Add information about pbs performance

BENCHMARKS.md

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+## python-build-standalone performance
+
+<img 
+    width="1820" height="1730" 
+    alt="Shows a chart with violin plots with benchmark results" 
+    src="https://github.com/user-attachments/assets/5499f9ce-ee02-4485-baa2-9d982fe457e6"
+/>
+
+The figure above compares CPython 3.14.6 performance across several distributions using pyperformance. Each [violin](https://en.wikipedia.org/wiki/Violin_plot) represents the distribution of per-benchmark mean runtime ratios between an alternative CPython distribution and python-build-standalone on the same platform and architecture. Ratios greater than 1 indicate that python-build-standalone was faster; ratios less than 1 indicate that the alternative distribution was faster.
+
+The horizontal axis uses a logarithmic scale. The vertical marker within each violin indicates the geometric mean of the runtime ratios, which is also expressed as a percentage beside each distribution.
+
+From top to bottom, the distributions shown are:
+* The Docker `python:3.14` image for x86-64, providing CPython 3.14.6.
+* A conda-forge Python 3.14.6 environment for `linux-64`.
+* The system Python 3.14.6 in a `fedora:44` x86-64 Docker container.
+* The system Python 3.14.6 in a `debian:forky` x86-64 Docker container.
+* CPython 3.14.6 from the [Python.org macOS installer](https://www.python.org/ftp/python/3.14.6/python-3.14.6-macos11.pkg) on an arm64 Mac.
+* A conda-forge Python 3.14.6 environment for `osx-arm64`.
+* CPython 3.14.6 installed through Homebrew on an arm64 Mac.
+* CPython 3.14.6 from the Python.org Windows installer for x86-64.
+* A conda-forge Python 3.14.6 environment for `win-64`.
+
+The reference interpreter for each comparison was the corresponding platform- and architecture-matched python-build-standalone CPython 3.14.6 distribution from the [`20260623` release](https://github.com/astral-sh/python-build-standalone/releases#release-20260623), installed using `uv`.
+
+Benchmarks were run in early to mid-July 2026 and reflect the distributions and packages available during that period.
+
+Supporting material, including [Dockerfiles](https://github.com/jjhelmus/cpython-benchmarks/tree/main/containers), commands for creating conda environments, and scripts for running the benchmarks, can be found in [jjhelmus/cpython-benchmarks](https://github.com/jjhelmus/cpython-benchmarks). This repository also contains the raw data and the [script](https://github.com/jjhelmus/cpython-benchmarks/blob/main/plot_pbs_314_comparison.py) used to produce the figure.
+
+### Benchmark methodology
+
+Benchmarks were executed from a virtual environment created with the reference interpreter into which pyperformance 1.14.0 was installed. Results were collected using:
+``` shell
+pyperformance run --rigorous --warmups 2 --output <logfile>
+```
+
+The complete benchmark suite was run at least twice to assess consistency.
+
+Linux benchmarks were run inside Docker containers on an Ubuntu 24.04 host with an Intel Core i9-9900K processor. Hyper-Threading and Intel SpeedStep were disabled.
+
+macOS benchmarks were run on a MacBook Pro with an Apple M5 Max processor.
+
+Windows benchmarks were run on a Windows 11 host with an Intel Core i5-9500 processor. Intel Turbo Boost was disabled; this processor does not support Hyper-Threading.