Artificial Intelligence platforms now provide multiple large language models (LLMs) to perform different tasks. If you are using platforms like Antigravity, choosing the right AI model can help you generate better results while saving credits and resources.
In this article, we will explain the common AI models available in Antigravity and what tasks they are best suited for.
1. Gemini 3.1 Pro – Best for Architecture and Complex Planning
Gemini 3.1 Pro is designed for complex reasoning and deep analysis. This model is powerful but usually consumes more credits compared to lighter models.
Best use cases
- Software architecture planning
- SaaS product design
- Database schema design
- API architecture
- Security planning
Example tasks
- Designing a monitoring platform architecture
- Creating a microservices design for a SaaS application
- Planning database structures for large applications
Because this model performs deep reasoning, it should ideally be used for planning and strategy, not repeated code generation.
2. Gemini 3.1 Flash – Best for Fast Content and UI Generation
Gemini 3.1 Flash is optimized for speed. It generates responses quickly and uses fewer credits than larger reasoning models.
Best use cases
- UI components
- Small code snippets
- SEO content
- JSON structures
- Landing page content
Example tasks
- Generating React components
- Writing Tailwind CSS layouts
- Creating simple API request formats
For frequent tasks like UI generation, this model is a good choice.
3. Claude Sonnet 4.6 – Best for Coding and Backend Logic
Claude Sonnet 4.6 is one of the best models for programming tasks. It performs well when writing structured code and handling backend logic.
Best use cases
- Backend development
- API development
- Code debugging
- Refactoring code
- Writing automation scripts
Example tasks
- Creating Node.js APIs
- Writing backend monitoring services
- Fixing application errors
For developers and DevOps engineers, this model can be very useful for writing production-ready code.
4. Claude Opus 4.6 – Best for Large Projects and Advanced Reasoning
Claude Opus 4.6 is a more powerful model that can handle larger prompts and complex workflows.
Best use cases
- Full application generation
- Complex debugging
- AI workflow design
- Multi-file projects
Example tasks
- Generating full SaaS applications
- Designing complex backend systems
- Large-scale AI project planning
Because it is a high-capacity model, it should be used when deep reasoning and large outputs are required.
5. GPT-OSS 120B – Best for DevOps and System Tasks
GPT-OSS 120B is a large open-source model that works well for system-level tasks and scripting.
Best use cases
- Linux commands
- Bash scripting
- Docker configuration
- DevOps automation
- Log analysis
Example tasks
- Writing deployment scripts
- Creating Dockerfiles
- Analyzing server logs
This model is especially useful for engineers working with infrastructure and automation.
Best Strategy for Using Multiple AI Models
Instead of using a single model for everything, the best approach is to combine different models based on the task.
A recommended workflow is:
- Use Gemini Pro for planning and architecture
- Use Claude Sonnet for backend code development
- Use Gemini Flash for frontend UI generation
- Use GPT-OSS for DevOps scripts and automation
- Use Claude Opus for large project generation
This method improves efficiency and reduces AI credit usage.
Final Thoughts
Modern AI platforms offer multiple models because each model is optimized for different types of tasks. By selecting the correct model for your work, developers and engineers can build applications faster, reduce costs, and improve productivity.
Whether you are building SaaS platforms, working with DevOps automation, or generating UI components, understanding the strengths of each AI model can help you make the most of AI-powered development tools.