This is exactly why PyPy blew both Cannoli and CPython away in the microbenchmarks used for analysis. As I've said elsewhere, the focus was on comparing Cannoli (unoptimized) to Cannoli (optimized) and not a direct comparison to CPython or PyPy. However, the microbenchmarks were running iterations of 1-10 million, giving the JIT plenty of time to find beneficial traces in the PyPy interpreter.