Artificial intelligence (AI) is a key to strategic operational efficiency and competitiveness for enterprises. In this context, Grok, developed by xAI, has rapidly attracted attention in the enterprise solutions space as a large language model (LLM) equipped with real-time data processing and advanced reasoning capabilities.

AI tools are indispensable for improving operational efficiency, enabling data-driven decision-making, and strengthening market competitiveness. For companies that require rapid responses in global markets and highly accurate communication in Japanese business contexts, selecting the right AI tool carries strategic significance.

In today’s business environment, leveraging AI tools has become key to improving operational efficiency, enhancing decision-making, and strengthening competitiveness. Choosing the right AI tool directly translates into cost savings and productivity gains.

This article compares ChatGPT from OpenAI and Perplexity from Perplexity AI, Inc., detailing their features, business applications, and pricing models. By doing so, it provides concrete information to help select the AI tool best suited to your company’s needs. Let us begin by defining ChatGPT and Perplexity.

With the rapid evolution of generative AI, enterprises are accelerating the adoption of AI tools to improve operational efficiency and enhance competitiveness. Microsoft Copilot, built on OpenAI technology and integrated into Microsoft 365 and GitHub, provides intelligent assistance across applications such as Word, Excel, PowerPoint, Outlook, and Teams. By automating tasks such as document drafting, data analysis, and meeting summarization, Copilot helps employees boost productivity and focus on higher-value work.

Generative AI has become a core driver of operational efficiency and innovation in enterprises. Among these models, Claude — the large language model (LLM) developed by Anthropic — has attracted attention for prioritizing safety and human-centered design. Its unique approach, Constitutional AI, aims to produce ethically guided and more transparent responses.

With advances in large language models (LLMs), the scope of generative AI use in enterprises and research institutions has expanded more than ever. However, simply using an LLM as-is is often insufficient to meet real-world business requirements. For example, when you want to incorporate the latest information or need responses grounded in internal knowledge, an LLM alone cannot handle it. In addition, for long-text processing and complex context understanding, gaps in knowledge and hallucinations (hallucination) become issues.