How does DeepSeek-V3 compare to GPT-4?

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DeepSeek-V3 and GPT-4 are both advanced large language models (LLMs), but they have distinct differences in their performance, architecture, and applications. Here's a detailed comparison:

Performance

  1. Benchmark Scores:
    • DeepSeek-V3: Outperforms GPT-4 in several benchmarks, particularly in coding tasks and mathematical reasoning. For instance, DeepSeek-V3 achieved a 90.2% score on the MATH 500 benchmark, significantly higher than GPT-4's performance.
    • GPT-4: While GPT-4 excels in general knowledge understanding and complex question answering, it lags behind DeepSeek-V3 in specific areas like coding and advanced math reasoning.
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  1. Coding Competitions:
    • DeepSeek-V3: Demonstrated superior performance in coding competitions hosted on Codeforces, outperforming GPT-4 and other models.
    • GPT-4: Still performs well in coding tasks but is not as specialized as DeepSeek-V3 in this area.
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Architecture

  1. Parameters and Efficiency:
    • DeepSeek-V3: Utilizes a Mixture-of-Experts (MoE) architecture with 671 billion total parameters, of which 37 billion are activated per token. This architecture allows for more efficient use of computational resources.
    • GPT-4: While the exact number of parameters is not publicly disclosed, GPT-4 is known for its large parameter count and sophisticated architecture, which contributes to its high performance across various tasks.
  2. Training Data:
    • DeepSeek-V3: Trained on 14.8 trillion high-quality tokens, which is a significant dataset that contributes to its robust performance.
    • GPT-4: Trained on a vast and diverse dataset, although the exact size is not publicly known. OpenAI has emphasized the quality and diversity of its training data to ensure broad applicability and high performance.

Applications

  1. Text Generation:
    • DeepSeek-V3: Excels in generating high-quality text for various applications, including essays, emails, and other descriptive content from prompts.
    • GPT-4: Also proficient in text generation, with a strong emphasis on natural language understanding and generation across different domains.
  2. Coding and Software Engineering:
    • DeepSeek-V3: Particularly strong in coding tasks, including writing new code that integrates seamlessly with existing code.
    • GPT-4: Capable of generating code but not as specialized as DeepSeek-V3 in this area. It performs well in general software engineering tasks but may not match DeepSeek-V3's specific coding benchmarks.
  3. Mathematical Reasoning:
    • DeepSeek-V3: Outperforms GPT-4 in mathematical reasoning tasks, scoring higher on benchmarks like Math-500 and AIME 2024.
    • GPT-4: While capable of handling mathematical problems, it does not match the specialized performance of DeepSeek-V3 in this area.

Cost and Accessibility

  1. Development Cost:
    • DeepSeek-V3: Developed at a relatively low cost of $5.5 million, using 2048 Nvidia H800 GPUs over two months.
    • GPT-4: Developed by OpenAI, which typically invests significantly more in its models, although exact costs are not publicly disclosed.
  2. API Pricing:
    • DeepSeek-V3: Offers competitive pricing with $0.27 per million tokens for input and $1.10 per million tokens for output until February 8, 2025.
    • GPT-4: Pricing details are not provided in the references, but it is generally known to be more expensive than open-source models like DeepSeek-V3 due to its proprietary nature and advanced capabilities.

Conclusion

DeepSeek-V3 and GPT-4 are both powerful LLMs with their own strengths and specializations. DeepSeek-V3 excels in coding and mathematical reasoning tasks, offering competitive performance at a lower cost. GPT-4, on the other hand, is a more generalist model with strong performance across a wide range of tasks but may not match DeepSeek-V3's specialized capabilities in certain areas. The choice between the two would depend on the specific needs and applications of the user or organization.