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State of LLMs in China and the US, plus JDPON Don (hexbear.net)
submitted 2 weeks ago by piccolo@hexbear.net to c/technology@hexbear.net
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I've been meaning to try to write a bit more, and unfortunately I can't put this on a blog post attached to my name if I wish to be employable in tech in the US, so I figured I'd write a bit of an effortpost about the state of LLMs in the world. I've been learning a lot about LLMs lately (don't ask me why the things that become hyperfocuses for me become hyperfocuses) and I figured that some people here might be interested in learning more.

I was inspired to post by this article posted to /c/news, and all I have to say about this is JDPON Don back at it with another banger. In all seriousness, I think this is very good for the state of Chinese AI, which is already very good.

For those not following recent LLM updates (very understandable), the TL;DR is that a lot of new open-source models coming out of China are really good, and pushing the state of the art. Generally, they're still less good than the best closed-source models from the US (Claude in particular is the best currently), but they're much much much cheaper and honestly getting quite good. Plus, they seem to be giving US-based AI companies a good scare, which is always fun.

For reference, the best models from US firms in general are Claude (by Anthropic), Gemini (by Google), and OpenAI's models, though it seems like GPT-5 was a bit of a disappointment. My bet's on Anthropic in general for all of the closed source models - they seem to be killing it, in general, and have some very interesting research about understanding LLMs. This is a very cool paper from them that covers trying to understand how LLMs work with a quite novel model of it that I think could give a lot of explainability to how they operate.

[Side note: I think it's quite scary that leading AI research firms making leading AI models generally don't know how they work or how to reason about what they're doing, especially given that they can tell when they're being evaluated and notably suppress the "scheming" part of them when they think they're being tested on scheming.]

Anyways, back to China. One of the most significant LLMs to come out of China in the last while was DeepSeek-R1, which was able to match or outperform OpenAI's state of the art model o1 (the best model at the time) on most benchmarks. R1 completely changed the metagame - it changed the dominant type of model for LLM (dense LLM vs Mixture-of-Experts) singlehandedly, and scared OpenAI into dropping its prices for o1. And DeepSeek did this while there is a huge GPU shortage in China because of the export controls. And they did this while spending only $5.5M USD, compared to the estimated $100M to train GPT-4 (which is less powerful than o1). This is absolutely bonkers, and there's a reason this caused the stock market in the US to dip for a bit.

Now, R1 is not quite as good as the closed source models, despite the benchmarks. In particular, its English flows less well and it struggles with some types of queries. But it's crazy that a company came out of nowhere, trained a new type of model for 1/20 the cost of OpenAI training a worse model, released it for free, and completely changed the meta. And it also reasons, which isn't new, but it is a particularly good reasoner, and I think they got a lot of things right with how it works.

Anyways, R1 is old news now. There are a billion new open source models coming out from China now. Some notable companies include Alibaba (Qwen), Moonshot AI (Kimi), and Z.ai (formerly Zhipu AI; GLM). People on reddit-logo say that Qwen3 Coder and Qwen3 235B A22B (both Thinking and Instruct) are very good - for my use cases (mostly programming), I much prefer GLM 4.5. I was impressed with Qwen for questions about code, but I found it to be less good at actually writing it, for the most part. YMMV, though, I think this is a somewhat unpopular opinion. But anyways, it seems like each week a new top open source model appears, from China. Far and away they are leading the open source efforts. And even if they aren't quite as good as Claude, Claude Sonnet 4 costs $15/million tokens of output, whereas Qwen3 Coder is free up to 2000 requests per day from Alibaba, and costs $0.80/million tokens of output, which is crazy cheap.

Another notable thing about Chinese open source models is that they are generally much easier to jailbreak than Western models, except for older less powerful open source models like Llama's and Mistral's models, which are also very easy. So you can get them to write all the erotic bomb making content you'd want (I'm happy to provide tips on jailbreaking if anyone would like).

