Comment by throwaw12
16 hours ago
Qwen3.8-Max is the first in Qwen-Max series to be open-weight as well.
Kimi K3, GLM 5.2 and now Qwen3.8-Max - open weight models.
DeepSeek V4 Flash outperforming Gemini 3.1 pro, probably DeepSeek V4 Pro update is also coming soon
Chinese labs are cooking very hard. US closed weight labs are probably hard time to resist not calling Washington DC for more AI regulations
Kimi K3 is more like "weights available" in that you can download and use them but it is under a custom license that has a bunch of limitations where you have to pay Moonshot for doing some stuff. GLM 5.2 on the other hand is plain old MIT.
Not sure how Qwen3.8-Max is going to be licensed, hopefully it'll be Apache like the smaller ones.
You can do whatever you want with the model within your own organization. If you use it commercially—either as a model-as-a-service business or in a very large-scale product—you should check the additional license terms, which go beyond MIT. My interpretation is that Moonshot cares about the exact inference behavior and accurate representation of their model or derivatives, and perhaps also about capturing some additional value despite their own GPU limitations, so the extra license terms focus on those large-scale commercial deployments.
Considering that very few orgs are going to be able to host a 3T parameter model internally, chances are most deployments would be subject to these restrictions and require a separate license from Moonshot.
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Kimi’s policy is extreme reasonable.
US AI companies are already sweating and 100% pressuring the Trump administration for more anti-Chinese regulation, since there have already been talk of Trump considering banning Chinese models. There's however another push back from the startup industry urging them not to ban it, since it will stifle the innovation. In other recent news OpenAI also greatly cut their model prices, 20% for 5.6 Terra and 80% for 5.6 Luna, to stay competitive.
I’ve seen comments on HN saying how bad this is for the Chinese model developers since the cheaper option like Deepseek Flash are not longer as price competitive to justify the hassle/risk/lack of multimodal… but isn’t this a gigantic red flag for OpenAI/Anthropic at their current valuations?
Sure, it’s just the lowest end for now, and the enterprise money is at the top of the market. And there’s protectionism/enterprise lock-in/etc that complicate things somewhat.
But still, if the US AI labs ever tap the training brakes for a millisecond, the “inference is still a money maker” argument seems to evaporate when they’ll immediately have to fight a race to the bottom until margins are virtually nothing.
Or if the benchmaxing “line goes up” FOMO mindset starts to lose its luster and companies find their individual niches for productive use of AI and stop bothering with all the latest and greatest churn for top dollar.
Which might be even worse if it means the training arms race is still ongoing but neither Anthropic or OpenAI want to be the first to “lose”. While the marginal value of each new model training run keeps decreasing and enterprises signal they’re more concerned with cost reductions than solving ARC-AGI-7 puzzles.
Huh? V4Flash is still incredibly price competitive. The new version is right around GLM 5.2 and maybe slightly worse than opus 4.8 depending on which benchmark you use while being much cheaper(even factoring how most US zdr providers charge 10x Deepseek’s api caching price). K3 is also a tad behind fable/sol while being alot cheaper