Table of Contents
This article provides a high-level comparison of leading language models currently available within the CometAI platform. The models, developed by Anthropic, OpenAI, Mistral, Meta, Gemini, Grok, & NVIDIA 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.
Anthropic
Google
Meta
Mistral
NVIDIA
OpenAI
xAI
Note: This model list and associated details are current as of July 2026.
This section shows how to control which models appear in your chat drop-down.
- Open the "Settings" tab by clicking the Paintbrush icon in the left-hand menu
- Select Settings from the menu- a new window.
1Settings — opens CometAI configuration options
- In the pop-up, stay on the default "Configurations" tab.
1Configurations tab under Settings
2Theme toggle — switch between Dark mode and Light mode
3Models shown in blue are currently displayed/available to use
4Scroll bar — more model columns are available to the right
5Save button — applies your configuration changes
Important: The default model for your workspace will always remain active, even if all other models from that provider are disabled.
Asterisk Reminder: If you change any configuration, an asterisk (*) will appear next to the Configurations tab to indicate unsaved changes (Figure 5). To apply your changes, click Save in the bottom-right corner of the window.
To check the pricing details of each model directly in CometAI:
- Open the "Settings" tab by clicking the Paintbrush icon in the left-hand menu. .
- Select Settings from the menu- a new window will open.
1Settings — opens CometAI configuration options
- In the Settings pop-up, select the Model Pricing tab.
1Model Pricing tab under Settings
2Column headers — Input/Output Tokens and cached-token rates
3Provider group label (e.g. OpenAI) groups its models below
4Aspire models — included at no extra cost ($0.00 across the board)
5Paid models — priced per million tokens, varies by model
- You will see a table listing each available model along with pricing details in Tokens per Million.
- The table includes:
- Model name
- Input Tokens (cost for text sent to the model)
- Output Tokens (cost for text generated by the model)
- Cached Tokens (lower-cost responses from prior requests)
Cached Tokens refer to results that are served from the platform’s memory (or “read cache”) when the same input was recently submitted. This reduces compute cost and improves response time for repeated prompts.
For optimal cost management, begin with our lower-cost models, which often deliver excellent results for many use cases. Should you find the output quality insufficient for your requirements, you can progressively move to more advanced (and more expensive) models until you achieve the desired performance level.
1Model selector — shows the currently active model
2(Default) badge — marks the account's default model
3Camera icon — model supports image/vision input
4$ cost indicator — green/yellow/red shows relative price tier
5Menu icon — additional options for that model
- Each model in the drop-down has three icons to the right:
- Supports / Does Not Support Images in Prompts (Shutter/Shutter Disabled images)
- Pricing Indicators (Dollar Sign):
- Green Dollar Sign = Inexpensive
- Yellow Dollar Sign = Moderate Cost
- Red Dollar Sign = Expensive
- Context Window (kebab icon):
- ≣ Large Output Token Limit (≥ 100,000 tokens)
- ≡ Average Output Token Limit (4,096 – 100,000 tokens)
- = Less than Average Output Token Limit (< 4,096 tokens)
1Info icon — opens this Legend popover
2Settings (gear) icon — opens Settings, including Model Pricing
3Model Pricing tab link — jump straight to the full pricing table
Note: Users can hover over the (i) icon above the model selection drop-down to see a Legend pop-up menu. The Legend displays explanations for three icon categories: image support, pricing tiers, and context window limits.
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Users can hover over any model name in the drop-down to view a detailed description, including recommended use cases, supported input types, and training data cut-off.