
1. Introduction
In recent years, the evolution of generative AI has been remarkable, especially large language models (LLMs: Large Language Models), which have been increasingly implemented across various industries for tasks such as improving work efficiency, information gathering, and planning. New models are emerging daily, and while options like ChatGPT, Gemini, Perplexity, and Grok are expanding, there are also many situations where users find themselves wondering "Which one should I use?" or "Which one best fits my purpose?".
This article introduces the major LLMs by comparing their features, functions, and usability, and recommends models based on different use cases. Rather than advocating for a specific tool, it focuses on making it easier to choose based on "what purpose you want to use it for." We hope this will be helpful for those who are trying to incorporate LLMs into their work for the first time or those who want to try using multiple models.
- Table of Contents
- 1. Introduction
- 2. Overview of Major LLMs
- 3. Features, Strengths, and Weaknesses of Each LLM
- 4. Comparison of Pricing Structures and Usage Restrictions (As of March 2026)
- 5. Recommended LLMs by Use Case
- 6. Summary
- 7. FAQ (Frequently Asked Questions)
- 8. Human Science Teacher Data Creation, LLM RAG Data Structuring Outsourcing Service
2. Overview of Major LLMs
This article briefly introduces the four LLMs covered.
>What is an LLM? Easy-to-understand explanation of business applications | AI & Annotation Blog |
ChatGPT (OpenAI)
ChatGPT is a large language model developed by OpenAI and is currently one of the most well-known LLMs used by many companies and individuals. The model is continuously updated as part of the "GPT" series, steadily evolving in terms of naturalness of dialogue, response accuracy, and processing speed. It excels at natural conversation and document generation, with an intuitive interface, making it widely supported as an introduction to generative AI. Additionally, through a partnership with Microsoft, integration with Office products (such as Word and Excel) and the Azure OpenAI Service for business use is progressing.
Additionally, the paid plans offer flexibility suitable for business use, including access to high-performance models (GPT-5 series), plugins, and custom GPTs. The top-tier paid plan, the Enterprise Plan, also provides security features and management functions tailored for companies, designed with full-scale business utilization in mind.
Gemini (Google)
Gemini is a multimodal generative AI developed by Google DeepMind, evolved from the former "Bard" series. It is built on Google Search technology and its strong integration with other Google services (such as Gmail, Google Docs, YouTube, etc.) is a key strength. Gemini features multimodal capabilities that handle not only text but also images and PDFs, with its range of support expanding through continuous updates from Google. This enables it to, for example, read PDF-format meeting materials to create summaries or generate reports including charts and tables, thus supporting documents containing information beyond just text.
Additionally, by linking with a Google account, users can access information and settings tailored to their environment, making it highly convenient for both personal and business use. For users who prioritize compatibility with Google products, it can be considered a strong option that allows for natural adoption and integration.
Perplexity (Perplexity AI)
Perplexity is an AI service characterized by its style of providing answers while searching and referencing real-time web information. It responds to user questions by presenting multiple related sources and summarizing the key points, making it suitable as a reliable information-gathering tool with cited references. Since answers clearly indicate source information such as URLs and article titles, it is a major strength that users can quickly obtain data backed by evidence for business or research purposes.
Furthermore, the paid plan allows users to switch between multiple advanced LLMs such as GPT-4 and Claude 3, enabling model selection according to the purpose. Responses in Japanese are also stable, with outputs that concisely summarize key points and have a clear sentence structure, which is well received. Perplexity's unique approach, combining search functionality and summary generation, makes it a powerful tool for business professionals and researchers who want to efficiently organize information.
Grok (xAI)
Grok is an interactive AI developed by xAI, led by Elon Musk, and is notably built with integration into the social media platform X (formerly Twitter) in mind. It particularly excels in accessing highly real-time information such as trending topics and user posts that are posted daily on X, giving it strong capabilities in understanding and responding to current events. Because it provides responses that take into account the flow of information centered on social media, it is also expected to be useful for trend analysis and marketing purposes.
Additionally, compared to other LLMs, its conversational style often includes casual and humorous outputs, making it a user-friendly design for those who want to enjoy more natural and human-like interactions. It is expected to become even more refined as xAI’s proprietary model in the future, representing a presence that suggests new possibilities for generative AI centered on integration with social media platforms.
