Choosing the right intelligence for your specific workflow.
Each AI model supports different strengths. To help you decide which model to use, this guide provides real-world examples of common developer tasks along with sample prompts, responses, and guidance on why a particular model is a good fit.
These examples show how models vary in their reasoning style, response depth, and ability to handle visual input. Use them to compare output quality and choose the right model for your specific workflow.
Best for: Speed, responsiveness, and general-purpose reasoning.
If you're working on tasks that require broad knowledge, fast iteration, or basic code understanding, GPT-4.1 is likely the model to use.
Writing helper functions for access control and adding docstrings.
Best for: Simple, repetitive tasks and low latency.
Designed for cost-efficiency and real-time suggestions. Optimized for speed, allowing fast iteration on small code changes.
Filtering a list of user records to include only active users and sorting them by date.
# Filter to only include active users
active_users = [user for user in users if user["active"]]
# Sort active users by signup_date
active_users_sorted = sorted(active_users, key=lambda user: user["signup_date"])Best for: Visual context, UI inspection, and diagram analysis.
Supports image input to help with UI debugging, diagram interpretation, and layout analysis.
Generating class structures from a UML diagram image of a shopping cart system.
Best for: Everyday coding support and documentation.
Offers helpful, direct answers without over-complicating tasks. Consumes fewer resources than larger models.
Implementing unit and integration tests to ensure edge case coverage.
Best for: Multi-file refactoring, maintenance, and complex problem solving.
Excels at maintaining context across large codebases and complex logic chains.
Modernizing a legacy COBOL application by rewriting it in Node.js, requiring understanding of unfamiliar source code and logic conversion.