.
Sure you might never see frontier performance on consumer hardware but do you really need that though? Does anyone? And if you ever do just get a 30-day sub for that work, and cancel it.
We are running Qwen 3.8 27B FP8 with 4x 5060 16gb at the office for a small development team. It is doing pretty well for implementing backend and frontend tasks, and is already useful. In 6 and 12 months from how I expect it to get even better. Some planning is still done with a stronger model, but even that is maybe not strictly necessary.
And we are likely to see many companies start building 27-32B MoE models custom built for use cases now that Rubin is going to make training costs much lower.
I expect training costs to get under 100k USD within a year for small models with another 100k maybe for RL.
I think just having all your own data from claude and stuff on your system that you can feed these models and have companies offer great base 27B models will be how it works.
I didn't try the last flash model because I would need to use SSD streaming (I have 128 GB), but the benches for this release are so compelling (and it using less tokens to get tasks done) it might be worth the throughput hit. I will probably give it a try when I have some downtime.
It feels very freeing to be doing useful work on a local model on my own hardware with my own coding harness. The TCO argument is starting to look compelling too.
The speed and cost/intelligence ratio is just outrageous.
If I'm impatient I switch to Luna for a single turn just to get it started and let DS continue.
Benchmark is historical PR reviews. Models are put in a sandbox with a git and db snapshot as it existed at the time. PRs are a selection of changes some bugs (major and minor), some with things that aren’t bugs that have tripped up models in the past and some no findings that they should approve and move along.
4.1 flash already wasn’t that strong at this task, but the speed edged out v4 pro for a spot in the roster.
Definitely an improvement on deepseek-v4-flash-0731: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Looks much better.
Lovely to see they're doing their own search though, I love how many folk are actively working on search clones at the moment.
It works when it works, but will probably be flaky AF for a few days.
Works well with Copilot:
COPILOT_PROVIDER_API_KEY=<OR key> \
COPILOT_PROVIDER_BASE_URL=https://openrouter.ai/api/v1 \
COPILOT_MODEL=deepseek/deepseek-v4.1-flash-1017 \
COPILOT_PROVIDER_MAX_PROMPT_TOKENS=1048576 \
COPILOT_PROVIDER_MAX_OUTPUT_TOKENS=700000 \
COPILOT_OFFLINE=true \
copilot
Not only did it come in significantly cheaper even than Haiku, but it was more correct than Sonnet.
This is great! I am gonna get coding!!!
https://www.reddit.com/r/DeepSeek/comments/1x84wke/deepseek_...
DeepSeek v4.1 Flash was a beast.
It was already cheaper and more powerful than Haiku 5.5 which was released like a full month after it... by a trillion dollar american "frontier" company.
Can't wait to see what this one has in store.
BTW who would have thought China would be the champion of freedom and opensource in the AI arena, while US is fighting for corporate/government control.
There are different types of freedom and China's bet is that as long as they provide enough economic freedom the public will be ok with somewhat restricted social and political one.
The fact that she can't call Xi "Winnie the Pooh" on Douyin wasn't something that occurred to her, or maybe that she was aware of at all. I didn't ask. I don't like to get into these discussions when I'm in China.
Citizens of Palm Beach county cannot voice their opposition to how their tax dollars are being spent. They rightly want that money to be invested locally, instead the County uses it to invest $1B in Israeli bonds, something that people widely oppose.
They first said that it's a budget meeting and Israeli bonds are a "policy matter". Then they started arresting people for speaking out against it. Then they started arresting anyone who applauded.
"Free speech" -> I spit on American "free speech". It's the most insidious and dishonest thing. At least the CCP doesn't PRETEND to give people rights that they simply don't have.
the U.S allows "free speech" if it's inconsequential.
Actually she can do this. There's plenty of such videos on Douyin ... If you clicked such videos frequently, algo will recommend a lot of them to you.
The worst case of uploading such videos is an aaccount ban (but very rare). So yes the freedom of speech here is fewer than in the U.S., but it's not like you'll be put into jail if you say sth about the president.
As a Luna6 proponent, this has a potential as it looks slightly more intelligent and at the similar price point.
Right now I use each of them in their own respective coding harnesses, deepseek-harness and kimi-code, but I do wonder what they are like inside something like opencode.
That's way cheaper than openai and other web search providers. I have no doubt that AI demand justifies all the compute money big tech is spending now, but I also think there are so many over valued VC funded companies that are going to crash in the next few years.