>Rising Demand for Domain-Specific LLMs and Their Background | AI & Annotation Blog |
3. Features, Strengths, and Weaknesses of Each LLM
The results of comparing each model in terms of performance and features are summarized below.
| LLM | Developer | Tendencies in Japanese Output | Research and search integration | Range of output formats | UI characteristics | Main use cases |
|---|---|---|---|---|---|---|
| ChatGPT | OpenAI | Natural and stable Japanese. Highly suitable for business documents | Web browsing functionality available | Wide range including text, code, image generation, and data analysis | UI optimized for general business tasks | Proposal creation, summarization, and work efficiency improvement |
| Gemini | Google DeepMind | Natural output based on Google foundational models | Google Search integration | Supports multimodal input such as text, images, and PDFs | High compatibility with Google products | Document comprehension and multimodal analysis |
| Perplexity | Perplexity AI | Strength in summarizing key points with sources | Source citation based on web search | Text-centered | Design with an easy-to-view list of sources | Research and information organization |
| Grok | xAI | Strengths in Real-Time Capability and Understanding SNS Context | X Data Integration | Text-centered | Design Integrated with X | Trend Analysis, SNS Post Support |
4. Comparison of Pricing Structures and Usage Restrictions (As of June 2025)
Many LLMs can be started for free, but some require payment to use high-performance models. Below is a comparison of the main pricing structures.
| LLM | Availability of Free Plan | Paid Plan (Reference) | API Availability | Main Limitations of Free Version |
|---|---|---|---|---|
| ChatGPT | Available | Individual Paid Plan: Approximately $20/month | Available | Restrictions on available models and number of messages / Lower priority during congestion |
| Gemini | Available | Google One Paid Plan: Approximately 3,000 yen/month | Available | Model performance, usage frequency, and some features are limited |
| Perplexity | Available | Pro: About $20/month | Available | Usage limits on advanced models / Some feature restrictions |
| Grok | Available (with conditions) | Paid plan: Around $30/month | Available | Restrictions on advanced features (DeepSearch mode and Think mode) |
5. Recommended LLMs by Use Case
■Business Use (Data Analysis, Presentation Creation, Team Collaboration): ChatGPT
ChatGPT is useful when you want to quickly draft reports or proposals, or when you want to organize meeting notes and minutes into a polished form. It can generate natural and easy-to-read Japanese and excels in logical structure, making it suitable for situations where business documents need to be completed in a short time.
Also, if you specify the format and tone in advance, it will organize the document accordingly, making it convenient when you want to unify the document style within the team. By creating a custom GPT, it is also possible to provide responses that reflect internal rules and terminology.
■Information Search and Fact-Checking: Perplexity
Perplexity is invaluable when you want to grasp market trends or research a specific topic with evidence. It provides answers with sources by referring to the latest information on the web in response to your questions, allowing you to verify "where this information came from" when creating research materials or reports.
When you want to investigate complex topics from a comprehensive perspective, you can utilize the Deep Research feature to obtain organized answers from multiple related viewpoints. It excels in situations where you need to collect information balancing both accuracy and speed.
■Real-time Information and Social Media Analysis: Grok
Grok is useful when you want to quickly grasp trending topics on social media or monitor reactions to your brand or services. It is integrated with X (formerly Twitter), making it easy to capture trends and user responses in real time, which is ideal for situations requiring time-sensitive decisions.
For example, during a campaign, analyzing "which types of posts are increasing" or "how user evaluations are changing" can help adjust marketing strategies and review PR policies.
■Security-Focused Use Cases (Handling Confidential Data): ChatGPT
If you want to use an LLM for tasks that handle highly confidential data such as customer information and contract data, ChatGPT’s Enterprise Plan (paid) is a reliable choice. It complies with regulations like GDPR and HIPAA and meets the information protection standards required for business operations.
Especially in industries that require strict compliance, such as healthcare, finance, and legal sectors, it is important to choose an LLM with robust security measures. This option is suitable when you want to establish a system to safely utilize generative AI within your organization.
■Document Processing and Multimodal Support: Gemini
For example, Gemini is useful when you want to load and summarize meeting materials in PDF format or organize and explain the contents of reports that include charts and tables. Its multimodal capabilities come into play when organizing information across multiple files or processing materials that include images and layouts.
