Claude Sonnet 4.5 Code Review Benchmark

Why benchmark LLMs for code review? Most LLM benchmarks focus on code generation -- writing new code from scratch, solving algorithmic puzzles, or completing functions. But code review is a fundamentally different task. A model that excels at generating code may perform poorly when asked to find subtle bugs in someone else's code, assess security implications of a design choice, or evaluate whether a refactor actually improves maintainability. Code review requires a different set of capabilities than code generation. When reviewing code, the model needs to: Understand intent from context. The model must infer what the code is supposed to do based on surrounding code, PR descriptions, commit messages, and file naming conventions -- not from an explicit prompt. Identify what is wrong without being told what to look for. Unlike code generation where the task is clearly defined, code review is open-ended. The model needs to independently surface bugs, security issues, performance problems,