Qwen3.8-Flash-Next与GLM-5.3-Flash发布
Today is a huge day for open-weight and local AI.
开源模型性能逼近前沿闭源模型，做本地部署或开源模型选型的同学务必关注，建议对比测试这两款新模型的真实表现。
Today is a huge day for open-weight and local AI.
今天对于开放权重和本地AI来说是意义重大的一天。
Qwen3.8-Flash-Next is a 125B MoE with another 51B n-gram embedding parameters, yet only 6B are active per token.
Qwen3.8-Flash-Next是一个拥有125B参数的MoE模型，另外还有51B的n-gram嵌入参数，但每个token仅激活6B参数。
It beats Claude Opus 4.6 Max on 8 of 9 comparable benchmarks, including SWE-bench Pro, CoWorkBench, GPQA Diamond and LiveCodeBench.
在9个可比较的基准测试中，它在8个上超越了Claude Opus 4.6 Max，包括SWE-bench Pro、CoWorkBench、GPQA Diamond和LiveCodeBench。
GLM-5.3-Flash is even larger: 320B total, 18B active. It scores 84.3 on Terminal-Bench 2.1 versus 85.0 for Opus 4.8, while beating Opus on DeepSWE, AutomationBench and GDPval-AA v2.
GLM-5.3-Flash更大：总参数320B，激活参数18B。它在Terminal-Bench 2.1上得分为84.3，而Opus 4.8为85.0，但在DeepSWE、AutomationBench和GDPval-AA v2上超越了Opus。
MIT licensed, natively multimodal, 1M context.
采用MIT许可证，原生多模态，支持1M上下文。
One important distinction: 6B or 18B active parameters does not mean 6B or 18B storage. The complete weights still need to be stored.
一个重要区别：6B或18B激活参数并不意味着6B或18B存储。完整的权重仍然需要存储。
So “local” here means a serious workstation or local server, not an ordinary laptop.
因此，这里的“本地”意味着需要一台高性能工作站或本地服务器，而不是普通笔记本电脑。
The fact that we now have models operating at the level of Opus 4.6 - or even Opus 4.8 - a level of performance that was state of the art only a few months ago and can now, at least theoretically, be run locally, should serve as a wake-up call, especially for U.S. frontier labs.
我们现在拥有性能达到Opus 4.6甚至Opus 4.8水平的模型，这种性能在几个月前还是最先进的，现在至少理论上可以在本地运行，这应该是一个警钟，尤其是对美国前沿实验室而言。
Not only because the gap continues to narrow, but also because there appears to be enormous demand for open AI. In that sense, what has been released here is genuinely welcome.
不仅因为差距在持续缩小，而且因为对开放AI的需求似乎巨大。从这个意义上说，这里发布的成果确实值得欢迎。
So yeah, great day for everyone!
所以，是的，对每个人来说都是美好的一天！
更进一步：量化金融体系
看懂新闻只是起点——沿量化金融路径，把它变成能交付的工程能力