How does DeepSeek's performance on HumanEval compare to GPT-4

In terms of efficiency, DeepSeek utilizes a Mixture-of-Experts (MoE) architecture that activates only 37 billion of its total 671 billion parameters for tasks, allowing it to maintain high performance with significantly lower computational costs—reportedly 214.3 times cheaper than GPT-4 for token processing[1][2]. This efficiency translates into faster and more precise code generation and debugging capabilities, making DeepSeek a compelling choice for developers.

Moreover, the context window of DeepSeek is substantially larger at 128K tokens, compared to GPT-4's maximum of 8K tokens, enabling it to handle more extensive inputs during processing[1][2]. This feature can be particularly advantageous for complex coding tasks that require greater context.

Overall, DeepSeek not only surpasses GPT-4 in terms of coding performance on HumanEval but also offers significant cost and efficiency benefits, positioning itself as a strong alternative in the landscape of large language models.