How does DeepSeek's performance on benchmarks like HumanEval and GSM8K compare to other models

Performance Metrics

**HumanEval: DeepSeek scores 73.78% on the HumanEval benchmark, which assesses coding ability through various programming tasks. This score positions it favorably against other leading models, including OpenAI's offerings, although specific comparisons to models like GPT-4 or Llama 3 were not detailed in the available sources.

**GSM8K: In problem-solving tasks, DeepSeek achieves an impressive 84.1% on the GSM8K benchmark. This score reflects its capability in handling mathematical reasoning and complex problem-solving scenarios effectively.

Efficiency and Resource Utilization

DeepSeek's architecture employs a Mixture-of-Experts (MoE) system, activating only a fraction of its total parameters (671 billion) during tasks—specifically around 37 billion. This selective activation not only enhances performance but also significantly reduces computational costs, allowing DeepSeek to achieve these benchmark scores with just 2.8 million GPU-hours, which is considerably lower than many other models that require more extensive resources for similar performance levels[2][3].

Comparison with Other Models

While specific direct comparisons to models like GPT-4 were not provided in the search results, it is noted that DeepSeek's efficiency and open-source nature make it an attractive alternative for developers who might find proprietary solutions cost-prohibitive. The model's ability to handle long context windows of up to 128K tokens further distinguishes it from many competitors, which typically support fewer tokens (usually between 32K and 64K) [2][3].

In summary, DeepSeek demonstrates strong performance on key benchmarks while maintaining cost efficiency and operational effectiveness, making it a noteworthy contender among contemporary LLMs.