> It's hard to say what their motivation is.

Feels pretty easy to me.

They want to turn LLMs into a commodity, and watch the US AI labs crash and burn.

There will still be plenty of customers who will pay them to host the models and run inference, even if the weights are open and others can offer competing products. (If necessary, the Chinese government can ban use of foreign inference services by Chinese citizens and businesses to give their own companies a domestic monopoly.)

When their models equal or surpass those from the Western AI labs, they can even stop releasing weights for new models, and keep all the inference revenue for themselves.

Meanwhile, they're still manufacturing much of the hardware that everyone in the world needs in order to run datacenters (see also: Spolsky's "commoditize your complement" essay).

Beyond that, it's a soft-power play. As the world keeps looking at the US more and more skeptically as an ally and superpower, Chinese companies releasing weights for competitive models is a way for China to look better and more world-minded.

> It's hard to say what their motivation is.

Not for anyone who reads history.

Back in the late 18th century, England was the world's top economy, in big part due to its textile industry. England had an export ban on the technology, but textile worker named Samuel Slater brought blueprints over (Supposedly in response to a bounty posted in a newspaper by the US government!). The technology diffused rapidly because the legal environment made competition easy, and ironically the US had better sources of energy (superior water-power sites).

Arguably, China is doing the same thing in the 21st century.

In China, you can’t officially use US APIs. The world saw a taste of this with Fable, but in China, this has been the situation all along.

So it’s not a surprise why open weights are so cherished. As frontier models continue to block everyday individuals from securing their own codebase, I expect the adoption and usage of open weights to continue.

As an example, HuggingFace recently was investigating a security incident and got locked out of frontier closed APIs. Yes, HuggingFace.

https://huggingface.co/blog/security-incident-july-2026

Fwiw, American industry has given away a lot for free - you could include large parts of the open source movement in that - and all the "free" VC backed services like facebook would be another prong of the same comparison. I would rather compare this way, that China is gaining soft power and goodwill, in the technology and innovation sense, in a way that's similar to how USA has done in the past.

There’s a Twitter thread making rounds by Dean Ball about deceleration in AI development caused by open models and I can’t understand how people don’t see that it’s true: open models dismantle the frontier lab capex spend potential by reducing the training budget to zero in the limit. Tokens from different providers are not fungible, but customers are nevertheless very price sensitive and close enough is good enough, eg. K3 being opus+ in capability and cheaper than opus per successful task in the long run is an obvious financial decision.

No training budget means deceleration, or at least slower acceleration, margin compression and a completely demolished IPO valuation; path to machine god requires dollars and capable open models externalize training costs to true frontier labs parasitically.

IMHO humanity has a better chance at not destroying itself due to less than breakneck pace - but there’s a chance frontier models get sponsored by the USG and are never released publicly so they can’t be distilled and then what?

> It's hard to say what their motivation is.

Not that hard to say IMO, they basically see models becoming a commodity and see value in the applications on top of them. So if Alibaba Cloud is the best place to build applications on top of Qwen, why not give the model itself away?

China is watching world sentiment shifting away from USA. Doing many small things that show both strength and openness is surely very intentional.

> It's hard to say what their motivation is.

Maybe because the industry isn't yet very sure as to what the use cases might be for these technologies they're hoping that by making it open source and accessible to everyone that someone could find interesting applications for it and even more so, perhaps, way to further the technologies themselves.

There are more Chinese than Americans, so statistically speaking, I'm guessing, there'd be a greater chance for one of Chinese engineers to make advancements than one of American. But that's pure speculation on my part, being neither, I'm just happy I can be a part of it and play with the tools as well~

Popular open-source projects:

Google: Chromium, Kubernetes, Android, TensorFlow

Meta: React, PyTorch, Llama

Microsoft: VS Code, TypeScript, .NET Core

LinkedIn: Kafka

Slotted in along these, an analogous explanation is that Alibaba needs Qwen internally (vs depending on an American company), but licensing is not part of their revenue strategy. (As a cloud vendor, they can make money on inference. The strategy is very similar to the US hyperscalers ex-Google.)

