How to choose your LLM without ruining your Java code

Evaluating AI models for code generation involves assessing not just their ability to execute code but also their reliability, maintainability, and security, as evidenced by the analysis of over a dozen models in the Sonar Leaderboard. The findings highlight significant disparities in code verbosity and security, with some models like Gemini 3 Pro delivering concise and efficient code, while others like GPT-5.2 High produce more verbose outputs. Security assessments reveal that newer models do not inherently offer better code quality, often reintroducing vulnerabilities like SQL injection due to their focus on rapid output over secure practices. Opus 4.5 Thinking emerges as the top choice for tasks requiring high security and business logic due to its low issue density, while Gemini 3 Pro is recommended for general tasks due to its balance of efficiency and code quality. Users are advised to employ tools like SonarQube to scan AI-generated code for vulnerabilities and technical debt, ensuring production readiness.