内容来源：https://aibusiness.com/generative-ai/qwen-3-8-flash-next-cheap-there-are-complicating-factors

内容总结：

阿里发布Qwen 3.8-Flash-Next：低价策略能否成为企业AI选型的关键？

本报讯 中国AI科技巨头阿里巴巴于本周三推出其旗舰模型的全新变体，意图在价格上与欧美供应商展开竞争，为企业客户提供更具成本效益的AI解决方案。然而，此次发布也向业界传递了一个重要信号：价格并非企业选择模型的唯一考量因素。

新品亮点：高效架构与超低定价

据悉，此次发布的Qwen 3.8-Flash-Next是一款多模态模型，提前展示了未来Qwen 4的架构设计。该模型采用混合专家（MoE）架构，总参数量达1250亿，但每个Token仅激活60亿参数，显著降低了推理成本。相比之下，Qwen 3.8 Max的参数量高达2.4万亿，而 rival 月之暗面的Kimi K3 MoE模型也有2.8万亿参数。

在定价方面，阿里给出了极具竞争力的价格——每百万输入Token仅需0.16美元，每百万输出Token为0.47美元。与OpenAI和Anthropic的封闭模型不同，该模型采用开放权重模式，允许企业自主部署。

高德纳（Gartner）分析师Arun Chandrasekaran指出，阿里之所以能维持低价，得益于模型的推理效率——虽然参数量庞大，但每次仅激活少量参数，从而将成本控制在低位。他表示：“阿里正将这款产品定位为广泛企业工作负载的‘主力机型’，在工具调用、代码生成等智能体场景中极具竞争力。”

专家提醒：价格之外仍需多重考量

尽管Qwen 3.8-Flash-Next在价格上颇具吸引力，Chandrasekaran提醒企业客户需审慎评估多个维度。首先，由于该模型为开放权重，企业应考量是选择自行部署还是通过API调用。其次，考虑到中国AI厂商与中国政府的关联，数据驻留和安全隐患不容忽视。

“企业追求低成本模型无可厚非，但不能以牺牲安全性、数据驻留和持续创新能力为代价。”Chandrasekaran强调，“低价本身不应成为唯一的选型参数。”

他建议，企业应确保所选模型支持多种应用场景，同时综合考虑安全性、法律赔偿保障及其他治理因素。

市场挑战：阿里在西方面临的壁垒

对阿里而言，西方市场的渗透仍是其面临的主要挑战。尽管阿里在全球其他地区拥有可观的市场份额，但在美国和西欧市场仍需进一步拓展。与此同时，公司还面临来自月之暗面、智谱AI和DeepSeek等国内同行的激烈竞争。

Chandrasekaran认为，阿里应更积极地定位为垂直化、应用化和智能体化的服务商，而非纯粹的大模型公司，这或许是其在激烈市场竞争中突围的关键路径。

中文翻译：

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尽管阿里巴巴已将推理成本和Token价格保持在低位，但企业仍需考虑其他指标来判断该模型是否适合自己。

中国AI科技巨头阿里巴巴周三推出了其旗舰模型的新版本，旨在与美国的供应商在价格上竞争，并为企业提供成本更低的AI模型。然而，阿里巴巴的最新发布提醒企业，价格并非模型选择的唯一主导因素。

Qwen 3.8-Flash-Next是一款多模态模型，提前预览了即将推出的Qwen 4所采用的架构。它是一个125B参数的模型，每个Token激活6B参数，采用混合专家（MoE）架构。相比之下，Qwen 3.8 Max拥有2.4万亿参数，Qwen 3.8-27B拥有27B参数。竞争对手中国AI供应商月之暗面的Kimi K3 MoE模型拥有2.8万亿参数。

阿里巴巴强调，与Qwen 3.7-Plus相比，Flash-Next版本的训练和推理成本更低。据该供应商称，该模型擅长计算机使用，能够通过视觉和文本提示与复杂的API、计算器以及自定义外部数据库进行交互。

Qwen 3.8-Flash-Next是模型供应商满足企业对更便宜、更具成本效益模型需求的又一例证，这发生在顶级AI供应商之间的价格战以及开源与闭源模型供应商之间紧张关系升级的背景下。该供应商将Flash-Next定价为每百万输入Token 0.16美元，每百万输出Token 0.47美元。与OpenAI和Anthropic的前沿模型不同，它是开放权重的。

“他们试图从定价角度保持该模型极具竞争力，”Gartner分析师阿伦·钱德拉塞卡兰表示。他说阿里巴巴能够做到这一点是因为该模型的推理效率：虽然它包含许多参数，但每次只有少数参数处于激活状态，从而将推理成本保持在低位。