Joel Spolsky wrote in depth about this notion of commoditizing one's complement in 2002[1] using tech examples stretching back into the '80s.

1 - https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/

They are trying to make money. That's what firms in any capitalistic economy care about the most. Regardless of the government's presumed interference, the companies themselves are all trying to make money. All competing for subscriptions and API payments.

One aspect of this is making a name for yourself i.e. PR. Making a capable model open source helps a lot with that.

> It's hard to say what their motivation is

Why is it hard? Their government has been very clear that they plan to win on manufacturing: https://english.www.gov.cn/news/202601/08/content_WS695f1b55...

Technically they've been saying it for the last 40 years.

Humanity is a bit of a stretch, and to be seen over time, not that I'm saying it won't happen; let's get some hubris here.

Xi Pitches China as Leader of New Global AI Order, Challenging US Dominance:

https://www.reuters.com/world/asia-pacific/chinas-xi-promote...

What I understand is that by doing this it seems like profit will shift to chip makers,as we'll run more models locally, and currently American companies have the advantage here.

So what would the long game be for chinese companies?

I'm paraphasing but the Chinese premier said recently AI should be seen as a common good that should benefit everyone.

The Chinese firms may just be making a bad business decision.

It’s too soon to say if it’s good for humanity; that might be overly optimistic. Commodity markets aren’t always good (for example, arms or drug markets). Will LLM’s turn out like one of those? There are people I respect arguing in favor of more regulation.

> it also happens to be really good for humanity.

AI being good for humanity is still an open question, but for closed vs. open models/weights, yeah it is preferred. I foresee it won't be much longer before everyone will be slicing/distilling/tuning their models once the architecture improves.

Its about closing the gap. its the gap over everyone else that will give one country leverage over everyone else in the AI age. Makes me wonder what the world would look like if a country or group of countries did this during the industrial revolution.

> It's hard to say what their motivation is.

Involution is a major problem in Chinese industries [1]. Where companies will sell their products at a loss, effectively playing fiscal chicken [2] with one another to dominate a market. It is such an issue the government has had to step in to prevent EV companies from destroying themselves by more-or-less requiring companies sell their goods at a profit [3].

The straight forward line of reasoning that AI/LLM labs are applying this logic to their profit.

I think (we) Americans are reading a bit too far into this assuming government intervention, conspiracy, etc.. Chinese markets are downright cut throat. They're using those tactics to compete with US labs.

1. https://www.reuters.com/business/autos-transportation/what-i... 2. https://en.wikipedia.org/wiki/Chicken_(game) 3. https://www.theguardian.com/business/2025/aug/05/china-warns...

Good for humanity, and also GDDR/HBM manufacturers.

Wait till they get the heretic treatment. It's going to be great.

US Tech companies have created $20 Trillion in stock market value on top of plenty of OS stack. They will do fine with commodity intelligence.

In fact, there are no other organizations in this world that is well suited to leverage scaled intelligence than Silicon Valley and great American companies

It's the exact same playbook Silicon Valley uses. Subsizide, lose piles of money, capture market share, recoup investment.

They've done this in other industries like solar panels, chips, and EVs. This is no different.

Anyone else think the AI environmental backlash is astroturfed?

I keep looking at the numbers. The power use numbers are not that problematic. Ordering a burrito on DoorDash uses more power than a few days of heavy AI use. The water argument applies to some locations, and is mostly a local governance problem... if the data centers are using too much water, it means they are not being charged enough for that water. Charge them more and they'll push toward closed loop cooling.

Yet the visceral pile-on here is so extreme, it feels fake.

One thing I've learned after 40 years on this planet is: propaganda works, and much of what a large fraction of people believe across the entire political spectrum (left, right, anything else) is there because someone paid to put it there. It's depressing but it's true, and it makes sense. Propaganda is an asymmetrical attack on human cognition and discourse, and in information security the attacker always has an easier job. Crafting viral bullshit is orders of magnitude easier than fact checking. On top of this, humans are busy and don't have time to fact check and logic check everything they read. As a result, much of what we believe is "sponsored content."

