中国AI大模型全景图：从通用底座到行业深度的生态革命

近两年来，中国人工智能大模型的发展已进入高速轨道。从通用基础模型到垂直行业应用，从中文语义理解到多模态内容生成，国产模型正以令人瞩目的速度重塑全球人工智能的竞争格局。时至2025年，中国已构建起“通用大模型与行业大模型”双轨并进、协同发展的繁荣生态，并在多个关键技术领域实现了从追赶到并跑，乃至局部领跑的跨越。本文将深入剖析当前国内最具代表性的AI大模型A deep learning model with massive parameters (billions to trillions) trained on vast datasets, known for strong generalization and multi-task potential.，盘点其突破性进展与核心价值。

In the past two years, the development of large AI models in China has entered the fast lane. From general-purpose foundations to vertical scenarios, from Chinese language understanding to multimodal generation, domestic models are reshaping the global AI competitive landscape at an astonishing pace. By 2025, China has formed a dual-track ecosystem of “general-purpose + industry-specific” large models, achieving technological catch-up and even surpassing in multiple fields. This article will provide an in-depth analysis of the most representative domestic AI large models and their breakthrough progress.

一、 通用大模型：国产力量的崛起

通用大模型作为人工智能的“技术底座”，其能力决定了整个生态的上限。中国科技企业在此领域已形成多强并立的格局，各具特色。

百度·文心一言（ERNIE）

百度文心一言在SuperCLUE等权威中文大模型评测中多次位居综合能力榜首，尤其在金融、教育等对专业知识要求高的场景中表现卓越。其多模态生成指AI模型能够同时处理和生成多种类型的数据，如文本、图像、语音、视频等，实现跨模态的理解和创作能力。能力（涵盖文本、图像、语音）成熟度行业领先，已成功赋能智能投顾等系统，据称可将用户画像精准度提升35%。最新开源的文心4.5系列包含十款不同规模的模型，其FLOPs利用率衡量模型计算效率的指标，表示实际有效计算占理论最大计算能力的比例，高利用率意味着更好的硬件资源利用。高达47%，并为开发者提供了从训练到部署的全栈工具链支持。

Baidu's ERNIE Bot has repeatedly topped the comprehensive capability rankings in authoritative Chinese large model evaluations such as SuperCLUE, demonstrating outstanding performance particularly in scenarios requiring deep professional knowledge like finance and education. Its multimodal generation capabilities (covering text, image, and speech) are industry-leading in maturity and have successfully empowered systems like intelligent investment advisors, reportedly improving user profile accuracy by 35%. The newly open-sourced ERNIE 4.5 series includes ten models of varying scales, achieving a FLOPs utilization rate as high as 47%, and provides developers with a full-stack toolchain support from training to deployment.

深度求索·DeepSeek

DeepSeek以其卓越的“性价比”而闻名。其中，DeepSeek-R1模型仅以约十分之一的参数规模，实现了媲美GPT-4的数学推理能力，而训练成本仅为557.6万美元，堪称一次“性价比革命”。该模型擅长动态推理优化与个性化内容生成，在金融高频交易决策中可实现毫秒级响应，并将工业故障诊断效率提升25%。其积极的开源策略加速了国产AI芯片的适配进程，沐曦、天数智芯等超过15家芯片厂商已完成对其模型的深度优化。

DeepSeek is renowned for its exceptional “cost-performance ratio.” Among them, the DeepSeek-R1 model, with only about one-tenth the parameter scale, achieves mathematical reasoning capabilities comparable to GPT-4, at a training cost of only $5.576 million, which can be called a “cost-performance revolution.” This model excels at dynamic reasoning optimization and personalized content generation, achieving millisecond-level response in high-frequency financial trading decisions and improving industrial fault diagnosis efficiency by 25%. Its proactive open-source strategy has accelerated the adaptation process for domestic AI chips, with over 15 chip manufacturers like MetaX and Tianshu Zhixin having completed deep optimization for its models.

阿里巴巴·通义千问（Qwen）

通义千问Qwen2.5-Max版本在具有全球影响力的Chatbot Arena盲测中位列第七，并在数学与编程能力单项评测中斩获冠军。其多模态模型Qwen-VL支持图文统一理解与生成，并支持开放指令编辑，极大提升了内容创作的自由度。阿里巴巴坚持全尺寸开源策略（提供从7B到110B参数的系列模型），极大地推动了开发者社区的繁荣与技术创新。

Alibaba's Qwen2.5-Max version ranked seventh in the globally influential Chatbot Arena blind test and won championships in individual evaluations for mathematical and programming capabilities. Its multimodal model Qwen-VL supports unified understanding and generation of images and text, along with open instruction editing, significantly enhancing the freedom of content creation. Alibaba adheres to a full-scale open-source strategy (offering a series of models from 7B to 110B parameters), greatly promoting the prosperity of the developer community and technological innovation.