Also, it seems that in the current market, companies in general are tripping over each other to give you free access to open source LLMs as each tries to become the place to get LLM access from, which means it's a really good time to be mooching access to these guys. Alibaba will give lots and lots of Qwen3 Coder credits, OpenRouter will give you 2000 free requests a day for eternity to a lot of good models if you at any point put $10 into their system, Chutes will give you 200 free requests/day for basically any open source model for a one time payment of $5, etc. Even Google will give you free access to their top tier model (though a pretty small amount per day) via Gemini CLI.

Anyways, my main point is that China is doing all of this despite a huge GPU shortage in the country. So if JDPON Don really wants to give them more access to Nvidia chips, it must be because he wants to boost their LLM market even further.

Thanks for coming to my Theodore lecture.

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[–] Xiisadaddy@lemmygrad.ml 26 points 2 weeks ago (1 children)

I just think its so funny how the company thats keeping its models closed source and is being outcompeted now by open source models is called OpenAI lol

[–] space_comrade@hexbear.net 18 points 2 weeks ago (1 children)

Didn't OpenAI start as a nonprofit of some sort and vowed to be open and ethical and whatnot? They dropped the pretense as soon as they caught a whiff of the money lmao.

[–] Le_Wokisme@hexbear.net 15 points 2 weeks ago

iirc it was a nonprofit that owned some for-profit shit and somehow that's legal.

[–] Assian_Candor@hexbear.net 22 points 2 weeks ago

Releasing open source models that outperform the closed source ones will collapse the US economy. This should be a national priority.

[–] joaomarrom@hexbear.net 14 points 2 weeks ago* (last edited 2 weeks ago) (1 children)

Interesting write-up, where do you get your info from? I'm also interested in the topic, both in the technical and political sense of the China vs. US AI race. Also, do you see anything in the horizon regarding people trying other ways to conceptualize "AI"? I mean, nowadays, for all intents and purposes, AI equals LLM.

I find that ironic because I'm firmly in the camp of those who do not believe that an LLM will ever be true AI or AGI or whatever the hell. If we ever do figure out AGI or superintelligence, whatever that might mean, it will NOT be an LLM. And I have a feeling that US companies are so heavily invested into LLM that this might be limiting the way they understand the very concept of AI.

I mean, what if Chinese researchers (because let's face it, a great novel breakthrough would most likely come from there) find out that there's another way to do AI that has nothing to do with LLMs and does not require GPUs? Just spitballing here, but if that were the case, then how would the US government and AI companies pivot, now that they're so heavily invested into this?

I feel like the gargantuan sunk cost of billions upon billions invested in one particular technology is at least partially driving this monotonic search for ways to make LLMs better and better, rather than branching off in some novel direction. We're at a point where NVIDIA stock prices are so central we're not even going to pretend to do something different.

It's as if instead of investing in making computers more portable and accessible, we all decided to develop computing by making increasingly larger versions of ENIAC. The industry has decided that the goal is to maximize resource expenditure, and bigger = better, and more than a little bit of that is, the way I see it, strictly a function of the economics of AI development, rather than technical aspects. Even state-of-the-art technology couldn't resist the Amerikkkan tendency to transform everything into another financial instrument.

Personally, I only stand to benefit from China's superiority in the AI race. There are two ways in which I use AI:

First, if I need to find something specific online, I'll sometimes go to ChatGPT and use its online search function to see if it ends up pointing me towards useful references, something that Google can no longer do most of the time. I don't do this often at all, and it's kinda helpful in that regard, but not very much. I'll also use it sometimes for grammaticality checks since I'm not a native English speaker, but I take the answers with a grain of salt... what if it's trying to suck up to me by saying my sentences are "not only beautifully crafted — they're very deep and meaningful"?

Second, and this is what I do with AI 95% of the time, is I use Deepseek to study Chinese, confirming everything it tells me with my native tutor, of course, which is why I'll gladly accept cheaper, more efficient Chinese models.

Edit: another thing I find LLMs to be useful for is to search for collocations. This is entirely unsurprising as a useful feature, since collocations are by definition a function of natural frequent association, and the entire concept of LLMs revolves around word associations.