People get mad when you talk about this because everyone wants to believe they're too smart to fall for propaganda.

In any case, the US AI labs deserve to lose for their stupid "safety" regulatory capture monopolization push, which ended up blowing their own feet off and handing the lead to China.

The purpose is the same as that of all Putin-Xi-Khameni geopolitica: destruction of any democratic alternative to cults of personality.

> most effective way to debase American frontier labs

You're not going to debase the frontier labs through distillation.

Think of those poor billionaires, I feel terrible for their awful plight!

Both Qwen 3.8 and the latest Kimi release were announced at this conference

I know this is a bit cliche but I wonder how much headroom there is in the lower parameter count range. Is there any good reason to believe there is a lot of headroom or there is not? I suppose I'm just wondering if this wave of nearly Fable class models will be runnable on ~$10k worth of hardware at reasonable speeds in the near future.

You can read his speech here: https://www.xinhuanet.com/politics/leaders/20260717/72728b6f... He mentioned open source as one way to stimulate innovation and development, that's all. Also pay attention to the part where he says that misuse needs to be prevented. If unsupervised access to LLMs becomes perceived as undermining state control, no more open weights for you.

That conference, WAIC, is ongoing. Tomorrow (July 20) is the last day. Qwen 3.8 was announced AT the conference and so was the latest release of Kimi. That viral video of robots boxing was also from this conference and so was Xi Jinping's announcement of WAICO.

Doing "almost exactly the same thing" is fubdamental to competition and capitalism. The ones doing it better will survive, that's how we improve.

About climate, I think you overplay it. China is already investing heavily in nuclear, and we should be doing the same.

yep. kimi 3 just because the open source GOAT.

Distills don't take months to train, they take weeks. Distills are very easy to train.

> the 122B version of 3.5

Yeah, this is what I'm holding out for, the NVFP4 variant of 3.5 122B is blazing fast with reasonable quality and even with max context fits perfectly within 96GB.

SO MUCH THIS. I have Strix Halo with 128GB RAM and was a large and fast model like 122B A10B. Here's hoping!

Absolutely. There's a glaring gap in the space for something about the size of Nemotron Super or just under, but actually ... competent.

The fantasy is a 100B or 80B model, but MoE and highly tuned for coding.

Indeed! That would be the sweet spot for my 2x3090 rig

> I'm afraid that small capable models have been left in the dust

I wholly disagree. Rather than going the "everything is a claude code skill" route, I've been hacking together purpose-built harnesses for all sorts of tasks, and in that environment a wee little baby model can do some really useful things. You end up burning lots of tokens making the thing, but then all that investment comes back when the resulting tool works perfectly fine on a dinky little model that fits on my 3060 Ti.

Google makes Gemma 4 31B QAT; that's not a small lab. It's one of the better models out there for consumer hardware. Allows me to run it on a 7900XTX with 64k context.

nvidia and amd don't give a flying fuck about end users right now while they can milk triple digit markups from infinite money VCs via data center GPUs.

someone will keep putting out consumer level models. Once you have the larger models, you can derive the smaller onces.

Europe will definitely be interested in democratizing these things if China starts losing interests; from there, there'll be more countries looking to keep their citizens entrained in their own Country's infrastructure.

It'll especially be true if the memory cartel keeps prices high and NVIDIA tries to gouge higher memory models.

It's an arms race everyone can join because PC hardware was mostly democratized in the last decade.

there's been a lot of research about reducing models by taking out layers; there's also using it to train smaller models by optimizing parameters.

I dont see most model building as anything more than a pig at a slop troth, despite the level of sophistication; they're still rarely pruning the input beyond random sampling.