字节跳动·豆包大模型

豆包大模型采用了先进的稀疏混合专家（MoE）架构，以极低的训练成本实现了相当于7倍参数规模的稠密（Dense）模型的性能。其在多模态人机交互领域表现尤为亮眼，支持富有情感的实时语音合成（能模拟哭、笑、方言等），目前已覆盖超过300家医院的智能患者教育场景。近期推出的“深入研究”功能，可以快速生成定制化分析报告并一键转换为播客音频。

ByteDance's Doubao large model employs an advanced Sparse Mixture of Experts (MoE) architecture, achieving performance equivalent to a Dense model with 7 times the parameter scale at an extremely low training cost. Its performance in multimodal human-computer interaction is particularly notable, supporting emotional real-time speech synthesis (simulating crying, laughter, dialects, etc.), and has currently covered intelligent patient education scenarios in over 300 hospitals. The recently launched “In-depth Research” feature can quickly generate customized analysis reports and convert them into podcast audio with one click.

二、 垂直领域：深耕行业的先锋力量

当通用能力趋于成熟，深入特定行业、解决具体痛点成为大模型价值深挖的关键。一批专注于垂直领域的模型正展现出强大的生产力。

月之暗面·Kimi

Kimi以其高达20万汉字（约32万tokens）的上下文处理窗口，树立了长文本理解的行业标杆，显著提升了法律文书审阅、学术文献分析等场景的效率。在医学文献综述场景中，据称可帮助研究人员提效50%，其能力正加速向法律、科研等专业领域延伸。

Kimi, with its context window capable of handling up to 200,000 Chinese characters (approximately 320,000 tokens), has set an industry benchmark for long-text understanding, significantly improving efficiency in scenarios like legal document review and academic literature analysis. In medical literature review scenarios, it is reported to help researchers improve efficiency by 50%, and its capabilities are rapidly extending to professional fields such as law and scientific research.

科大讯飞·星火大模型

依托科大讯飞在语音技术上的深厚积累，星火大模型的多语言交互能力突出（支持30多种语言），其APP累计下载量已超过2亿。通过深度融合语音识别与合成技术，它在教育和医疗行业形成了成熟的解决方案，将智能问答的准确率提升了50%以上。

Leveraging iFlytek's deep expertise in speech technology, the Spark large model excels in multilingual interaction (supporting over 30 languages), with its cumulative app downloads exceeding 200 million. By deeply integrating speech recognition and synthesis technologies, it has developed mature solutions in the education and healthcare industries, improving the accuracy of intelligent Q&A by over 50%.

智谱AI·GLM-4

作为清华大学背景的代表性模型，GLM-4是国内首个支持视频通话交互的千亿参数级别模型。它在语言理解与创意写作方面表现均衡，在知识问答的精准度上可媲美GPT-4 Turbo，展现了扎实的综合能力。

As a representative model with Tsinghua University background, GLM-4 is China's first hundred-billion-parameter model supporting video call interaction. It demonstrates balanced performance in language understanding and creative writing, with knowledge Q&A accuracy comparable to GPT-4 Turbo, showcasing solid comprehensive capabilities.

百川智能·百川大模型

百川大模型专注于医疗垂直场景，其疾病诊断辅助系统已覆盖全国超过1000家医院。其开源模型Baichuan-7B/13B的下载量突破百万，为缓解基层医疗资源短缺问题提供了有效的AI工具。

Baichuan large model focuses on the vertical healthcare scenario, and its disease diagnosis assistance system has covered over 1,000 hospitals nationwide. Its open-source models Baichuan-7B/13B have surpassed one million downloads, providing effective AI tools to alleviate the shortage of primary healthcare resources.

如果您想快速对比这些垂直领域模型的详细参数，可以访问【AIbase模型广场】（网址:https://model.aibase.cn/models）。该平台提供了强大的筛选和对比功能，可按行业、任务类型、参数规模等维度精准查找模型，帮助开发者和企业用户高效完成技术选型。
If you want to quickly compare the detailed parameters of these vertical domain models, you can visit the 【AIbase Model Square】 (URL: https://model.aibase.cn/models). This platform offers powerful filtering and comparison features, allowing precise model searches by industry, task type, parameter scale, and other dimensions, helping developers and enterprise users efficiently complete technical selection.

