SheetMagic Integrations

OpenRouter serves as a consolidated interface for various large language models (LLMs). It efficiently identifies the most competitive prices and optimal latencies/throughputs from numerous providers, allowing users to establish their own priorities for these factors. There’s no need to modify your existing code when switching between different models or providers, making the process seamless. Users also have the option to select and finance their own models. Instead of relying solely on flawed evaluations, OpenRouter enables the comparison of models based on their actual usage across various applications. You can engage with multiple models simultaneously in a chatroom setting. The payment for model usage can be managed by users, developers, or a combination of both, and the availability of models may fluctuate. Additionally, you can access information about models, pricing, and limitations through an API. OpenRouter intelligently directs requests to the most suitable providers for your chosen model, in line with your specified preferences. By default, it distributes requests evenly among the leading providers to ensure maximum uptime; however, you have the flexibility to tailor this process by adjusting the provider object within the request body. Prioritizing providers that have maintained a stable performance without significant outages in the past 10 seconds is also a key feature. Ultimately, OpenRouter simplifies the process of working with multiple LLMs, making it a valuable tool for developers and users alike.

Collaborate seamlessly on online spreadsheets from any device and in real-time, making teamwork more efficient. Create a definitive reference point for your data with user-friendly sharing and simultaneous editing capabilities. Enhance your workflow by utilizing comments to assign tasks and keep discussions active. Features like Smart Fill and formula recommendations allow for quicker analysis while minimizing mistakes. Quickly gain insights by posing questions about your data using straightforward language. Sheets integrates smoothly with other beloved Google applications, streamlining your tasks. Effortlessly analyze data collected through Google Forms in Sheets, or incorporate your spreadsheet charts into Google Slides and Docs. Additionally, you can respond to comments directly within Gmail and easily showcase your spreadsheets during Google Meet presentations, making collaboration even more effective. This interconnectedness not only saves time but also enhances productivity across all your projects.

OpenAI aims to guarantee that artificial general intelligence (AGI)—defined as highly autonomous systems excelling beyond human capabilities in most economically significant tasks—serves the interests of all humanity. While we intend to develop safe and advantageous AGI directly, we consider our mission successful if our efforts support others in achieving this goal. You can utilize our API for a variety of language-related tasks, including semantic search, summarization, sentiment analysis, content creation, translation, and beyond, all with just a few examples or by clearly stating your task in English. A straightforward integration provides you with access to our continuously advancing AI technology, allowing you to explore the API’s capabilities through these illustrative completions and discover numerous potential applications.

Gemini, an innovative AI chatbot from Google, aims to boost creativity and productivity through engaging conversations in natural language. Available on both web and mobile platforms, it works harmoniously with multiple Google services like Docs, Drive, and Gmail, allowing users to create content, condense information, and handle tasks effectively. With its multimodal abilities, Gemini can analyze and produce various forms of data, including text, images, and audio, which enables it to deliver thorough support in numerous scenarios. As it continually learns from user engagement, Gemini customizes its responses to provide personalized and context-sensitive assistance, catering to diverse user requirements. Moreover, this adaptability ensures that it evolves alongside its users, making it a valuable tool for anyone looking to enhance their workflow and creativity.

GPT-4, or Generative Pre-trained Transformer 4, is a highly advanced unsupervised language model that is anticipated for release by OpenAI. As the successor to GPT-3, it belongs to the GPT-n series of natural language processing models and was developed using an extensive dataset comprising 45TB of text, enabling it to generate and comprehend text in a manner akin to human communication. Distinct from many conventional NLP models, GPT-4 operates without the need for additional training data tailored to specific tasks. It is capable of generating text or responding to inquiries by utilizing only the context it creates internally. Demonstrating remarkable versatility, GPT-4 can adeptly tackle a diverse array of tasks such as translation, summarization, question answering, sentiment analysis, and more, all without any dedicated task-specific training. This ability to perform such varied functions further highlights its potential impact on the field of artificial intelligence and natural language processing.

The GPT-3.5 series represents an advancement in OpenAI's large language models, building on the capabilities of its predecessor, GPT-3. These models excel at comprehending and producing human-like text, with four primary variations designed for various applications. The core GPT-3.5 models are intended to be utilized through the text completion endpoint, while additional models are optimized for different endpoint functionalities. Among these, the Davinci model family stands out as the most powerful, capable of executing any task that the other models can handle, often requiring less detailed input. For tasks that demand a deep understanding of context, such as tailoring summaries for specific audiences or generating creative content, the Davinci model tends to yield superior outcomes. However, this enhanced capability comes at a cost, as Davinci requires more computing resources, making it pricier for API usage and slower compared to its counterparts. Overall, the advancements in GPT-3.5 not only improve performance but also expand the range of potential applications.

