Running AI models locally for code generation used to mean accepting mediocre output. That changed. In 2026, you have real choices — but picking the wrong model for your use case costs you latency, accuracy, or both. This article breaks down three leading open-weight models on real coding tasks, not marketing claims.
The Testing Setup
Before comparing results, the methodology matters. I ran all three models against 120 code generation tasks across four categories:
Algorithm implementation (sorting, graph traversal, dynamic programming)
API integration (REST clients, retry logic, pagination)