三、 生成与创作：AIGC的核心引擎

在内容创作（AIGC）这一热门赛道，中国模型同样取得了突破性进展，极大地降低了创意生产的门槛。

腾讯·混元大模型

依托万亿级的参数规模，混元大模型支持从文本直接生成视频，并与微信生态深度整合，探索社交场景下的创新应用。新发布的混元-A13B模型优化了部署效率，仅需1张中低端GPU即可运行，大幅降低了AI应用的门槛。

Leveraging a trillion-level parameter scale, Hunyuan large model supports direct text-to-video generation and is deeply integrated with the WeChat ecosystem, exploring innovative applications in social scenarios. The newly released Hunyuan-A13B model optimizes deployment efficiency, requiring only one mid-to-low-end GPU to run, significantly lowering the barrier to AI application.

字节跳动·即梦

作为一款AI短视频创作工具，“即梦”支持通过文本或图片输入生成1080P高清视频，其操作简易性在同类产品中领先。上线一年内，在字节跳动的内容平台生态中，创作者使用率已达到40%，深度适配了短视频内容的生产流程。

As an AI short video creation tool, “即梦” supports generating 1080P high-definition video from text or image input, with its operational simplicity leading among similar products. Within one year of launch, its adoption rate among creators within ByteDance's content platform ecosystem has reached 40%, deeply adapting to the short video content production workflow.

四、 行业落地与未来趋势

当前，大模型的竞争焦点已从单纯比拼技术参数，转向实际应用的价值创造。众多成功的行业落地案例正在涌现：

• 中关村科金“灵筑智工”：应用于交通基建领域，使工程文档处理效率提升60%。
  • Zhongguancun Kejin's “Lingzhu Zhigong”: Applied in transportation infrastructure, improving engineering document processing efficiency by 60%.
• 中国移动“九天客服大模型”：实现24小时拟人化智能客服服务。
  • China Mobile's “Jiutian Customer Service Large Model”: Achieves 24/7 anthropomorphic intelligent customer service.
• 浪潮云“海若大模型”：优化医疗病历生成与整理流程。
  • Inspur Cloud's “Hairuo Large Model”: Optimizes the medical record generation and organization process.

展望未来，技术演进呈现三大清晰趋势：

1. 低成本推理优先：模型蒸馏一种模型压缩技术，通过训练一个小型模型（学生模型）来模仿大型模型（教师模型）的行为，在保持性能的同时减少模型规模和计算需求。、小型化以及MoE架构中仅激活部分参数（小参数量激活）成为降低部署与使用成本的主流技术路径。
   • Low-cost Inference Priority: Model distillation, miniaturization, and activating only partial parameters in MoE architectures (small parameter activation) have become mainstream technical paths to reduce deployment and usage costs.
2. 端侧部署将AI模型直接部署在终端设备（如手机、物联网设备）上运行，减少对云端服务器的依赖，提升响应速度和隐私保护。普及：随着模型优化，类似谷歌Gemma 3B的轻量化模型正实现在手机等终端设备上的多模态高效运行，让AI能力无处不在。
   • Proliferation of On-device Deployment: With model optimization, lightweight models like Google's Gemma 3B are enabling efficient multimodal operation on terminal devices such as smartphones, making AI capabilities ubiquitous.
3. 开源生态决胜：以Qwen、DeepSeek为代表的积极开源策略，正加速大模型技术在千行百业的渗透与应用创新，构建起强大的开发者护城河。
   • Open-source Ecosystem as a Decisive Factor: Proactive open-source strategies represented by Qwen and DeepSeek are accelerating the penetration and application innovation of large model technology across all industries, building a strong developer moat.

结语

中国AI大模型A deep learning model with massive parameters (billions to trillions) trained on vast datasets, known for strong generalization and multi-task potential.的爆发并非单一技术的点状突破，而是一场“千帆竞发”的全面生态革命。当这些技术从实验室走向工厂产线、医院诊室、学校课堂，我们正在见证一场虽无声却深刻的生产力进化。未来，通用底座与行业纵深结合、技术创新与场景落地并重，将是中国人工智能持续领先的关键。

The explosion of China's AI large models is not a point breakthrough of a single technology, but a comprehensive ecological revolution with “a thousand ships competing.” As these technologies move from the laboratory to factory production lines, hospital clinics, and school classrooms, we are witnessing a silent yet profound evolution of productivity. In the future, the combination of general-purpose foundations with industry depth, and the equal emphasis on technological innovation and scenario implementation, will be the key to China's sustained leadership in artificial intelligence.