Earlier quoted context omitted.
Let's see how long it takes before the big US AI companies start lobbying to outright ban use of Chinese AI, even the open source / local models. For "national security" reasons, of course.
> Let's see how long it takes before the big US AI companies start lobbying to outright ban use of Chinese AI, even the open source / local models. For "national security" reasons, of course. Already do on EVs.
DeepSeek v4
611–620 of 1001 posts
Re: DeepSeek v4
#612Something is odd with this model, their blog posts shows REALLY good results, but in most other third-party benchmarks, people realize it's not really SOTA, even bellow Kimi K2.6 and GLM-5/5.1 In my tests too[0], it doesn't reach top 10. One issue, which they also mentioned in their post, is that they can't really serve well the model at the moment, so V4-Pro is heavily rate-limited and gives a lot of timeout errors…
Hmm, the Flash performs significantly better than Pro in the benchmark? That's very strange; could rate limiting cause that?
Re: DeepSeek v4
#613Re: DeepSeek v4
#614The incredible arrogance and hybris of the American initiated tech war - it is just a beautiful thing to see it slowly fall apart. The US-China contest aside - it is in the application layer llms will show their value. There the field, with llm commoditization and no clear monopolies, is wide open. There was a point in time where it looked like llms would the domain of a single well guarded monopoly - that would have…
As much I apprecite the sentiment, I think it is too early to declare that the well guareded monopoly is over. Yes, these models have answers, but don't expect all the large enterprises to switch to these models. The other aspect is scaling to serve these models will need a lot of time even if Huawei succeeds. Not all the Governments trust China and there will be a lot of resistance to work with these models eventual…
You might be underestimating how significantly cheaper this is and how much people care about price.
Walmart is a horrible company owned by horrible people and yet it’s cheap so it dominates.
If the quality really is in the Opus 4.6 range (considering how bad 4.7 is), then it’s a pretty big deal.
Re: DeepSeek v4
#615Open Source as it gets in this space, top notch developer documentation, and prices insanely low, while delivering frontier model capabilities. So basically, this is from hackers to hackers. Loving it! Also, note that there's zero CUDA dependency. It runs entirely on Huawei chips. In other words, Chinese ecosystem has delivered a complete AI stack. Like it or not, that's a big news. But what's there not to like when…
My guess is Chinese govt is going to mandate that labs switch all future training and inference to Huawei. DeepSeek has shown it's possible. Once they are done, the rest of the world is going to be buying Huawei! I for one can't wait for a cheap Huawei GPU!
Re: DeepSeek v4
#616Earlier quoted context omitted.
The players barely ever change. People don't have problems following sports, you shouldn't struggle so much with this once you accept top spot changes.
It is funny seeing people ping pong between Anthropic and ChatGPT, with similar rhetoric in both directions. At this point I would just pick the one who's "ethics" and user experience you prefer. The difference in performance between these releases has had no impact on the meaningful work one can do with them, unless perhaps they are on the fringes in some domain. Personally I am trying out the open models cloud host…
Their financial projections that to a big part their valuation and investor story is built on involves actually making money, and lots of money, at some point. That money has to come from somewhere.
Re: DeepSeek v4
#617The paper is here: [0] Was expecting that the release would be this month [1], since everyone forgot about it and not reading the papers they were releasing and 7 days later here we have it. One of the key points of this model to look at is the optimization that DeepSeek made with the residual design of the neural network architecture of the LLM, which is manifold-constrained hyper-connections (mHC) which is from thi…
> this is why Anthropic wants to ban open weight models Do you have a source?
Annecotal, but I saw a tweet from someone who interviewed at Anthropic, and was explicity rejected because of cultural mismatch because they were not against open weight models.
It's hard not to see Anthropic's messaging of "this tech that we're pushing on you is going to take your job and maybe kill you" as being about anything other than regulatory capture, with the goal of the government shutting down competitors.
I think OpenAI and Anthropic are both really in a tough spot - spending so much on what is becoming a commodity product for which neither seems positioned to be low cost producer. Maybe a bit like the UK-France channel tunnel project where the product itself is a success but a bloodbath for those who invested to build it.
Re: DeepSeek v4
#618The incredible arrogance and hybris of the American initiated tech war - it is just a beautiful thing to see it slowly fall apart. The US-China contest aside - it is in the application layer llms will show their value. There the field, with llm commoditization and no clear monopolies, is wide open. There was a point in time where it looked like llms would the domain of a single well guarded monopoly - that would have…
I've been baffled watching America double down on the same strategy even when it failed to produce results They sanctioned the hell out of Huawei and now Huawei is bigger than ever America is just not able to digest the idea that another country can be as good, if not better, at innovation
I'm no huge fan of America, but claiming China is as good or better at innovation is asinine.
It costs 100-1000x less manpower, money, and time to hug the heels of innovators than to actually pioneer. Say what you will about America but they absolutely lead technological innovation and it's not even remotely close.
Re: DeepSeek v4
#619Earlier quoted context omitted.
It honestly has all kinda felt like more of the same ever since maybe GPT4? New model comes out, has some nice benchmarks, but the subjective experience of actually using it stays the same. Nothing's really blown my mind since. Feels like the field has stagnated to a point where only the enthusiasts care.
For coding Opus 4.5 in q3 2025 was still the best model I've used. Since then it's just been a cycle of the old model being progressively lobotomised and a "new" one coming out that if you're lucky might be as good as the OG Opus 4.5 for a couple of weeks. Subjective but as far as I can tell no progress in almost a year, which is a lifetime in 2022-25 LLM timelines
Another annoyance (for more API use) is summarized/hidden reasoning traces. It makes prompt debugging and optimization much harder, since you literally don't have much visibility into the real thinking process.
Re: DeepSeek v4
#620I just did some quick testing on my own benchmark that tests LLMs as customer support chatbots, and found out that deepseek-v4-flash (scored 90.2%) was better than qwen3.5-27b (89%) and qwen3.5-35b-a3b (89.1%) and roughly equal to gemini-3-flash-preview (90.5%), but deepseek-v4-flash had the lowest cost of all of them by far. Half the cost of gemini-3-flash and an order of magnitude less cost than the qwen models.
Have you noticed the deepseek-v4-pro performing worse than deepseek-v4-flash? It performed even worse than qwen3.5-27b. I found it surprising and I'm wondering if there is a bug on my software because I had to implement sending the `reasoning_content` otherwise the API failed with BadRequestError.