OpenCode if you want a good, opinionated setup
Maybe the equation changes if I exceed my subscription usage, and need to fill the gap with something else. But it seems like a huge waste of your time to go out of your way to use a cheap model if you have the tokens to spare in your subscription.
https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017/...
> providers have to implement which could be different than what the labs themselves use.
I'd imagine (most) providers just take implementation by vLLM/SGLang.
My goal is to reverse engineer an entire gamecube game -- I'm pushing an existing community decomp that was stalled to the finish line, solo, with lots of AI help.
This work was directly inspired by https://decomp.dev/ -- which has just shut down a few days ago because of, ironically... people like me who enjoy decompiling things with AI, so I'm still weighing / grappling with the ethics of how to release my work, it's murky waters at best.
I use Opus 5.5 as the orchestrator, as well pi harness because it's somewhat lightweight. But for the really hard functions I don't use deepseek I use opus managing other opus agents.
I'm working to release something soon as proof that I can do a 100% decompilation job, but the main idea is that when decompiling a gamecube game your sources of truths are individual functions or files, so you can build off that primitive.
Each ai gets a worktree, and a shared giant context file that's basically a record of every experiment that has happened. They're assigned tasks to essentially do as much as they possible can for each individual funciton. After about an hour claude uses a script to merge the work that's actually correct (verified by tooling that checks if the c goes to asm) and then there are usually 3 rounds of reviews by independent ais, because there will always be some slight mistakes. The techniques used as well as the mistakes the models make are recorded in the giant ledger. There's a bunch of random one off scripts such as stuff to clean up, and tools to make sure the agents don't accidentally google stuff / delete stuff / use too much space.
Claude steers the ship via ssh on my strong linux tower at home so it keeps my laptop nice and cool and at this point I just check in from time to time, I am basically a manager, and Claude has it's own workers.
Last point -- I don't know any c besides the basics, I've only been "coding" python / ts for a couple of years, but even then with the extreme strength of models the future is managing agentic swarms, so I'm leaning in as much as I can. I really see this as the future of large scale projects. For some people it's a mockery of decompilation, but, my goal is the finish line, not the journey.
When I type deepseek on my CLI it launches Claude with my fireworks API key. Works very well and feels almost free.
I have been intrigued about Chinese models for a long time, but never tried anything except an early Qwen. My daily driver was Luna, with occasional elevation to Terra or Sol - all on 5.6 as I found the output of 6 jarringly incomprehensible.
I still had 95 USD on my OpenRouter account, so I thought I would give DeepSeek Flash 4.1 a try.
I think now I can understand the person who created that thread.
Here is the setup I settled on, after a lot of refactoring and calibration of the workflow:
I have a "plan" prompt which explains to the agent (Luna) that I want to have a discussion about an idea I have in mind. The agent should use the Socratic method to pull out all the necessary info from me using pointed questions. When the agent feels it has a complete picture, it should elevate to a higher level model (Sol), and ask its review. If Sol has complaints, Luna keeps asking me until the points raised by Sol are clarified. The review rounds with Sol continue until Sol says "ready to implement".
At this point Luna creates four files in a directory I specify:
1. PLAN.md: the implementation plan with all the details that came out from the design discussion 2. PHASES.md: implementation phases 3. DECISIONS.md: design decisions (this serves the same role as ADRs aka. architectural decision records) 4. DIARY.md: append-only log of what happened during implementation
Files 2-4 are written by the implementing agent. Luna only adds headers to explain the purpose of the files.
Once these files are there, I start up a new Pi session, switch to Deepseek 4.1 Flash and then point it to this plan folder.
And then it goes brrrrr... :-)
Here is what I implemented in the past week using this method:
1. A capture/analysis/decompilation pipeline for C64 demos: I ask the agent to start a selected demo through VICE, and say "on" or "off" when I want it to start/stop capture. The capture process uses VICE's builtin trace functionality: the trace log is piped into Clojure where it is saved into an optimized binary format (chunks of trace events). Once I have everything captured, I unleash my analysis pipeline which checks the write patterns of the executed code: what it wrote (VIC, SID, CIA, memory) and when (rasterline, cycle). Then it splits the execution trace into segments by type deduced from the write patterns (decruncher, loader, raster, music) and then decompiles each segment into both traditional ASM files and a s-exp based syntax which roundtrips back into the in-memory representation. Once I have the results of the analysis pipeline, I can tell the agent to analyze any demopart and it can explain how the effects are built down to the tiniest timing detail. As a first test, I asked it to create a clean-room implementation of Fairlight's legendary intro and two days later I had it, with everything in sexp assembly, including the music routine and its data. (The reason it took so long was that this was the first decomp and it provided a lot of feedback to the pipeline development.) Finally I had it write an assembler, linker and packager which creates a PRG from the assembled and linked artifact, then runs it through VICE. Done.
