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开放重量型号至少从表面看比美国专利型号便宜得多。
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你可以在笔记本电脑上运行中国AI模型。 这些模型是强大的，有能力的，在高需求中，帮助解决一个商业问题：花费在AI符上。
这正在改变人工智能服务的经济学，并为前沿模式造成竞争问题。
在其星期一的IPO peopleus 发布中，Anthropic 称认为随着多种型号的出现，"客户可能得出结论，边疆的增量性能收益不能成为增加成本的理由".
中国AI模型占了OpenRouter上报道的代号量的一半以上，按其全球统计，在10月25日结束的一周内。 大约一年前，在网站每周使用图上命名的9个模型中，只有一个中国模型出现。 OpenRouter通过单一API提供多个AI模型的访问。
开源企业软件供应商Red Hat为自身内部使用，正在试制自办版的GLM，是北京Z.ai的开放量产模式。 中国科技巨头Alibaba受欢迎的Qwen型号也以更小的规模提供。
工程师最初对于开放量级模型是否和专有的前沿模型一样有能力感到紧张，称 Stephen Watt，杰出的工程师和红帽CTO办公室副总裁。 但他们发现他们“足够好”，称。
Watt 称开放重量型号，主要来自中国，在某些使用型号中达到了与Anthropic Opus 4.6相仿的功效水平。 他的称工程师对远离边疆模型感到担忧，但Red Hat的用法数据表明开放式模型对于许多任务来说是足够的。
但过了一段时间，你就会疯狂地坚持下去，因为这只会消耗你所有的利润
业务用户，工程师和开发人员可以根据AI模型的能力分配工作量。 它们可以在内部，在封闭环境中托管模型。 但有些人对使用中国模型感到担忧，尤其是因为地缘政治刺激了对美国所开发模型的兴趣。
对中国模式的犹豫不决，在存在的地方，通常是基于地缘政治的顾虑。 这正在形成对西方所建造的开放量级模型的倒置需求，例如由Meta和Reflection AI,Watt 称所开发的。
DataCamp是大约6 000个组织使用的AI学习平台，它的AI导师分析了最近一个月约140万小时的学习。 它一直在评价Gemma 4,Google开放量级模式用于辅导。 中国模式对于一些政府和企业用户来说可能是个问题，称联合创始人和CEO乔纳森·科内利森。
但目前开放重量模式的主要限制是基础设施。 运行开放量级模型的提供商无法以所需的速度或规模交付它们，Cornelissen 称。 后期没有竞争力，他称。 有些国家速度快，但缺乏能力，而另一些国家的反应时间不足。
目前，DataCamp正在从Anthropic向OpenAI移动，他认为Cornelissen 称成本较低。 它没有将其导师转移到一个开放量级的模型。
乔纳森·斯蒂尔(Jonathan Steele)是芝加哥Beermann LLP的合伙人，也是网络安全咨询Stele要塞的创始人，在当地的MacBook Pro上使用Qwen模型进行计费和其他敏感任务。 他还将Claude和ChatGPT用于其他工作。
开放重量模型的经济效益可能令人信服，但其效用取决于基础设施和产出质量。
比较成本不是直接的。 开源型可能要花上半小时才能制作草稿，但需要5小时的律师审查
但有的公司，如"红帽子"等，正在想方设法从中国模式中获取价值。 其Z.ai飞行员被多达1400名工程师使用，其中大约70%的推断流量会流向开放重量型号。 工程师有使用前沿模型的预算，并根据他们想要完成的工作在开放重量模型和专有模型之间分配。
对"红帽"来说，控制成本是一个关键驱动器。 边疆模式开支不断增加，不受限制。 工程师随着开量型号的到来，每个准工的配额限于每月300美元，以鼓励他们选择合适的型号完成任务。
配额是试验性的，工程师可以要求管理人员例外。 外部主机模型的成本从跑出到平起平起，到现在"正下降",Watt 称。
对于未来来说，涉及中国AI模式的地缘政治问题可能会加剧。 美国国土安全部本月禁止联邦民间机构使用中国AI开发商DeepSeek的AI技术，引用"多威胁载体"，包括可能向中国转移数据。 但订单并没有区分DeepSeek的主机版本和企业可以在本地服务器上运行的版本。
阿尔伯特·齐格勒(Albert Ziegler)，在Xbow的AI主管，一个自主的进攻性安全公司，称，它曾经测试过GLM，但并没有在生产中使用。 但他并不排除开放重量型号。 他的标准是相对于成本，安全基准测试和客户接受的性能。
在Xbow的测试中，GLM有时需要比封闭型号多得多的符号来解决一个问题，抵消了它的大部分价格优势。 对于其生产应用来说，其他封闭型号，从埃隆·穆斯克的SpaceXSI的Grok，在这种情况下，效率更高，齐格勒·称。
齐格勒的广泛关切是开放式模型可能带来的危险。
一年后，开放量级模型会比今天的封闭模型更好，"和相当多一点"，齐格勒预测。 这可能会吓人，他争论。 “人们可以在自己的私人集群上毫无限制地主持这种口径的模型。”
本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
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The open-weight models are, at least on the surface, considerably cheaper than their U.S. proprietary counterparts.