I run a 2x 3090 rig, but a single 3090 already provides a great experience at a reasonable quant and context size. On a single card I used to run Qwen_Qwen3.6-27B-Q4_K_M or similarly quantized 35B MoE at 65536 context size

It's useless to talk about models and harnesses without context and method. Depending on how you use the model and what the model is used for, experience may vary drastically. Also, different models with different harnesses require different approaches.

I've been using https://gitlab.com/gabriel.chamon/orisun which is my own simplified methodology, for coding web apps in python and elixir and have been very successful using qwen3.6 27b Q4 locally with help of larger models for architecture, so I get very suspicious when people talk how useless larger models are. They are either using it for a domain that models don't perform well or just not using it right.

  > At 10% of regular price it is a steal so far.

Here standard plan has been discounted to $18.00, from $25.00/month.

If by Opus you mean Opus 4 and not Opus 4.8, then sure.

Can we please include information of what languages we use when making claims like these?

It makes a huge difference if you're writing Javascript/HTML/CSS, Python, or C++/Rust.

Also the application type matters, e.g. user interfaces or scientific computing.

What the difference between your experience and https://news.ycombinator.com/user?id=5701652400? ‘s?

Such diametrically different ones.

Are these different models or different efforts for thinking (internal back and forth review) using the same model?

so Deepseek 4 Pro cannot go on own sessions for too long.

I delegate small-medium tasks: refactors, summaries, research, writing tests + have very good codebase already + extensive history / architecture / docs / linters. so it picks up and does decent small-medium scope work. it is fast, accurate, cheap. does exactly what I want directly and does not waste time nor tokens.

definitely not "implement me complex greenfield project".

My 2 weeks with DeepSeek V4:

Pro is ~50% more expensive than Flash.

Both need babysitting.

Plan, split in small tasks, give it docs, types, tests, linter, best practice examples, etc.

Always start a new session when starting a task.

Do regular manual sanity checks, and tell it to find issues in the codebase.

I pay like $1,50 per day for Pro.

A local AI is not about cost. In fact you will likely pay more for it than with most providers. Just look up the advantages of having access to a technology like this that can be self hosted

> Anthropic should not have bugged their knowledge distillation attacks.

It is like one of Pizzaro's men crying that someone have stolen his precious golden dublons

As Lenin have said - "Loot the looters" (Russian: Грабь награбленное)

I like that v4 flash is so fast! I run it on both FireWorks.ai in the US and bought some tokens directly from DeepSeek as an experiment. I only work on Open Source projects, so I don’t have to worry about my work being used to train models - I welcome AI’s being trained on my open content books and code (but not my conventionally published books: I am a party to the copyright suit against Anthropic).

> it usually is good enough for most tasks

The model is fantastic. And costs almost nothing. The only problem I see is that they will train on your data.

There are zero-data-retention providers of DeepSeek models, of which I have used openrouter (with zdr guardrails), and fireworks. But these are 3x to 5x more expensive than directly using DeepSeek, possibly due to poor caching. Thats the price to pay for zdr.

Who do you buy DeepSeek from?

I bought it through OpenRouter and used it with Pi agent.

The model was good, but there appeared to be a pricing glitch or something, because it burned through $50 in under an hour on pretty trivial stuff.

Pi agent claimed it only used like $1. OpenRouter claimed differently and said I used all $50.

It doesn't matter if it's cheaper, specially if it consumes more resources to do the same task as the competition

Besides, in a few days, they'll change their pricing, doubling it during their peak hours, so, realistically:

- It will be 2x more expensive if you live in their time zone

- It will be 1.5x more expensive if you live in a time zone that is adjacent to theirs

- It will be the same price IF you use it while they sleep (during offpeak hours)

It's still cheap, but the price/performance ratio is not that good

DeepSeek V4 didn't produce the same impact as V3, and Huawei dropping the ball is making it worse

They had promised massive price cuts for July, so now (Huawei chips), but they had to rush the cuts because lack of momumtum (they advertised them as promotion), and are now backtracking by introducing this peak hours pricing

Trump decided to help them a little by allowing them to buy more NVIDIA chips, so what exactly is China's role in all of this?

