When Sonnet Outperforms Opus - Choosing the Right AI Model Tier
The instinct is always to use the best model available. Opus is smarter, so it should produce better code, right? In practice, Sonnet wins on a surprising number of daily coding tasks - and understanding when to use which model saves both time and money.
Where Sonnet Excels
Sonnet is faster. For straightforward tasks - writing utility functions, generating boilerplate, fixing lint errors, writing tests for existing code - speed matters more than depth. You get the result in seconds instead of waiting for Opus to overthink a simple problem.
Sonnet also tends to be more direct. Ask it to write a Python script that parses CSV files, and it gives you a clean solution without over-engineering abstractions you didn't ask for. Opus sometimes adds unnecessary sophistication to simple problems.
For high-volume tasks like processing multiple files, running code reviews across a codebase, or generating documentation, Sonnet's lower cost per token makes parallel execution practical. Five Sonnet agents cost less than one Opus session.
Where Opus Justifies the Cost
Opus earns its premium on tasks requiring deep reasoning - debugging complex race conditions, designing system architecture, refactoring interconnected modules, or understanding subtle business logic. These are problems where thinking depth directly correlates with output quality.
If the task requires holding multiple complex constraints in mind simultaneously, Opus is worth the wait and the cost. If the task has a clear pattern and a known-good approach, Sonnet handles it fine.
The Practical Split
Route 70-80% of daily coding tasks to Sonnet. Reserve Opus for architecture decisions, complex debugging, and anything where the first attempt needs to be right because iteration is expensive. This isn't about being cheap - it's about matching capability to complexity.
The best model for the job isn't always the most powerful one. It's the one that solves your specific problem at the right speed and cost.
Fazm is an open source macOS AI agent. Open source on GitHub.