Tokyo AI Integrations

OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome. Apply our API to any language task — semantic search, summarization, sentiment analysis, content generation, translation, and more — with only a few examples or by specifying your task in English. One simple integration gives you access to our constantly-improving AI technology. Explore how you integrate with the API with these sample completions.

Claude is a next-generation AI assistant developed by Anthropic to help individuals and teams solve complex problems with safety, accuracy, and reliability at its core. It is designed to support a wide range of tasks, including writing, editing, coding, data analysis, and research. Claude allows users to create and iterate on documents, websites, graphics, and code directly within chat using collaborative tools like Artifacts. The platform supports file uploads, image analysis, and data visualization to enhance productivity and understanding. Claude is available across web, iOS, and Android, making it accessible wherever work happens. With built-in web search and extended reasoning capabilities, Claude helps users find information and think through challenging problems more effectively. Anthropic emphasizes security, privacy, and responsible AI development to ensure Claude can be trusted in professional and personal workflows.

Gemini is Google’s advanced AI assistant designed to help users think, create, learn, and complete tasks with a new level of intelligence. Powered by Google’s most capable models, including Gemini 3, it enables users to ask complex questions, generate content, analyze information, and explore ideas through natural conversation. Gemini can create images, videos, summaries, study plans, and first drafts while also providing feedback on uploaded files and written work. The platform is grounded in Google Search, allowing it to deliver accurate, up-to-date information and support deep follow-up questions. Gemini connects seamlessly with Google apps like Gmail, Docs, Calendar, Maps, YouTube, and Photos to help users complete tasks without switching tools. Features such as Gemini Live, Deep Research, and Gems enhance brainstorming, research, and personalized workflows. Available through flexible free and paid plans, Gemini supports everyday users, students, and professionals across devices.

GPT-4 (Generative Pre-trained Transformer 4) is a large-scale unsupervised language model, yet to be released by OpenAI. GPT-4 is the successor to GPT-3 and part of the GPT-n series of natural language processing models, and was trained on a dataset of 45TB of text to produce human-like text generation and understanding capabilities. Unlike most other NLP models, GPT-4 does not require additional training data for specific tasks. Instead, it can generate text or answer questions using only its own internally generated context as input. GPT-4 has been shown to be able to perform a wide variety of tasks without any task specific training data such as translation, summarization, question answering, sentiment analysis and more.

GPT-3.5 is the next evolution of GPT 3 large language model from OpenAI. GPT-3.5 models can understand and generate natural language. We offer four main models with different levels of power suitable for different tasks. The main GPT-3.5 models are meant to be used with the text completion endpoint. We also offer models that are specifically meant to be used with other endpoints. Davinci is the most capable model family and can perform any task the other models can perform and often with less instruction. For applications requiring a lot of understanding of the content, like summarization for a specific audience and creative content generation, Davinci is going to produce the best results. These increased capabilities require more compute resources, so Davinci costs more per API call and is not as fast as the other models.

DALL·E 3 understands significantly more nuance and detail than our previous systems, allowing you to easily translate your ideas into exceptionally accurate images. Modern text-to-image systems have a tendency to ignore words or descriptions, forcing users to learn prompt engineering. DALL·E 3 represents a leap forward in our ability to generate images that exactly adhere to the text you provide. Even with the same prompt, DALL·E 3 delivers significant improvements over DALL·E 2. DALL·E 3 is built natively on ChatGPT, which lets you use ChatGPT as a brainstorming partner and refiner of your prompts. Just ask ChatGPT what you want to see in anything from a simple sentence to a detailed paragraph. When prompted with an idea, ChatGPT will automatically generate tailored, detailed prompts for DALL·E 3 that bring your idea to life. If you like a particular image, but it’s not quite right, you can ask ChatGPT to make tweaks with just a few words.

GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, image, and video and generates any combination of text, audio, and image outputs. It can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time (opens in a new window) in a conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models.

DALL·E 2 can create original, realistic images and art from a text description. It can combine concepts, attributes, and styles. DALL·E 2 can can expand images beyond what’s in the original canvas, creating expansive new compositions. DALL·E 2 can make realistic edits to existing images from a natural language caption. It can add and remove elements while taking shadows, reflections, and textures into account. DALL·E 2 has learned the relationship between images and the text used to describe them. It uses a process called “diffusion,” which starts with a pattern of random dots and gradually alters that pattern towards an image when it recognizes specific aspects of that image. Our content policy does not allow users to generate violent, adult, or political content, among other categories. We won’t generate images if our filters identify text prompts and image uploads that may violate our policies. We also have automated and human monitoring systems to guard against misuse.

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TypeScript adds additional syntax to JavaScript to support a tighter integration with your editor. Catch errors early in your editor. TypeScript code converts to JavaScript, which runs anywhere JavaScript runs: In a browser, on Node.js or Deno and in your apps. TypeScript understands JavaScript and uses type inference to give you great tooling without additional code. TypeScript was used by 78% of the 2020 State of JS respondents, with 93% saying they would use it again. The most common kinds of errors that programmers write can be described as type errors: a certain kind of value was used where a different kind of value was expected. This could be due to simple typos, a failure to understand the API surface of a library, incorrect assumptions about runtime behavior, or other errors.

Gemini Enterprise is a comprehensive AI platform built by Google Cloud designed to bring the full power of Google’s advanced AI models, agent-creation tools, and enterprise-grade data access into everyday workflows. The solution offers a unified chat interface that lets employees interact with internal documents, applications, data sources, and custom AI agents. At its core, Gemini Enterprise comprises six key components: the Gemini family of large multimodal models, an agent orchestration workbench (formerly Google Agentspace), pre-built starter agents, robust data-integration connectors to business systems, extensive security and governance controls, and a partner ecosystem for tailored integrations. It is engineered to scale across departments and enterprises, enabling users to build no-code or low-code agents that automate tasks, such as research synthesis, customer support response, code assist, contract analysis, and more, while operating within corporate compliance standards.