Enabling Smarter Model Routing for AI Coding | AI Coding Cost

Frontier AI coding models can offer higher benchmark performance for complex or security-critical work, but the text argues that using them for every task may add unnecessary cost when lower-tier models can meet functional requirements for routine changes such as configurations, refactors, scaffolding, and small fixes. It distinguishes functional correctness, security, reliability, and maintainability as separate quality dimensions, noting that models may perform differently across them and that benchmark success does not eliminate risks such as vulnerabilities, runtime defects, or code smells. Using Claude Sonnet 4.5 and Opus 4.5 as an example, it states that a 1.67-fold price premium corresponded to a 3.7-percentage-point difference on SWE-bench Verified, illustrating the potential value of task-specific routing. The proposed approach is to set a minimum correctness threshold by task class, select the least costly qualifying model, and use independent verification to identify and reduce remaining security, reliability, and maintainability risks. SonarQube, Sonar Vortex, Gitar, and related agents are presented as model-agnostic tools that enforce quality policies throughout development, provide feedback during coding and review, and create auditable evidence that generated code has been checked against consistent standards.

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