alt Hacker NewsOpenAI had to cut costs because of Anthropic. I also do not trust the benchmarks when it comes to models anymore. I have tried both Claude and OpenAI models and while it is true that the 5.6 series is smarter than Deepseek (at the time i tested it against 4.0) at that price it is still not worth it and sometimes randomly refuses to do tasks or stops midway etc.
Do also remember China is this far in the AI race despite all chip restrictions from America. If they were in equal standards I truly think Chinese models would have long surpassed American ones. Also would like to remind how Anthropic CEO is being hostile and blaming Chinese models with distilling meanwhile their own models claimed to be Qwen¹ and their stance against open models is negative² and they still keep blaming China for it.
1- https://news.ycombinator.com/item?id=48671252
2-https://www.anthropic.com/news/position-open-weights-models
Replies
> Also would like to remind how Anthropic CEO is being hostile and blaming Chinese models with distilling
Why wouldn't he? If there really was 25,000 accounts breaking ToS any CEO would at minimum be upset. Evidence of Claude distilling qwen would be damning but that a) makes no sense b) doesn't exist afaik.
Not sure about that.
Given the difference in compute, it seems plausible.
However, the researchers at the US labs are surely no less talented, and they have better access to hire talent globally.
They too have to serve their models efficiently at a large scale, and with current capacity constraints this must be a top priority.
So first it’s “Chinese companies cut costs, and you’d never see American companies do that”, and then when it’s pointed out that one of the leading American labs literally just did that, it’s “yeah, but they had to because of competition”.
What do you think is motivating the Chinese labs, benevolence?
> If they were in equal standards I truly think Chinese models would have long surpassed American ones.
Limitations often lead to creativity to overcome them. The Chinese AI labs have had to focus much more on efficiency so they got good at it. Meanwhile breaking new ground is often harder than replicating it. So even if they had matching compute it's not a given they'd be better.