2. A DSP compiler which lets me compile sexp forms of a C-like DSP language into native DSP kernels running on the JVM, JS and WASM. Currently I have about 600 tests which verify that the behavior of the three targets matches that of a reference interpreter written in Clojure. I have a full numeric tower with i32, f32, f64, support for arrays and structs, typical control flow constructs, and a library of DSP building blocks like oscillators, filters, LFOs, delays and a chorus effect. By the time this is ready (end of the weekend?), I will be able to build synthesizers like Synth1, Surge XT or Vital in Clojure/Script, compile the DSP kernels in the browser, choosing between JS or WASM backends, driving the kernels via AudioWorklets and sequencing them from the host language.
There are two power tools at play here:
1. Deepseek Flash 4.1 2. Clojure and ClojureScript
Deepseek is a BEAST. It just goes on and delivers, phase after phase. What it needs: clear instructions - this is covered by the planning process quite well, but I still find it needs some steering from time to time (the right nudget of wisdom dropped into its thought stream at the right moment works wonders)
And Clojure... well. That article PG wrote about LISP (Beating the averages): what I witness now proves all of those claims.
The agent LOVES Clojure. Especially the REPL (not so much the parentheses).
Winning strategy: give it two tools: start_jvm and clojure_eval. Implement everything else it may need - tools to start up HTTP servers, compile ClojureScript, add dependencies, capture a C64 demo, run Playwright via spel, etc. - as lightweight Clojure namespaces which provide APIs to all these functionality. Let these namespaces be thin: the namespace and the functions therein shall have docstrings which tell the agent what the functions do, but the functions themselves shall be lightweight proxies to the real implementations in sub-namespaces. Then point the agent to these top-level API namespaces via your AGENTS.md. Bam, instant discoverability with minimal context usage. It will do EVERYTHING through clojure_eval and the round-trip time will be phenomenal, a perfect match to the raw speed of the agent.
...
Now all I need to figure out is what will I do when all my dreams become manifest in flesh.
Then I turned what Deepseek did into an upfront policy.
This seems to work quite well generally: watch the agent as it works, and think what could be provided to make its life as easy as possible
I wrote a Pi extension called "model-escalation" which lets the model request an automatic switch to a smarter model for the next turn if it thinks the problem it is struggling with is too complex or if the user explicitly asks it to do so.
With this extension in hand, I could split the work between Luna and Sol, resulting in a ~95%-5% split. I don't have to switch to Sol when review time comes, Luna does it automatically. Sol tells its remarks, then Luna gets the control back and continues the back&forth (if there are still complaints).
so AI labs can just pick which to show
I'm a communist so I'm fine, but you may not be, so be aware
[1] https://www.reddit.com/r/LocalLLaMA/comments/1x7g3zq/china_p...
Here's How Distill APT Used McKinsey's Private Access To Top Anthropic Model To Help The Chinese Keep Up With The Us Top IA Labs Despite Access Restrictions
Besides, Fable 5.5 is reportedly releasing within the next few days, probably on Tuesday...
https://www.reuters.com/world/asia-pacific/china-proposes-ad...
They’ve technically got expedited approval for open weight models I think, but it still needs approval. And frontier level stuff gets extra scrutiny
yes maybe opus 5.5 is stronger and flash is cheaper, but 6.1 strikes a good balance imo.
You can in contrast get far more done with $20 worth of deepseek api spend. No comparison.
Anthropic and DeepSeek (+ GLM) are definitely solving coding, though!
They're all quite different tools.
For me it's large files in Blender (in some cases >400MB), mostly, where Astra excels. I gave it a look, but I don't know if the benchmark encompasses that sort of thing.
.step files are simpler pieces of straightforward code, so it makes sense that Opus and Sonnet 5.5 do very well...