Comparative Analysis of DeepSeek R1, ChatGPT, Gemini, Alibaba, and LLaMA: Performance, Reasoning Capabilities, and Political Bias

Abstract

Large Language Models (LLMs) have revolutionized artificial intelligence applications, ranging from content generation to complex problem-solving. However, their performance, efficiency, and inherent biases vary significantly. This paper presents a comparative analysis of DeepSeek R1, ChatGPT, Gemini, Alibaba, and LLaMA, evaluating their reasoning capabilities, computational efficiency, and training costs. A key aspect of this study is an indepth examination of political bias, assessing how these models respond to politically sensitive topics. Using benchmarking datasets and controlled experiments, we analyze their Chain of Thought (CoT) reasoning, bias patterns, and censorship tendencies. The findings highlight crucial differences in AI governance, transparency, and the ethical implications of biased AI-generated content. Graphical insights and real-world examples further illustrate the impact of bias on AI-generated responses, offering recommendations for developing more transparent and fair AI models

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