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You can run a Chinese AI model on a laptop. The models are powerful, capable, in high demand, and help address a business problem: spending on AI tokens.
That is changing the economics of AI services and creating competitive problems for frontier models.
In its IPO prospectus released on Monday, Anthropic said that as a broad variety of models becomes available, "customers may conclude that incremental performance gains at the frontier do not justify the additional cost."
Chinese AI models accounted for more than half the reported token volume on OpenRouter, in its global count, in the week ending on Oct. 25. About a year earlier, only one Chinese model appeared among the nine named on the site’s weekly usage chart. OpenRouter provides access to multiple AI models through a single API.
For its own internal use, open source enterprise software provider Red Hat is piloting a self-hosted version of GLM, an open-weight model from Z.ai in Beijing. Chinese tech giant Alibaba’s popular Qwen model is also available at a smaller scale.
Engineers were initially nervous about whether open-weight models would be as capable as proprietary frontier models, said Stephen Watt, distinguished engineer and vice president of the office of the CTO at Red Hat. But they discovered that they were “good enough,” he said.
Watt said open-weight models, primarily from China, had reached a level of efficacy comparable to Anthropic’s Opus 4.6 in certain use cases. He said engineers had concerns about moving away from frontier models, but Red Hat’s usage data suggested open-weight models were adequate for many tasks.
“You’d be crazy not to start on a frontier model on your AI journey, but after a while, you’d be crazy to stay on it because it just eats all your margin," Watt said, referring to the comparatively high cost of most closed models.
Business users, engineers and developers can allocate workloads based on the capabilities of AI models. They can host models internally, in closed environments. But some are apprehensive about using Chinese models, especially because of geopolitics, which is fueling interest in U.S.-developed models.
The hesitation about Chinese models, where it exists, is usually based on geopolitical concerns. This is creating a pent-up demand for Western-built open-weight models, such as those developed by Meta and Reflection AI, Watt said.
DataCamp, an AI learning platform used by about 6,000 organizations, had its AI tutor analyze some 1.4 million hours of learning in the most recent month. It has been evaluating Gemma 4, a Google open-weight model for tutoring. Chinese models could be a problem for some governments and enterprise users, said co-founder and CEO Jonathan Cornelissen.
But for now, the major limitation of open-weight models is infrastructure. The providers that run the open-weight models can't deliver them at the speed or scale that is needed, Cornelissen said. The latency is not competitive, he said. Some have the speed but lack capacity, while others fall short on response times.
For now, DataCamp is moving from Anthropic to OpenAI, which Cornelissen said he sees as less costly. It has not moved its tutor to an open-weight model.
Jonathan Steele, a partner at Beermann LLP in Chicago and founder of cybersecurity consultancy Steele Fortress, uses a Qwen model locally on a MacBook Pro for billing and other sensitive tasks. He also uses Claude and ChatGPT for other work.
The economics of open-weight models may be compelling, but their usefulness depends on infrastructure and output quality.
Comparing costs isn't straightforward. If an open-source model might take half an hour to produce a draft but needs five hours of attorney review, while a frontier model might take 15 minutes to draft and 30 minutes of revisions, it’s hard to determine which is more expensive.
But some companies, like Red Hat, are finding ways to get value from Chinese models. Its Z.ai pilot is being used by as many as 1,400 engineers, with about 70% of their inference traffic going to the open-weight models. Engineers have a budget for using frontier models and allocate between the open-weight and proprietary models based on the work they want done.
For Red Hat, controlling cost was a key driver. Frontier-model spending was growing unconstrained. Engineers, with the arrival of open-weight models, are limited to a $300-a-month quota per associate to encourage them to choose the right models for the task.
The quotas are experimental, and engineers can request exceptions from managers. The cost of externally hosted models went from runaway to flatlining to "radically decreasing" now, Watt said.
For the future, geopolitical issues involving Chinese AI models may intensify. The U.S. Department of Homeland Security this month barred federal civilian agencies from using Chinese AI developer DeepSeek’s AI technology, citing "multiple threat vectors," including potential transfer of data to China. But the order doesn't distinguish between DeepSeek's hosted versions and those a business can run on a local server.
Albert Ziegler, head of AI at Xbow, an autonomous offensive security company, said it had tested GLM but was not using it in production. But he didn’t rule out open-weight models. His criteria are performance relative to cost, safety benchmarks and customer acceptance.
In Xbow's tests, GLM sometimes needed substantially more tokens to solve a problem than a closed model, offsetting much of its price advantage. For its production applications, other closed models, Grok from Elon Musk’s SpaceXSI, in this case, were more efficient, Ziegler said.
A broad concern for Ziegler is the danger open-weight models might bring.
In a year, the open-weight models will be better than today's closed models, "and by quite a bit," Ziegler predicted. That could be scary, he argued. "People can host a model of that caliber on their own private cluster without any restrictions.”
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