ChatGPT与DeepSeek-R1比较研究：架构、推理能力与应用场景分析A Comparative Study of ChatGPT and DeepSeek-R1: Analysis of Architecture, Reasoning Capabilities, and Application Scenarios

Abstract

人工智能技术的飞速发展推动了大语言模型（LLM）的不断进步。在众多LLM中，OpenAI推出的ChatGPT和DeepSeek-AI开发的DeepSeek-R1尤为引人注目。ChatGPT基于GPT-4架构，具备强大的自然语言理解能力和广泛的应用场景，而DeepSeek-R1则通过强化学习方法优化推理能力，在数学推理和编程任务中展现了强劲的竞争力。本文基于DeepSeek-R1的最新研究成果，全面对比ChatGPT与DeepSeek-R1在模型架构、训练方法、推理能力、应用场景及开放性等方面的差异。研究发现，ChatGPT依赖监督微调（SFT）和基于人类反馈的强化学习（RLHF），在自然语言处理任务上表现突出，而DeepSeek-R1更倾向于通过强化学习优化推理能力，尤其在数学推理、代码生成等任务上表现优异。此外，ChatGPT采用闭源策略，主要用于商业应用，而DeepSeek-R1则采取开源模式，为研究社区和开发者提供更大的灵活性。本文的研究结果为人工智能研究人员和开发者提供了重要参考，以期促进LLM技术的发展，并为未来的大模型优化提供新思路。

The rapid development of artificial intelligence has driven the continuous advancement of large language models (LLMs). Among them, OpenAI's ChatGPT and DeepSeek-AI's DeepSeek-R1 have garnered significant attention. ChatGPT, built upon the GPT-4 architecture, demonstrates strong natural language understanding and wide-ranging applications, whereas DeepSeek-R1 leverages reinforcement learning techniques to optimize reasoning capabilities, excelling in mathematical reasoning and programming tasks. This paper, based on the latest research on DeepSeek-R1, provides a comprehensive comparison between ChatGPT and DeepSeek-R1 in terms of model architecture, training methods, reasoning capabilities, application scenarios, and openness. The study reveals that ChatGPT relies on supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), making it highly effective in natural language processing tasks. In contrast, DeepSeek-R1 emphasizes reinforcement learning to enhance reasoning abilities, particularly excelling in mathematical reasoning and code generation tasks. Moreover, ChatGPT follows a closed-source approach, primarily for commercial use, while DeepSeek-R1 adopts an open-source model, offering greater flexibility for researchers and developers. This study provides valuable insights for AI researchers and developers, contributing to the advancement of LLM technology and future model optimization strategies.