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

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 an overview of the connectors currently available in CometAI, guidance on how to use them effectively, and a roadmap for future connectors.

This article covers all the updates happening in CometAI for the Year 2026.

The Web Search feature allows CometAI to retrieve and reference information from the internet when generating responses. By combining AI-generated responses with relevant web results, Web Search helps provide more current information, recent updates, and additional sources beyond the model's built-in knowledge.

The Microsoft 365 integration allows CometAI to work with your Microsoft account services - such as OneDrive and SharePoint - directly inside conversations and assistants. Once connected, you can attach files from OneDrive or SharePoint, and ask CometAI to summarize or generate. CometAI only acts when you request it, and it stays within your existing Microsoft 365 permissions.

This article guides on how users can create Assistant group interface - NEW feature launched in CometAI 3.0

This article covers the various user-facing CometAI API endpoints available to users. It includes a detailed description of each endpoint, example API requests and URLs, sample request bodies, expected response formats, and guidance on how to test and view responses using Postman. The article also explains the expected data returned by each endpoint, common success and error responses, and provides examples of what a successful API call looks like.

This article explains how to access & use the CometAI API Feature. It covers the three types of API keys available in CometAI - Owner Keys, System Keys, and Delegate Keys - including their purpose, when to use each key type, and best practices for selecting the appropriate key based on your use case.

By adjusting parameters like Temperature and Response Length, you can tailor the output to be more creative or precise, and more concise or detailed, depending on your needs. This guide explains how to access and use these settings to optimize your results.

A Prompt Template is a reusable structure that simplifies the creation of AI-generated responses for common tasks. Users will be able to define variable placeholders - such as audience, tone, or topic - that can be quickly customized each time the prompt is used. By using templates, users can streamline repetitive tasks like drafting emails or creating social media posts, reducing the need to rewrite similar prompts and improving workflow efficiency.

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 outlines the CometAI 3.2 update for UT Dallas' AI platform, highlighting new model options (including Claude Opus 5, GPT-5.6, and Grok 4.6), a new default model (Aspire - Gemma 4-31B), two new advanced reasoning modes (xHigh and Max), the retirement of legacy models, and channels for reporting issues or attending upcoming office hours.

Layered Assistants allow you to build a hierarchy of AI assistants that work together to handle user requests. A Layered Assistant uses a parent assistant to evaluate incoming questions and automatically route them to the most appropriate child assistant for a response.