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This document provides a detailed comparison of the Large Language Models available within the CometAI platform, including those developed by Anthropic, OpenAI, Mistral, Meta, Gemini, Grok, & NVIDIA. Each model is analyzed based on its design purpose, technical specifications, pricing, and optimal use cases to help users make informed decisions based on their unique needs. Review this document from time to time for updated Model Information in CometAI.

This document provides a high-level comparison of leading language models currently available within the CometAI platform. The models, developed by Anthropic, OpenAI, Mistral, and Meta, are evaluated across performance strengths, cost-efficiency, and best-fit use cases for academic and enterprise environments. This guide is designed to help faculty and staff select the most appropriate model for tasks such as summarization, writing, planning, coding, and visual document interpretation.

This article provides information about CometAI model lifecycle status, outlining upcoming deprecations and End-of-Life (EOL) dates to help users plan for continuity. It includes a master deprecation table with removal dates and recommended successor models, plus notes explaining key lifecycle definitions and implications of using deprecated models.

This article covers about the current issues and bugs that are present in CometAI platform

This article introduces Generative AI and its role in higher education, highlighting both its benefits like enhancing learning and research, and risks such as misuse and misinformation. UTD supports responsible Gen AI use under academic and ethical guidelines, provides access to approved tools, and encourages best practices for effective, compliant usage.

This article covers the range of AI and Gen AI tools offered by The University of Texas at Dallas (UTD). It provides an overview of the available tools, their accessibility for students, faculty, and staff and key details about their functionalities

This article explains Retrieval Augmented Generation (RAG) Agents—AI systems that combine information retrieval with generative models to deliver accurate, context-rich responses. It outlines UTD’s support for using such tools responsibly, details their capabilities, and offers best practices for ethical and effective use. It also provides guidance on how to access approved RAG services through UTD.

AWS Builder Center launched in July 2025, consolidating AWS Developer Center and community.aws for a streamlined user experience. It's open to faculty, staff, and students.
Register at builder.aws.com by creating an AWS Builder ID and setting up a profile to get started.

This article guide provides step-by-step instructions on how to securely log in & log out to the CometAI platform using your UT Dallas NetID and password.

Learn how to initiate RAG based conversation in CometAI

This guide explains how to use the Import Conversations and Export Conversations options in the CometAI Platform. Import Conversations let users upload. json (Conversation) files, and Export Conversations let users download all conversations from their current device and browser in a .json file.

This document explains how to organize and manage your conversations in Comet AI. It covers creating folders, moving and sorting conversations, renaming items, searching, deleting, and archiving conversations and folders. Use these features to keep your workspace organized and efficient.

This article covers about how to export Chats & Conversations in CometAI Platform.

Learn how to create your own RAG assistant, streamline repetitive processes, ensure consistent outputs, and add tailored functionality to your workspace.

This article provides a comprehensive overview of the advanced configuration features within CometAI. Understanding and utilizing these settings can significantly enhance the model's performance, efficiency, and relevance for academic research, coursework, and development projects.