Clarify available models.

[wt17]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @wt17, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request improves the documentation in the README.md by providing more precise information about the availability of different Gemini models across various platforms. The update aims to enhance user clarity by explicitly categorizing models based on whether they are accessible via both the Gemini Developer API and Vertex AI Client, or solely through the Gemini Developer API.

Highlights

• Model Availability Clarification: The README.md file has been updated to clearly distinguish which Gemini models are available through the Gemini Developer API and which are also accessible via the Vertex AI Client.
• Gemini 3 Models Categorization: The gemini-3-flash-preview and gemini-3-pro-preview models are now explicitly listed under a new section indicating their availability exclusively on the Gemini Developer API.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request updates the README.md to clarify which models are supported by the Gemini Developer API and which are supported by Vertex AI. This is a useful clarification for users. However, the application does not currently validate the chosen model against the selected environment (Gemini API vs. Vertex AI), which can lead to runtime errors if an unsupported combination is used. I've added a comment to the README.md suggesting that argument handling be improved to prevent this and provide a better user experience.

[gemini-code-assist]

While this clarification is helpful, the application code does not enforce these model restrictions based on the selected environment (Gemini Developer API vs. Vertex AI). The --model argument accepts all listed models regardless of the environment, which can lead to runtime errors if an invalid combination is chosen. To improve user experience, consider adding logic to main.py to validate that the selected model is compatible with the chosen environment. For example, after parsing arguments, you could check the USE_VERTEXAI environment variable and raise an error if an incompatible model is selected.