DeepSeek是什么？它有哪些核心模型和优势？（2026年最新解析）

概述

写在DeepSeekV4 发布之前。自2023年ChatGPT引爆全球AI浪潮以来，中国迅速掀起“百模大战指2023年后中国AI领域众多大模型竞相发展的激烈竞争局面。”。在众多参与者中，DeepSeek（深度求索）中国完全开源、免费商用的系列大语言模型，在国产开源大模型领域具有标杆地位。 凭借其完全开源、免费商用、性能卓越的系列大模型脱颖而出，成为国产开源大模型领域的标杆。成立不到三年，DeepSeek已发布包括 DeepSeek-CoderA DeepSeek model series specifically designed for code generation, focusing on code completion, repair, and mathematical reasoning.、DeepSeek-MoE基于混合专家（Mixture-of-Experts）架构的DeepSeek模型，通过专家网络组合提高效率。、DeepSeek-VLA DeepSeek vision-language model capable of processing and fusing image and text information.、DeepSeek-R1An open-source reasoning large language model developed by DeepSeek that performs comparably to OpenAI o1 on mathematical, coding, and natural language reasoning tasks. 等多个重量级模型。

Since ChatGPT ignited the global AI wave in 2023, China rapidly launched a "Hundred-Model War." Among the numerous participants, DeepSeek has distinguished itself with its fully open-source, free-for-commercial-use, and high-performance series of large models, becoming a benchmark in the field of domestic open-source large models. In less than three years since its establishment, DeepSeek has released several heavyweight models, including DeepSeek-CoderA DeepSeek model series specifically designed for code generation, focusing on code completion, repair, and mathematical reasoning., DeepSeek-MoE基于混合专家（Mixture-of-Experts）架构的DeepSeek模型，通过专家网络组合提高效率。, DeepSeek-VLA DeepSeek vision-language model capable of processing and fusing image and text information., and DeepSeek-R1An open-source reasoning large language model developed by DeepSeek that performs comparably to OpenAI o1 on mathematical, coding, and natural language reasoning tasks..

DeepSeek 核心模型家族概览

DeepSeek 并非单一模型，而是一个覆盖多种任务和架构的模型家族。其核心战略是通过开源和免费商用，降低大模型的应用门槛，推动AI技术的普及。

DeepSeek is not a single model but a family of models covering various tasks and architectures. Its core strategy is to lower the barrier to entry for large model applications and promote the popularization of AI technology through open-source and free commercial use.

下表概述了DeepSeek家族的主要模型及其关键特性：

核心技术解析：混合专家 (MoE) 架构

DeepSeek-MoE基于混合专家（Mixture-of-Experts）架构的DeepSeek模型，通过专家网络组合提高效率。 的成功很大程度上归功于其采用的混合专家架构。这是一种稀疏激活的模型设计，旨在以更低的计算成本实现与稠密模型相当的性能。

The success of DeepSeek-MoE基于混合专家（Mixture-of-Experts）架构的DeepSeek模型，通过专家网络组合提高效率。 is largely attributed to its adoption of the Mixture-of-Experts (MoE) architecture. This is a sparsely activated model design aimed at achieving performance comparable to dense models at a lower computational cost.

MoE 工作原理

在传统的稠密Transformer模型中，每个输入都会经过所有神经元（参数）。而在MoE架构中，模型包含多个“专家”子网络。对于每个输入，一个路由网络（Router）会动态选择最相关的少数几个专家（例如2个）进行处理，其他专家则保持休眠。这种“稀疏激活”特性是其高效的关键。

In traditional dense Transformer models, every input passes through all neurons (parameters). In the MoE architecture, the model contains multiple "expert" sub-networks. For each input, a routing network (Router) dynamically selects the most relevant few experts (e.g., 2) for processing, while other experts remain inactive. This "sparse activation" characteristic is key to its efficiency.

