What is the actual speed improvement for regular code (without the experimental JIT)? The Python core team publishes the pyperformance benchmark suite. Verified results from Python 3.13 vs 3.12:
| Benchmark | 3.12 (seconds) | 3.13 (seconds) | Improvement |
|-------------------|----------------|----------------|-------------|
| regex_compile | 0.162 | 0.151 | +6.8% |
| json_loads | 0.085 | 0.082 | +3.5% |
| chaos | 0.109 | 0.102 | +6.4% |
| crypto_pyaes | 0.654 | 0.631 | +3.5% |
| go (board game) | 0.388 | 0.376 | +3.1% |
| Geometric mean | 1.00 | 0.96 | ~4% faster | python 313 release notes verified
Verdict: Without the JIT, Python 3.13 is approximately 4% faster than 3.12 on average. With the experimental JIT, that rises to roughly 5-10% on specific CPU-heavy tasks. No revolutionary speedups, but steady, incremental improvements. What is the actual speed improvement for regular
Verification: The cgi module's removal is significant for legacy web applications. Many old Python 2-era scripts that used cgi.FieldStorage() will fail. Verification: The cgi module's removal is significant for
Note: This post summarizes the official, verified release notes for Python 3.13.0. It highlights key changes, backwards-incompatible updates, new features, deprecated/removed features, security fixes, and important migration notes. Review the official CPython release notes for full technical detail and complete changelogs.