The GPT-4 model represents a significant advancement in AI, being a large multimodal system capable of handling both text and image inputs while producing text outputs, which allows it to tackle complex challenges with a level of precision unmatched by earlier models due to its extensive general knowledge and enhanced reasoning skills. Accessible through the OpenAI API for subscribers, GPT-4 is also designed for chat interactions, similar to gpt-3.5-turbo, while proving effective for conventional completion tasks via the Chat Completions API. This state-of-the-art version of GPT-4 boasts improved features such as better adherence to instructions, JSON mode, consistent output generation, and the ability to call functions in parallel, making it a versatile tool for developers. However, it is important to note that this preview version is not fully prepared for high-volume production use, as it has a limit of 4,096 output tokens. Users are encouraged to explore its capabilities while keeping in mind its current limitations.

Claude represents a sophisticated artificial intelligence language model capable of understanding and producing text that resembles human communication. Anthropic is an organization dedicated to AI safety and research, aiming to develop AI systems that are not only dependable and understandable but also controllable. While contemporary large-scale AI systems offer considerable advantages, they also present challenges such as unpredictability and lack of transparency; thus, our mission is to address these concerns. Currently, our primary emphasis lies in advancing research to tackle these issues effectively; however, we anticipate numerous opportunities in the future where our efforts could yield both commercial value and societal benefits. As we continue our journey, we remain committed to enhancing the safety and usability of AI technologies.

DALL·E 3 showcases a remarkable enhancement in its understanding of subtlety and intricate details compared to its predecessors, enabling a smooth transformation of concepts into highly precise images. Unlike many contemporary text-to-image systems that often overlook specific terms or phrases, necessitating users to master the art of prompt crafting, DALL·E 3 marks a significant advancement in our capability to produce visuals that closely align with the text provided. When using the same prompt, DALL·E 3 demonstrates considerable enhancements over DALL·E 2, showcasing its improved accuracy and creativity. Built directly upon the foundation of ChatGPT, DALL·E 3 allows you to collaborate with ChatGPT as a creative partner to refine and develop your prompts. You can simply articulate your vision, whether it be a concise phrase or an elaborate description, and ChatGPT will generate customized, detailed prompts for DALL·E 3 to bring your ideas to fruition. Furthermore, if you find an image appealing yet feel it needs some adjustments, you can easily request ChatGPT to make modifications with just a few simple words, ensuring the final result perfectly aligns with your vision. This seamless interaction elevates the creative process, making it even more intuitive and user-friendly.

GPT-4o, with the "o" denoting "omni," represents a significant advancement in the realm of human-computer interaction by accommodating various input types such as text, audio, images, and video, while also producing outputs across these same formats. Its capability to process audio inputs allows for responses in as little as 232 milliseconds, averaging 320 milliseconds, which closely resembles the response times seen in human conversations. In terms of performance, it maintains the efficiency of GPT-4 Turbo for English text and coding while showing marked enhancements in handling text in other languages, all while operating at a much faster pace and at a cost that is 50% lower via the API. Furthermore, GPT-4o excels in its ability to comprehend vision and audio, surpassing the capabilities of its predecessors, making it a powerful tool for multi-modal interactions. This innovative model not only streamlines communication but also broadens the possibilities for applications in diverse fields.

The latest advancement, GPT-4 with vision (GPT-4V), allows users to direct GPT-4 to examine image inputs that they provide, marking a significant step in expanding its functionalities. Many in the field see the integration of various modalities, including images, into large language models (LLMs) as a crucial area for progress in artificial intelligence. By introducing multimodal capabilities, these LLMs can enhance the effectiveness of traditional language systems, creating innovative interfaces and experiences while tackling a broader range of tasks. This system card focuses on assessing the safety features of GPT-4V, building upon the foundational safety measures established for GPT-4. Here, we delve more comprehensively into the evaluations, preparations, and strategies aimed at ensuring safety specifically concerning image inputs, thereby reinforcing our commitment to responsible AI development. Such efforts not only safeguard users but also promote the responsible deployment of AI innovations.

Llama (Large Language Model Meta AI) stands as a cutting-edge foundational large language model aimed at helping researchers push the boundaries of their work within this area of artificial intelligence. By providing smaller yet highly effective models like Llama, the research community can benefit even if they lack extensive infrastructure, thus promoting greater accessibility in this dynamic and rapidly evolving domain. Creating smaller foundational models such as Llama is advantageous in the landscape of large language models, as it demands significantly reduced computational power and resources, facilitating the testing of innovative methods, confirming existing research, and investigating new applications. These foundational models leverage extensive unlabeled datasets, making them exceptionally suitable for fine-tuning across a range of tasks. We are offering Llama in multiple sizes (7B, 13B, 33B, and 65B parameters), accompanied by a detailed Llama model card that outlines our development process while adhering to our commitment to Responsible AI principles. By making these resources available, we aim to empower a broader segment of the research community to engage with and contribute to advancements in AI.