DeepSeek-MoE基于混合专家（Mixture-of-Experts）架构的DeepSeek模型，通过专家网络组合提高效率。 的优势分析

与标准稠密模型相比，DeepSeek-MoE基于混合专家（Mixture-of-Experts）架构的DeepSeek模型，通过专家网络组合提高效率。在多个维度上展现出显著优势。下表从性能、效率和实用性三个维度进行对比：

超长上下文指模型能够处理和理解非常长的文本序列，保持对前后信息的连贯性。支持：技术实现与应用价值

处理超长文本序列（如长达128K甚至更多tokens）是当前大模型的前沿能力，也是DeepSeek重点突破的方向之一。这不仅仅是增加上下文窗口那么简单，背后涉及一系列复杂的技术挑战与创新。

Handling ultra-long text sequences (e.g., up to 128K or even more tokens) is a cutting-edge capability of current large models and one of the key areas DeepSeek has focused on breaking through. This is not merely about increasing the context window; it involves a series of complex technical challenges and innovations.

核心挑战与解决方案

实现可靠的超长上下文指模型能够处理和理解非常长的文本序列，保持对前后信息的连贯性。理解主要面临三大挑战：计算复杂度、注意力机制失效和长程信息丢失。DeepSeek 通过组合多项技术来应对这些挑战。

Achieving reliable ultra-long context understanding primarily faces three major challenges: computational complexity, attention mechanism failure, and long-range information loss. DeepSeek addresses these challenges by combining multiple technologies.

1. 高效注意力机制优化

   • 源项 (Source): 采用 FlashAttention、分组查询注意力 (Grouped Query Attention, GQA) 等技术，显著降低自注意力机制在长序列上的计算和内存开销。
   • 翻译 (Translation): Employing technologies like FlashAttention and Grouped Query Attention (GQA) significantly reduces the computational and memory overhead of the self-attention mechanism on long sequences.

2. 位置编码与外推

   • 源项 (Source): 使用 RoPE (Rotary Position Embedding) 等具有良好外推性的位置编码，使模型能够更好地泛化到训练时未见过的更长序列位置。
   • 翻译 (Translation): Using position encodings with good extrapolation properties, such as RoPE (Rotary Position Embedding), enables the model to better generalize to longer sequence positions not seen during training.

3. 架构与训练策略

   • 源项 (Source): 在训练阶段逐步增加上下文长度，并可能结合 NTK-aware 缩放、YaRN 等方法对位置编码进行针对性调整，以增强模型的长程依赖建模能力。
   • 翻译 (Translation): Gradually increasing the context length during the training phase, and potentially combining targeted adjustments to position encodings using methods like NTK-aware scaling and YaRN, to enhance the model's ability to model long-range dependencies.

应用场景与价值

超长上下文指模型能够处理和理解非常长的文本序列，保持对前后信息的连贯性。能力解锁了众多此前难以实现的应用场景：

Ultra-long context capability unlocks numerous application scenarios that were previously difficult to achieve:

• 源项 (Source): 长文档分析与摘要：一次性处理整本学术论文、技术手册或长篇法律合同，进行深度分析、摘要生成和问答。
• 翻译 (Translation): Long Document Analysis and Summarization: Processing entire academic papers, technical manuals, or lengthy legal contracts in one go for in-depth analysis, summary generation, and Q&A.
• 源项 (Source): 复杂代码库理解：将整个大型软件项目的代码库作为上下文，实现跨文件的代码理解、重构建议和漏洞检测。
• 翻译 (Translation): Complex Codebase Understanding: Using the entire codebase of a large software project as context to achieve cross-file code understanding, refactoring suggestions, and vulnerability detection.
• 源项 (Source): 长对话与个性化助理：记住跨越数天甚至数周的完整对话历史，构建具有长期记忆和高度一致性的个性化智能助理。
• 翻译 (Translation): Long Dialogues and Personalized Assistants: Remembering complete dialogue histories spanning days or even weeks to build personalized intelligent assistants with long-term memory and high consistency.
• 源项 (Source): 多轮研究任务：支持用户进行复杂的、多步骤的研究任务，模型可以持续引用和整合之前交互中提供的所有资料和信息。
• 翻译 (Translation): Multi-turn Research Tasks: Supporting users in complex, multi-step research tasks, where the model can continuously reference and integrate all materials and information provided in previous interactions.