[–] piccolo@hexbear.net 3 points 2 weeks ago

I get my info from a bunch of places, here are some of them:

  • Simon Willison's blog. He writes about LLMs in an interesting way, and has been consistently talking about security and best practices, which is refreshing when no one else talks about that. He seems a bit biased towards western models, but he still provides good coverage of a lot of model releases.
  • /r/LocalLlama - one of the best places on reddit for people talking about LLMs, particularly open ones that they can theoretically run on their own computer. However, most people there still use hosted LLMs via e.g. OpenRouter, which makes sense because not many people have the kinds of GPUs needed to run the largest of these models.
  • Gwern, AI Alignment Forum, and LessWrong. All of these have the rationalist bug and generally bad politics but they can be interesting to read. A lot of abstract philosophy about AI in general, plus crazy stuff like Roko's basilisk (don't look it up if you don't already know about it, it's a cognitohazard). Papers on AI safety are often linked here.
  • arXiv for technical papers, and once you find one interesting one it's easy to go down the citations rabbit hole to find 300 more.

Also, do you see anything in the horizon regarding people trying other ways to conceptualize "AI"? I mean, nowadays, for all intents and purposes, AI equals LLM.

Good question. I think that LLMs are definitely the dominant metagame currently. I think they will still get better, and I tend to agree that I don't think this will lead to AGI, but I also have no idea what will. I think Anthropic's research in understanding how the LLM "brain" works is very compelling and might lead to new developments, but I don't know what they are. Here's an essay talking about what might be the next improvement to LLMs: LLM Daydreaming · Gwern.net. I think this is very interesting and Gwern is good at predicting this kind of thing I think. But it also requires companies to be invested in a longer horizon of profit, which they're notoriously pretty bad at doing.

I also came across this article: Xi Jinping warns against China's overinvestment in EVs and AI, which seems potentially relevant. It's interesting that the US is saying nothing of the sort.

I mean, what if Chinese researchers (because let's face it, a great novel breakthrough would most likely come from there) find out that there's another way to do AI that has nothing to do with LLMs and does not require GPUs? Just spitballing here, but if that were the case, then how would the US government and AI companies pivot, now that they're so heavily invested into this?

Yeah, I really don't know. I mean I think that GPUs are likely in any future AI breakthrough because massively parallelizing computation is what they're good at, and they've been a staple of every kind of ML breakthrough in the last long while. Of course, massive parallelization doesn't equal AI, but it's hard for me to imagine an AI breakthrough that doesn't use massive parallelization.

I feel like the gargantuan sunk cost of billions upon billions invested in one particular technology is at least partially driving this monotonic search for ways to make LLMs better and better, rather than branching off in some novel direction. We're at a point where NVIDIA stock prices are so central we're not even going to pretend to do something different.

Yes, I agree for sure. I saw a hexbear the other day (sorry, can't remember who) saying that they didn't think it was a coincidence that the market shifted to LLMs right after the crypto bubble popped. There's a lot of GPU capacity that was freed up by that, which conveniently feeds perfectly into LLMs! And Nvidia is ridiculously overvalued as a company for sure. That being said, I think that even if LLMs are a bubble, it will be one more like the internet than like crypto - still overvalued but based on a fundamentally compelling technology, and it'll stick around even after the bubble pops.

First, if I need to find something specific online, I'll sometimes go to ChatGPT and use its online search function to see if it ends up pointing me towards useful references, something that Google can no longer do most of the time. I don't do this often at all, and it's kinda helpful in that regard, but not very much. I'll also use it sometimes for grammaticality checks since I'm not a native English speaker, but I take the answers with a grain of salt... what if it's trying to suck up to me by saying my sentences are "not only beautifully crafted — they're very deep and meaningful"?

Second, and this is what I do with AI 95% of the time, is I use Deepseek to study Chinese, confirming everything it tells me with my native tutor, of course, which is why I'll gladly accept cheaper, more efficient Chinese models.

Edit: another thing I find LLMs to be useful for is to search for collocations. This is entirely unsurprising as a useful feature, since collocations are by definition a function of natural frequent association, and the entire concept of LLMs revolves around word associations.

