The SWE-Pro benchmark addresses the lack of realistic evaluation frameworks for software performance optimization by introducing a repository-level dataset derived from 102 expert-written optimizations. Unlike previous benchmarks that oversimplify tasks, SWE-Pro pairs each task with parameterized tests to evaluate runtime, peak memory, and Time-Weighted Memory Usage under noise-aware conditions. The study reveals that current Large Language Models struggle significantly with these complex requirements, showing negligible runtime gains and nearly non-existent memory optimizations. In sharp contrast, expert implementations achieved an aggregate speedup of 15.5x and a peak memory reduction of 171.3x across the benchmark tasks. Expert-written improvements were observed in 91.2% of tasks for runtime and 65.7% for peak memory. These findings expose a substantial gap between current LLM capabilities and the demands of expert-level engineering.