Comparative Analysis of Different Generative Artificial Intelligence Tools in Game Code Development

Generative Artificial Intelligence (GenAI) is revolutionizing procedural content generation (PCG) in computer games by enabling automated creation of diversified, adaptive, and complex game elements . The prospects and challenges of GenAI in video games have emerged as significant digital discourse topics, encompassing both cutting-edge technological applications and broad impacts on popular culture.This study conducts a comparative analysis of application differences among GenAI models in game code development. Addressing the research gap concerning free mainstream large models (Grok3, DeepSeek, Doubao, O3-mini, ChatGPT-3.5), we establish a multidimensional evaluation framework to reveal performance disparities in development efficiency, logical consistency, and creative support. Empirical results demonstrate substantial variations in code generation completeness across models under identical prompts (with Grok3 achieving optimal performance and GPT-3.5 yielding suboptimal outcomes).The core finding reveals that natural language interaction empowers non-programmers to create games without coding expertise, thereby democratizing game development. This breakthrough provides theoretical and technical pathways for human-AI collaboration paradigms and lowers barriers to digital content creation.