Firstly, your English is very good. I never would have guessed you weren't a native speaker.

Secondly, these all seem like good use cases of LLMs. In my experience, Claude and Gemini both have decent search tools, with Gemini's especially good for academic research. I'm curious to see open source models get better at search, but also that might just be a function of access to search infrastructure, which obviously costs money. I also haven't fairly assessed this yet I think.

Thirdly, using it for language-related tasks is probably its most compelling use case for most people. They're getting really good at writing and editing. ChatGPT really likes to use very obviously AI language, but you can get DeepSeek/GLM/Claude to generate much less AI-sounding content.

[–] iByteABit@hexbear.net 12 points 2 weeks ago (1 children)

I wonder how much of a driving force it is for LLM research that capitalists are trying to automate away software engineers, which seems to be one of the biggest if not the biggest advertised use cases of each LLM

[–] piccolo@hexbear.net 5 points 2 weeks ago* (last edited 2 weeks ago)

Well, I think that's part of it. I think that companies are probably trying to do that, but I think most software jobs won't be replaced by Claude for a long while. I don't know what people who work for OpenAI/Anthropic/Google think about this - maybe they think it's coming, maybe not. IME, they're good at writing code when instructed by a skilled engineer, but on their own, not very good. And the code they write is not always very maintainable. As someone who is currently unemployed but normally works in software, I've been using LLMs for brainstorming software design decisions for personal projects, and I find that they are good at "talking through" these kinds of things but less good at actually implementing them in a way that makes sense.

Some more reasons why LLMs are getting better at programming tasks:

  1. There's a ton of training data. This is separately frustrating to me because all of the open source code that people wrote for the greater good or whatever is now just getting hoover'd up into these closed source models (tbh I care less when Qwen or DeepSeek does this because they release the weights). In open source, there's a license called the GPL that says that any derivative work has to be open sourced and released under the same terms of the GPL, and I think this is a Really Good way to make copyright law work for the public interest. Of course, the Silicon Valley mindset of move fast and break things (especially the law, until you're big enough that the law doesn't matter anymore - see Uber, Airbnb, etc) doesn't give a fuck about this, and now LLMs are already too entrenched for anything to happen about this.

  2. A lot of the time, there is a concrete right or wrong in programming. It's a lot harder to evaluate how well your model works as a creative writer, but for code you can run the code and see if it does the thing you wanted it to. (Obviously there are other factors like code style and stuff but at least you have the baseline. It's also easier for an LLM to grade whether output code is good style than it is to assess if a story is good.) Most LLMs nowadays are trained significantly on "synthetic data" which means data generated by other LLMs, and doing this at scale means you don't have a human in the loop reading over the training data and grading essays or anything. It's computers all the way down.

  3. Reasoning is important, and programming is a very concrete task to train chain-of-thought style responses, e.g. DeepSeek. That's also why they are trained on a lot of math, e.g. AIME benchmarks.

  4. I think that GPT3 got a lot of gains from training on programming, in a way that generalized to other tasks. Somehow having all of that structured data in its training data made it better at tasks across the board, and that was one of the breakthroughs that led to the original ChatGPT. I think that the companies think that because it's easy to train on code, and it seems that training on code makes it better at other things too, it's the easiest path to more intelligent LLMs.

  5. Programmers are early adopters of technology and potentially willing to pay large sums of money. Claude Code costs $200/month, which is crazy. And people buy it. Because when you make $200k/year, if a tool helps you do your job 30% better, it's worth that kind of money. I think that this is a unique phenomenon - other high paying jobs like lawyer or doctor wouldn't adopt this kind of technology as urgently. Hopefully most people in these classes realize that LLMs hallucinate too much to be useful in the general case. They can be good at reading papers or documents and summarizing them, but that is a task that is done much easier than programming, so even if they were widely used for that narrow purpose it wouldn't justify a $200/month subscription. Like if ChatGPT can do it for free, or you buy their $20 tier and it can do that fine, who would pay $200?