Additionally, since it can be integrated with Google services such as Gmail, Google Drive, and Calendar, it functions especially efficiently in work environments that regularly use Google Workspace. When you want to handle everyday tasks, where information tends to be scattered, through a single interface, Gemini is a strong option.
Note:
The performance of each LLM depends on the user's needs and usage environment. Please check the official websites for the latest features and pricing.
6. Summary
The major LLMs—ChatGPT, Gemini, Perplexity, and Grok—each have different development backgrounds, areas of expertise, and usage policies. Rather than simply using a well-known LLM, choosing a model that fits your specific goals and work can greatly impact productivity.
Especially when considering business use, careful selection including factors such as pricing, security, and data handling policies is crucial. We hope this article serves as a helpful guide when incorporating LLMs into your work.
>What AI and Machine Learning Can Do: 12 Use Cases by Industry | AI & Annotation Blog |
7. FAQ (Frequently Asked Questions)
Q1. Should multiple LLMs be used together, or should you focus on just one?
It is not always necessary to focus on just one. In actual business operations, cases of using multiple LLMs according to the purpose are increasing.
For example, by dividing roles such as using ChatGPT for drafting and organizing text, Perplexity for information gathering, Gemini for reading materials including PDFs and images, and Grok for real-time topic analysis on social media, you can leverage the strengths of each.
On the other hand, from the perspective of internal deployment and guideline development, introducing too many tools may complicate management. It is more practical to first decide on one main LLM and then gradually introduce other models as supplementary tools as needed.
Q2. Is it possible to use only the free plan for business purposes?
It is possible for simple use, but if continuous business use is expected, considering a paid plan is more realistic. The free version may have limitations on the number of uses, response speed, and the number of times advanced models can be used.
Also, when using as a team or integrating with APIs, security and data management policies become important. At the stage of considering full-scale implementation, it is crucial to compare not only costs but also stability and management features.
Q3. Which LLM is safe when handling confidential information?
When handling confidential information, it is essential to check the data usage policies of each service. In general free plans, input data may be used for training to improve quality.
For corporate use, it is safer to choose contract types with clear data protection policies, such as ChatGPT's Enterprise Plan. Additionally, establishing internal operational rules that clearly define "information that can be input and information that must not be input" is a prerequisite for safe utilization.
8. Human Science Teacher Data Creation, LLM RAG Data Structuring Outsourcing Service
Over 48 million pieces of training data created
At Human Science, we are involved in AI model development projects across various industries, starting with natural language processing, including medical support, automotive, IT, manufacturing, and construction. Through direct transactions with many companies, including GAFAM, we have provided over 48 million high-quality training data. We handle a wide range of training data creation, data labeling, and data structuring, from small-scale projects to long-term large projects with a team of 150 annotators, regardless of the industry.
Resource management without crowdsourcing
At Human Science, we do not use crowdsourcing. Instead, projects are handled by personnel who are contracted with us directly. Based on a solid understanding of each member's practical experience and their evaluations from previous projects, we form teams that can deliver maximum performance.
Not only for creating training data but also supports the creation and structuring of generative AI LLM datasets
In addition to creating labeled and identified training data for data organization, we also support the structuring of document data for generative AI and LLM RAG construction. Since our founding, we have been engaged in manual production as a primary business and service, leveraging our unique know-how gained from extensive knowledge of various document structures to provide optimal solutions.
Secure room available on-site
Within our Shinjuku office at Human Science, we have secure rooms that meet ISMS standards. Therefore, we can guarantee security, even for projects that include highly confidential data. We consider the preservation of confidentiality to be extremely important for all projects. When working remotely as well, our information security management system has received high praise from clients, because not only do we implement hardware measures, we continuously provide security training to our personnel.
In-house Support
We provide staffing services for annotation-experienced personnel and project managers tailored to your tasks and situation. It is also possible to organize a team stationed at your site. Additionally, we support the training of your operators and project managers, assist in selecting tools suited to your circumstances, and help build optimal processes such as automation and work methods to improve quality and productivity. We are here to support your challenges related to annotation and data labeling.
>Annotation Outsourcing Service|Over 48 Million Cases Including GAFAM|Human Science

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