“模型超市”越开越多:字节、阿里、腾讯竞相整合

marsbitPublished on 2026-04-24Last updated on 2026-04-24

Abstract

近日,字节跳动火山引擎推出“方舟Coding Plan”,正式上线GLM-5.1,并集成Minimax、Kimi、DeepSeek等多款国产大模型,以每月40元或200元的套餐价格提供多模型调用服务,降低开发者试错成本。然而,用户反馈显示存在使用额度消耗过快、算力调度不足导致的响应延迟和错误等问题,影响实际开发体验。 阿里云、腾讯云、百度智能云等厂商也纷纷推出类似“模型超市”服务,行业竞争从单一模型能力转向平台整合与生态服务能力。这一趋势引发了对独立模型公司“管道化”风险的讨论,即模型厂商可能丧失定价权和用户连接能力。智谱、月之暗面、MiniMax等公司正通过发展自治智能体、分散布局、深耕垂直场景等方式寻求突破。 分析师认为,短期来看平台方可能占据更多话语权,但垂直领域的模型壁垒仍难以被完全替代。未来行业或走向更清晰的分工协作,而非简单吞并。

字节跳动火山引擎方舟Coding Plan近日正式上线 GLM-5.1,官方表示“对齐原厂满血能力,不限购”。在此之前,火山的Coding Plan长期仅有GLM-4.7等较老模型。此次更新不仅引入了GLM-5.1,同时集成了Minimax M2.7、Kimi k2.6、DeepSeek-V3.2等多款最新国产大模型。

这意味着,开发者只需一份订阅费,即可同时调用多家头部模型。从市场反馈来看,这种“打包模式”极大地降低了开发者的试错成本。目前Lite套餐价格在每月40元,Pro套餐价格在每月200元,让不少开发者愿意“先买个占坑”。

智谱GLM-5.1本身在2026年4月初的一次更新中,已经展现出令人印象深刻的工程能力。在智谱发布的两个官方视频中,“8小时从零构建Linux桌面”、“655轮迭代,将向量数据库的查询吞吐提升到初始正式版本的6.9倍”,重新刷新了大众对于大模型“8小时有效执行”的想象。

记者实探开发者社区 多数用户表示“不耐用”

记者进入一个方舟Coding开发者交流群中,发现用户在分享体验感受的帖子之外,大量用户反馈了实际体验的落差。刷几页交流社区就会发现,投诉、申请售后退钱的帖子非常多,有不少网友直呼“感觉被骗”。

争议主要有两点:

一个是关于限额使用过快的问题。一位名为“哈基米”的用户发帖称“一个任务几轮对话 5小时限制就快用完了”,还有另一位网友发布了自己“5小时限额触发的原因”是因为账号在连续5小时内连续滑动窗口,实际请求数已超过6004次,超过了系统限额。

二是算力调度承压导致的体验下降。大量用户反映遇到429错误(请求过多),以及高峰期“首字延迟1分钟以上是常态”。有用户直言:“5小时限额触发太频繁,没法用来做正经开发。”

与此同时,在Coding Plan每月40元的低价背后,也隐藏着套餐内关于“一次调用请求”引向不同抵扣系数的"暗流"。比如一位用户在开发者交流群发布了“调用不同模型抵扣系数差异”的图片。比如豆包全系列、Qwen系列抵扣系数是1次,DeepSeek系列是2次,MiniMax-M2.7、Kimi-K2.6、GLM-5.1系列是5次。

这也反映出,“模型超市”的搭建并没有想象中容易,开发者被“性价比”吸引而来,但初期暴露出的算力调度等方面的短板,也让很多开发者尝试之后选择却步。这也暴露了“打包模式”初期的阵痛。随着用户的涌入,算力平台的承载能力面临挑战。如何在低价吸引与服务质量之间找到可持续的平衡点,将是火山引擎及跟进者需要解决的长期命题。

云厂商集体转向“模型超市” 分层固化初现

火山引擎Coding Plan的这次“整合式”更新,也并非孤立事件。

自2026年初以来,阿里云、百度智能云、腾讯云等主流云厂商均在推进多模型整合布局。比如阿里云作为行业先行者,较早推出多模型订阅套餐“百炼Coding Plan”。目前支持千问系列和kimi-k2.5、glm-5、MiniMax-M2.5等模型。目前Pro价格为每月200元,Lite 套餐自3月20日起已停止新购,4月13日起停止续费与升级。

腾讯云大模型Coding Plan订阅服务于2026年3月全面上新,支持 Tencent HY 2.0 Instruct、GLM-5、Kimi-K2.5、MiniMax-M2.5 等多个最新模型。百度千帆于2026年2月正式推出AI编码订阅服务Coding Plan,也是国内较早推出此类服务的云厂商之一。

“模型超市”模式,并非一家之选,而正在成为云厂商竞相布局的赛道。但撕开云厂商聚合策略的外衣,谁能提供更稳定的服务、更透明的额度规则、更灵活的容灾机制,谁能在编程之外,延展出更多企业级服务能力,续费率能不能跟得上,都成为新的竞争核心。

国际上,亚马逊 Bedrock、微软 Azure的模型聚合服务平台,与国内Coding订阅模式场景不同,但同属整合趋势。

整体来看,行业竞争也从“单一模型能力比拼”转向“平台整合能力+生态服务能力”的比拼,行业集中度将快速提升。

国信证券首席资产配置分析师王开告诉记者,虽然行业分化在加速,但整合期的判断可能为时稍早。“更准确地说,这是产业链分工的细化和迭代。模型厂商聚焦算法,云厂商聚焦工程交付,各自发挥主业优势。”他认为,无论其他云厂商是否跟进,竞争格局都将从单打独斗向生态位分化演变。

大模型公司 “管道化” 压力加剧?

