diff --git a/pyproject.toml b/pyproject.toml
index 06bafb6..9969269 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 [project]
 name = "python-constraint2" # when set back to "python-constraint", don't forget to remove '2' in other places (e.g. README)
 description = "python-constraint is a module for efficiently solving CSPs (Constraint Solving Problems) over finite domains."
-version = "2.2.0" # adhere to PEP440 versioning: https://packaging.python.org/en/latest/guides/distributing-packages-using-setuptools/#id55
+version = "2.2.1" # adhere to PEP440 versioning: https://packaging.python.org/en/latest/guides/distributing-packages-using-setuptools/#id55
 authors = [
     {name = "Gustavo Niemeyer", email = "gustavo@niemeyer.net"},
     {name = "Sébastien Celles", email = "s.celles@gmail.com"},
@@ -35,6 +35,11 @@ classifiers = [
     "Programming Language :: Cython",
     "Topic :: Scientific/Engineering",
     "Topic :: Software Development",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
 ]
 
 # ATTENTION: if anything is changed here, run `poetry update`
diff --git a/tests/test_util_benchmark.py b/tests/test_util_benchmark.py
index 39b4ee0..97f37d9 100644
--- a/tests/test_util_benchmark.py
+++ b/tests/test_util_benchmark.py
@@ -109,8 +109,8 @@ def stddev(column, mean):
     benchmark_std = [stddev(column, mean) for column, mean in zip(transposed_data, benchmark_mean)]
     relative_std = [(s / abs(m)) if m != 0 else 0 for s, m in zip(benchmark_std, benchmark_mean)]
 
-    # calculate mean relative standard deviation and apply threshold (`max(np.mean(np_relative_std), 0.125)`)
-    mean_relative_std = max(sum(relative_std) / len(relative_std), 0.125)
+    # calculate mean relative standard deviation and apply threshold (`max(np.mean(np_relative_std), 0.25)`)
+    mean_relative_std = max(sum(relative_std) / len(relative_std), 0.25)
 
     # calculate performance factor  (`np.mean(np_benchmark_mean / reference_microbenchmark_mean)`)
     performance_factor = sum(bm / rm for bm, rm in zip(benchmark_mean, reference_microbenchmark_mean)) / len(benchmark_mean)