总结与展望

DeepSeek 通过其坚定的开源策略、创新的模型架构（如MoE）以及对核心能力（如超长上下文指模型能够处理和理解非常长的文本序列，保持对前后信息的连贯性。）的持续投入，在中国乃至全球的大模型生态中占据了独特且重要的位置。它不仅仅提供了几个高性能的模型，更重要的是通过降低技术使用门槛和成本，为AI技术的产业化落地铺平了道路。

DeepSeek occupies a unique and important position in the large model ecosystem in China and globally through its steadfast open-source strategy, innovative model architectures (such as MoE), and continuous investment in core capabilities (such as ultra-long context). It not only provides several high-performance models but, more importantly, paves the way for the industrial application of AI technology by lowering the barriers to entry and costs.

未来，我们期待DeepSeek在以下方向继续深化：

• 源项 (Source): 多模态融合的深化：使DeepSeek-VLA DeepSeek vision-language model capable of processing and fusing image and text information.等模型在理解和生成图文混合内容上更加精准、高效。
• 翻译 (Translation): Deepening Multimodal Integration: Making models like DeepSeek-VLA DeepSeek vision-language model capable of processing and fusing image and text information. more accurate and efficient in understanding and generating mixed text-image content.
• 源项 (Source): 推理能力的突破：基于DeepSeek-R1An open-source reasoning large language model developed by DeepSeek that performs comparably to OpenAI o1 on mathematical, coding, and natural language reasoning tasks.，进一步攻克复杂数学、科学和逻辑推理的难题。
• 翻译 (Translation): Breakthroughs in Reasoning Capabilities: Building on DeepSeek-R1An open-source reasoning large language model developed by DeepSeek that performs comparably to OpenAI o1 on mathematical, coding, and natural language reasoning tasks. to further tackle complex problems in mathematics, science, and logical reasoning.
• 源项 (Source): 部署效率的极致优化：探索更极致的模型压缩、蒸馏和硬件适配方案，让超大模型能在边缘设备上运行。
• 翻译 (Translation): Extreme Optimization of Deployment Efficiency: Exploring more extreme model compression, distillation, and hardware adaptation solutions to enable ultra-large models to run on edge devices.

随着技术迭代和生态繁荣，DeepSeek有望持续推动开源大模型前沿，赋能千行百业的智能化转型。

With technological iteration and a thriving ecosystem, DeepSeek is expected to continue advancing the frontier of open-source large models, empowering the intelligent transformation of industries across the board.

常见问题（FAQ）

DeepSeek模型可以免费商用吗？

是的，DeepSeek系列模型完全开源且免费商用，其许可证政策消除了企业使用的法律与费用风险，加速了AI技术落地。

DeepSeek-MoE基于混合专家（Mixture-of-Experts）架构的DeepSeek模型，通过专家网络组合提高效率。相比传统模型有什么优势？

DeepSeek-MoE基于混合专家（Mixture-of-Experts）架构的DeepSeek模型，通过专家网络组合提高效率。采用混合专家架构，每次推理仅激活少量参数，大幅降低计算开销，可用更少GPU资源部署超大规模模型，显著降低服务成本。

DeepSeek有哪些主要模型？分别擅长什么？

DeepSeek-CoderA DeepSeek model series specifically designed for code generation, focusing on code completion, repair, and mathematical reasoning.擅长代码生成与理解；DeepSeek-MoE基于混合专家（Mixture-of-Experts）架构的DeepSeek模型，通过专家网络组合提高效率。高效推理成本低；DeepSeek-VLA DeepSeek vision-language model capable of processing and fusing image and text information.处理视觉语言多模态任务；DeepSeek-R1An open-source reasoning large language model developed by DeepSeek that performs comparably to OpenAI o1 on mathematical, coding, and natural language reasoning tasks.强化逻辑推理与数学能力。