This paper presents a comparative analysis of nine top generative artificial intelligence (AI) tools for natural language processing (NLP), including ChatGPT, Perplexity AI, YouChat, ChatSonic, Google's Bard, Microsoft Bing Assistant, HuggingChat, Jasper AI, and Quora's Poe. The study evaluates the strengths and weaknesses of each tool, focusing on their architecture, training algorithms, and performance in NLP tasks. It also explores the transformative impact of generative AI in NLP, including its integration with search engines, privacy concerns, and ethical implications. The analysis highlights the potential of generative AI in various applications, while also addressing challenges such as data limitations, computational costs, and ethical issues like technology misuse and regulatory challenges. The paper also discusses AI planning techniques in NLP, including classical planning, probabilistic planning, hierarchical planning, temporal planning, knowledge-driven planning, and neural planning models. These techniques are vital for achieving specific goals in NLP tasks. The study concludes that generative AI tools have significant advantages that outweigh their disadvantages, but their use requires careful consideration of ethical and practical challenges. The paper contributes to the academic discourse on human-computer interaction by providing a comprehensive overview of the current state of generative AI, its challenges, ethical considerations, and potential applications.This paper presents a comparative analysis of nine top generative artificial intelligence (AI) tools for natural language processing (NLP), including ChatGPT, Perplexity AI, YouChat, ChatSonic, Google's Bard, Microsoft Bing Assistant, HuggingChat, Jasper AI, and Quora's Poe. The study evaluates the strengths and weaknesses of each tool, focusing on their architecture, training algorithms, and performance in NLP tasks. It also explores the transformative impact of generative AI in NLP, including its integration with search engines, privacy concerns, and ethical implications. The analysis highlights the potential of generative AI in various applications, while also addressing challenges such as data limitations, computational costs, and ethical issues like technology misuse and regulatory challenges. The paper also discusses AI planning techniques in NLP, including classical planning, probabilistic planning, hierarchical planning, temporal planning, knowledge-driven planning, and neural planning models. These techniques are vital for achieving specific goals in NLP tasks. The study concludes that generative AI tools have significant advantages that outweigh their disadvantages, but their use requires careful consideration of ethical and practical challenges. The paper contributes to the academic discourse on human-computer interaction by providing a comprehensive overview of the current state of generative AI, its challenges, ethical considerations, and potential applications.