We are supposed to blindly pat them in the back while praising them, all while handing them over our data? I thought they were dangerous competition threatening our model of society

Over Dario's dead body.

At least Sam have us a nice model last year with the gpt-oss family.

If he bans them, they'll just show up as torrents. Or we'll download them from Chinese hugging face.

Different quants, but I've been having success with: https://unsloth.ai/docs/models/qwen3.6 I do llama.cpp

This is the sort of unmitigated pedantry thats the real problem. Everyone makes typos and errors of this nature. Feymann and Hemmingway both did. You deliberately chose this level of error multiple times when you chose to not put an apostrophe in youre and failed to capitalize proper nouns

Come off that high horse

Well, I think Chinese doesn't really have any grammar most of the time.

It's always fun to see what works for others, because for my work it'd have to be Sol > Fable. Fable makes too many mistakes.

Coordinating agents though? Fable any day.

100% this. There's currently this [1] submission that hasn't gained much attention, but is really important. In this [2] incident report from HuggingFace, they talk about detecting an attack and not being able to analyse the logs / IoC with API models because of guardrails. If not even highly regarded reputable companies can't sort out access to SotA models for blue team use, the raw capabilities don't matter. They're useless paperweights (hah!), and nothing else. Having to resort to open models is insane!

[1] - https://news.ycombinator.com/item?id=48965243

[2] - https://huggingface.co/blog/security-incident-july-2026

Tell that to my colleagues. Despite Sol getting the attention, Fable is really starting to have an impact on mathematicians right now. It has unbelievable insights in a lot of cases that can rapidly speed up progress.

And sol is much more reliable as a agent for doing work. Fable sometimes just goes on wild flights of fancy.

True. It was more of an open letter, with hopes that the Qwen team sees the comments in this thread.

dont we all deem the ability to improve large models as the defacto capability to produce small ones?

At 1.5 bits per weight it'll still be over 500gb - that's still not running on consumer hardware.

Best case they release smaller models. 120b class of qwen 3.8 would be incredible - it fits on device for those serious about AI, but without millions of dollars in hardware for terabytes of VRAM

I think AI will evolve the same way computers did. We're somewhere in the 80s-90s timeline of the evolution. My prediction is that on-device models will have excellent tool-calling, reasoning, and general skills, but the domain-specific knowledge will be retrieved on-demand from vendors like Google. Rather than downloading models, each device will have a hardware component with weights baked into silicon for maximum efficiency.

The U.S. has ~350 million people.

Anthropic and OAI can piss and moan all they want - limiting the U.S. to only their models would hurt the U.S. economy in myriad more ways than the failure of a couple of companies that scaled too quickly. If they get that outcome, the rest of the world would simply keep moving forward with access to open models and tokens at pennies on the dollar.

I'm not sure if corporations will be willing to allow LLMs to go the way of EVs.

Then again, all of Chinese models are open. And DeepSeek even publishes research papers alongside their models that go in depth into the methodology. I guess there's not much stopping USian companies from copying

Is there an iOS app for using Qwen?!

What's been your experience using these models? In my experiments, while it is true that the models are less likely to outright refuse to answer "sensitive" questions, they are still very resistant to actually respond in a meaningful / useful way.

You still don't know what's going on in there.

Deepseek is one of the worst in terms of hallucination rate according to artificial analysis' benchmark: https://artificialanalysis.ai/?omniscience=omniscience-hallu...

Deepseek V4 pro is a heavily undertrained model, they only trained it a bit more than the small version and that small version is 6-ish times smaller. Ive found that Flash is absolutely incredible as a workhorse for wide scale agentic nonsense, but Pro is a bit undercooked and really goes on strange tangents very often.

I have seen occasional weird behavior that I guess could be attributed to hallucinations, but for security auditing, DeepSeek v4 Pro is among the best models I've tested, competitive with Opus 4.8 and GPT 5.5 (MiMo and GLM also did well, Qwen 3.7 Max was below all of those, though only barely), and at an order of magnitude lower cost per task.