所谓“管道化”,并非指模型公司消失,而是指其丧失产品溢价、用户连接权与话语权,利润向算力平台方转移,成为“被支配”的角色。

云厂商的聚合浪潮之下,“管道化”也正在成为悬在独立大模型公司头顶的达摩克利斯之剑。这场无声的博弈中,智谱AI、月之暗面(Kimi)、MiniMax等头部玩家,并未选择被动妥协,而是从基因中各自生长,给出了不同的突围路径。

智谱AI CEO张鹏在4月8日的公开对话中,他明确表示,智谱的终极目标从来不是成为一款“可被随意替换的调用工具”,而是构建全自治智能体(Autonomous Agent)。这一定位试图让智谱从“模型供应商”升级为“任务执行者”,从而绕过纯API管道的低价陷阱。

月之暗面(Kimi)则采取“分散布局+深耕长文本”的策略。同步接入火山引擎、阿里云等多家主流云平台,实现算力多源供给、不被单一渠道绑定,保障服务稳定性与成本可控。2026年4月推出的Kimi K2.6 采用混合专家(MoE)架构,标准上下文窗口达256K tokens。

MiniMax则将将核心资源投入到内容创作、智能客服、教育、企业服务、娱乐社交等垂直领域,尤其在游戏AI、数字人、多模态交互等场景重点布局,打造“云平台难以替代的定制化能力”。

大厂的平台整合会加速模型公司被“管道化”吗?国信证券首席资产配置分析师王开认为,需区分长短期视角。

“短期看,分发渠道被平台掌控、定价权部分让渡,模型厂商利润向入口方转移是商业规律。但长期而言,通用模型易同质化,金融、医疗、法律等垂直场景的深度学习模型,专业壁垒不是集中聚合就能抹平的。”他认为。

在应对被平台化风险方面,还可以参考OpenAI和Anthropic的策略,一方面强化直面终端用户的渠道,比如ChatGPT与Claude的独立运营本质是在建立绕过平台的用户连接。另一方面技术迭代速度与用户品牌认知是两条有效护城河,因此模型公司需要兼顾研发投入与产品化布局。

这场“管道化与平台化”的博弈终局,可能不是谁吃掉谁,而是分工的进一步清晰。云厂商做管道,模型公司做技术,双方在博弈中逐渐找到各自的生存边界。

至于谁吃掉谁,在目前这个阶段,还远未到故事的结局。

本文来自微信公众号“科创板日报”,作者:王耐

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Related Questions

Q字节跳动火山引擎方舟Coding Plan最近上线了哪些新模型?

A字节跳动火山引擎方舟Coding Plan近日正式上线了GLM-5.1,并同时集成了Minimax M2.7、Kimi k2.6、DeepSeek-V3.2等多款最新国产大模型。

Q开发者对火山引擎Coding Plan的主要投诉有哪些?

A开发者主要投诉两点:一是限额使用过快,一个任务几轮对话就可能耗尽5小时限额;二是算力调度承压导致体验下降,如频繁遇到429错误(请求过多)和高峰期首字延迟1分钟以上。

Q除了字节跳动,还有哪些云厂商在推进“模型超市”模式?

A除了字节跳动,阿里云、百度智能云、腾讯云等主流云厂商也在推进多模型整合布局。例如阿里云的“百炼Coding Plan”、腾讯云的大模型Coding订阅服务以及百度千帆的AI编码订阅服务。

Q智谱AI、月之暗面(Kimi)和MiniMax如何应对“管道化”风险?

A智谱AI通过构建全自治智能体(Autonomous Agent)升级为“任务执行者”;月之暗面(Kimi)采取分散布局和深耕长文本策略,接入多家云平台;MiniMax则聚焦垂直领域如内容创作、智能客服等,打造定制化能力。

Q国信证券首席资产配置分析师王开如何看待“模型超市”对行业的影响?

A王开认为,短期看模型厂商利润会向平台方转移,但长期而言,垂直领域的专业模型壁垒难以被平台聚合抹平。行业将从单打独斗向生态位分化演变,最终可能是分工进一步清晰,而非一方吃掉另一方。

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What is $S$

What is AGENT S

Agent S: The Future of Autonomous Interaction in Web3 Introduction In the ever-evolving landscape of Web3 and cryptocurrency, innovations are constantly redefining how individuals interact with digital platforms. One such pioneering project, Agent S, promises to revolutionise human-computer interaction through its open agentic framework. By paving the way for autonomous interactions, Agent S aims to simplify complex tasks, offering transformative applications in artificial intelligence (AI). This detailed exploration will delve into the project's intricacies, its unique features, and the implications for the cryptocurrency domain. What is Agent S? Agent S stands as a groundbreaking open agentic framework, specifically designed to tackle three fundamental challenges in the automation of computer tasks: Acquiring Domain-Specific Knowledge: The framework intelligently learns from various external knowledge sources and internal experiences. This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

738 Total ViewsPublished 2025.01.14Updated 2025.01.14

What is AGENT S

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