All comparison data is captured from the DeepSeek official website.

In recent years, competition in the field of large AI models has intensified, with major technology companies and research institutions introducing notable models. This article will compare and analyze DeepSeek V3 & V2.5, Qwen2.5, Llama3.1, Claude-3.5, and GPT-4o in terms of architecture, parameter count, performance metrics, and explore their applications in different scenarios.

Architecture and Parameter Count

DeepSeek V3 and V2.5 use a mixture of experts (MoE) architecture, activating 37 billion and 21 billion parameters respectively, with total parameter counts reaching 671 billion and 236 billion. The MoE architecture effectively combines multiple expert models with a dynamic routing mechanism, offering a flexible and scalable design approach. In contrast, Qwen2.5 and Llama3.1 utilize a dense model architecture, activating 72 billion and 405 billion parameters. The architecture and parameter counts for Claude-3.5 and GPT-4o are not explicitly disclosed, but their performance indicates that they also possess significant computational capabilities.

Performance Comparison   

English Proficiency

In the MMLU (Massive Multitask Language Understanding) test, DeepSeek V3 leads with an EM (Exact Match) score of 88.5, slightly higher than GPT-4o's 87.2. In the DROP (Discrete Reasoning Task), DeepSeek V3 achieves the top performance with an F1 score of 91.6, surpassing Qwen2.5's 76.7. Additionally, DeepSeek V3 performs well in GPQA-Diamond (high-difficulty question answering) and SimpleQA (simple question answering), scoring 59.1 and 24.9 respectively.

Coding Ability

In code generation and programming tasks, DeepSeek V3 leads in HumanEval-Mul (multilingual code generation) with a Pass@1 score of 82.6, slightly above Claude-3.5's 81.7. DeepSeek V3 also shows strong performance in LiveCodeBench (real-time code generation) and Codeforces (programming competitions), scoring 40.5 and 51.6 respectively.

Mathematical Ability

In mathematical reasoning tasks, DeepSeek V3 excels in AIME 2024 (American Invitational Mathematics Examination) and MATH-500 (high-difficulty math problems) with scores of 39.2 and 90.2, significantly outperforming other models. Notably, in CNMO 2024 (Chinese National Mathematical Olympiad), DeepSeek V3's score of 43.2, indicating robust capabilities in mathematics.

Chinese Proficiency

In Chinese language tasks, DeepSeek V3 performs well in CLUEWSC (Chinese Language Understanding) and C-Eval (Chinese Evaluation) with EM scores of 90.9 and 86.5 respectively. In C-SimpleQA (Chinese Simple Question Answering), DeepSeek V3 scores 64.1, demonstrating effective processing of the Chinese language.

Application Scenarios and Choices  

In practical applications, the choice between DeepSeek V3 and mainstream large models depends on specific task requirements and resource conditions:

• Open-Domain Tasks : For complex open-domain tasks (such as general content generation, multi-turn dialogue, etc.), mainstream large models (like GPT-4 and Claude-3.5) are better choices due to their greater generality and performance.

• Specific Domain Tasks : For tasks in specific domains (such as medical diagnosis, legal consulting, etc.), DeepSeek V3 may provide advantages, as it can be tailored and optimized based on domain-specific data, providing more accurate services.

• Resource-Limited Environments : When computational resources are limited or cost is a concern, DeepSeek V3 may be a suitable option due to its lower deployment and operational costs, making it accessible for small and medium enterprises and individual developers.

• Privacy Protection and Data Security : For tasks that involve sensitive data or require stringent privacy protection, DeepSeek V3 may be appropriate, as it emphasizes data security and privacy.

Conclusion

DeepSeek V3 has shown strong performance across various benchmark tests, particularly excelling in English, coding, mathematics, and Chinese tasks. DeepSeek V3's MoE architecture and efficient parameter utilization could provide it with a competitive advantage in certain areas, while Qwen2.5, Llama3.1, Claude-3.5, and GPT-4o also demonstrate good performance in certain areas, As technology continues to advance, both DeepSeek V3 and its counterparts are expected to make further strides in their respective areas, fostering broader adoption and application of AI technology across various domains.

Remark：This article was generated by Generative AI (GenAI) and edited by ARCH Team. For all external links or information, please refer to their latest updates.