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Chinese model is good enough and cheap.

i've a Github copilot yearly subscription. Microsoft recently changed their billing to based on token. i'm still getting billed per premium request but GPT 5.4 is now 6x compare to 1x before.

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It's going to be an issue when China ends up scaling faster as well. Faster tokens, faster clusters, qat models, fp4, it's getting scary.
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Issue for who?
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Issue for any country that is not China. A single country getting the most AI tokens business would be generally bad for global economy. Hoping against hope that this business gets globally distributed and there is a healthy marketplace competition overall
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For uncle Sam Altman.
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American Politics and the far right.
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I'm kind of poor so I have been trying to use DeepSeek v4 Flash, GLM 5.1 etc. as much as possible recently instead of Claude or GPT.
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You would do us all a service by telling us how your experiences of that have been.
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I would say about 35% of the time I run into problems and eventually give up and go to GPT 5.5 and it much more efficiently handles the original task. Then I see the token costs going up and it motivates me to continue trying the open source ones.
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I used Opus 4.6, then downgraded to Sonnet, then to GLM5/5.1. GLM is as good as Sonnet. I recently started using Opus 4.8 again and GLM is not close to that.

30 day eval for each.

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I see bigger problem with model inconsistency. You never know whether Anthropic will route your request to a cheaper model for the price of Opus. So you can never estimate how much a task will cost, because you might have to restart several times and pay for each attempt. Then you have to prompt models to gauge whether they are real or impostors which also adds to token usage.
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> You never know whether Anthropic will route your request to a cheaper model for the price of Opus

For non subsidized plans? Pretty sure they'd need to put this in ToS, or law suites would have followed by now.

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How can you prove it?

Sometimes Opus just gives me a rubbish session.

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no they 100% use MTP with a cheaper model alongside opus, and it would infact be unprovable if they just sometimes switched to auto-accepting everything from the MTP. its true that if they did anthropic would need to hide that they do this, so its probably not a huge deal
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Another problem is that US models are all closed source, and if you're a large corporate you may not want your org to be held hostage by OpenAI / Anthropic.

I genuinely don't understand what moat these US model labs have. If they're saying recursive self improvement is just around the corner and Chinese labs are only slightly behind the leading US models, what moat does the US labs have? Are the US models going to recursively self improve better than the Chinese open source ones or something?

I might be completely wrong about this, but if I had money in OpenAI or Anthropic I'd be pulling it all right now. I think the chance of them going to near-zero over the next few years is very significant.

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Their moat is cash to pay politicians to regulate away competition.
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> you may not want your org to be held hostage by OpenAI / Anthropic

Or Google. I'm working with multiple customers right now that are very pissed at Google for deprecating Gemini 2.5 Flash, canning the GA release of 3.0 Flash and now have to decide whether to bite the bullet of the 5x price increase for 3.5 Flash or switching providers. Quite a few of them will likely fully pivot to open models.

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I'd be curious if any of your customers have tried 3.1 Flash Lite. It's cheaper than 2.5 Flash, and in my experience with the free tier, quite an upgrade in terms of quality of response. My suspicion is that Google is killing off the old models because they aren't a good value for the customer or for themselves.
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I think they are racing because the first ASI will 'win', preventing others, of course we won't be able to bake the right goals into it though.
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i dont think its going to automatically prevent others. super claude might understand why diversity is important. if were talking sci fi scenarios the most likely one is probably overwatch (multiple independent ais with gray ethics and complicated relationships) more than skynet.
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I wonder what are the economics driving these pricing decisions? Are the Chinese companies just subsidizing their models to a greater degree than the US, or is this an emergent property of energy policy between countries?
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For one, they invested in infrastructure. They can build fast and efficiently. They can provide power, they can provide cooling. Even if you just make roads better you make everything more efficient. Plus level of standard education. It all compounds.

On HN China is seen as a cheap labor copycat. This used to be a fair approximation at some point in the past. In my opinion China is getting ahead of everyone else much more than US used to be.

SF is a beautiful thing in the US, vast power and wealth comes from there. Smart people collaborating communicating and building fast and with excitement. China did SF kind of thing for many different sectors in many different places.

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Throwing out another factor: Chinese companies have been banned and/or limited from buying nvidia, and turned to local companies for their hardware. I haven't actually seen pricing/benchmarks comparing Chinese AI accelerators, but it wouldn't surprise me if that also worked out in their favor as well.
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And, possibly, state subsidies at every level.
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Lower cost of labor, lots of under the hood optimizations (e.g. cache hits for DS), many of these companies have existing infra (fewer upfront costs for deployment), etc
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China isn't that cheap for labor. And if you think the guys in Z.ai or xiaoxiao aren't the exact same guys from Tsinghua, Peking, MIT, Stanford, CMU, etc. and pulling in amazing salaries you'd be wrong.
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I'd assume there's more to the cost of labor than the salaries of the elite folks who do the R&D, but fair point
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Maybe not being led by a sociopath also helps.
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