“他们将此定位为一款适用于非常广泛的企业工作负载的‘主力机型’，在该领域这款模型极具竞争力，”钱德拉塞卡兰表示，并指出该模型针对智能体工作负载（如工具调用和编码应用）进行了优化。

“他们正努力将模型引向正确的方向，即在推理效率和推理成本方面做到精简，实现多模态，支持更多智能体用例，并制定极具进攻性的价格，”钱德拉塞卡兰说。

钱德拉塞卡兰继续说，虽然Qwen 3.8 Flash-Next对企业可能很有吸引力且定价合理，但企业应该审视自行托管它（因为它是开放权重的）是否比尝试通过API使用它更有意义。

他补充说，企业还应考虑该模型的数据驻留问题，鉴于中国AI供应商与中国政府的联系，以及相关的安全影响。

“他们希望确保自己使用的是成本高效的模型，但不能以牺牲安全性、数据驻留和持续创新为代价，”他说。“仅凭低价格不应该作为一个考量参数。”

企业应确保所选模型支持多种用例，同时还要考虑安全性、法律赔偿以及其他治理相关问题。

具体到阿里巴巴，该供应商面临的一个障碍是在西方市场的渗透率。虽然阿里巴巴在世界其他地区占据较大市场份额，但它仍需要在美国和西欧市场站稳脚跟。该供应商还面临来自中国其他AI供应商（如月之暗面、智谱和DeepSeek）的竞争。

“阿里巴巴努力将自己定位为更垂直化的参与者，更多是应用和智能体层面的参与者，而非纯粹是一家模型公司，这一点也非常重要，”钱德拉塞卡兰说。

英文来源：

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While Alibaba has kept inference and token price low, enterprises need to consider other metrics to determine if this is the right model for them.
Chinese AI tech giant Alibaba introduced a new variant of its flagship model on Wednesday, aiming to compete on price with U.S. vendors and to provide enterprises with lower-cost AI models. However, the latest Alibaba release serves as a reminder to enterprises that price is not always the main driver of model choice.
Qwen 3.8-Flash-Next is a multimodal model that provides an early preview of the architecture used in the upcoming Qwen 4. It is a 125B-parameter model, with an active 6B parameters per token and a mixture-of-experts (MoE) architecture. In comparison, Qwen 3.8 Max has 2.4 trillion parameters. Qwen 3.8-27B has 27B parameters. Rival Chinese AI vendor Moonshot’s Kimi K3 MoE model has 2.8 trillion parameters.
Alibaba highlighted that compared to Qwen 3.7-Plus, the Flash-Next version has lower training and inference costs. The model excels at computer use and can interact with complex APIs, calculators and custom external databases using visual and text prompts, according to the vendor
Qwen 3.8-Flash-Next is yet another example of model providers appealing to enterprises’ need for cheaper and more cost-efficient models, amid a price war among top AI vendors and escalating tension between open source and proprietary model providers. The vendor priced Flash-Next at $0.16 per million input tokens and $0.47 per million output tokens. It is open weight, unlike frontier models from OpenAI and Anthropic.
“They’ve tried to keep the model very competitive from a pricing perspective,” said Arun Chandrasekaran, an analyst at Gartner. He said that Alibaba can do that because of the model’s inference efficiency: although it contains many parameters, only a few are active, keeping inference costs low.
“They’re positioning this as a workhouse for a very broad category of enterprise workloads, where this model is super competitive,” Chandrasekaran said, noting that the model is optimized for agentic workloads such as tool calling and coding applications.
“They’re trying to move the model in the right direction, which is to make it very lean in terms of inferencing efficiency, inference cost, make it multimodal, enable more agentic use cases and price it very aggressively,” Chandrasekaran said.
While Qwen 3.8 Flash-Next could be compelling for enterprises and is priced well, enterprises should examine whether it makes sense to self-host it, since it is open weight, rather than trying to consume it using an API, Chandrasekaran continued.
He added that enterprises should also consider the model's data residency, considering Chinese AI vendors’ links to the Chinese government, and the associated security implications.
“They want to make sure that they're consuming a cost-efficient model, but not at the price of security, data residency and continuous innovation,” he said. “Low price alone should not be a parameter.”
Enterprises should ensure that the model they choose supports multiple use cases, while also considering safety, legal indemnification and other governance implications.
Specifically for Alibaba, one hurdle the vendor faces is market penetration in the West. While Alibaba enjoys a big market share in other parts of the world, it still needs to gain a foothold in the U.S. and Western European markets. The vendor also faces competition from other Chinese AI vendors such as Moonshot, Z.AI and DeepSeek.
“It is also very important that Alibaba tries to position itself more as a verticalized player, more as an application and agentic player rather than purely as a model company,” Chandrasekaran said.