diff --git a/tests/test_inference.py b/tests/test_inference.py
index dc45c409..55bafb21 100644
--- a/tests/test_inference.py
+++ b/tests/test_inference.py
@@ -5,7 +5,7 @@ from timm import list_models, create_model
 
 
 @pytest.mark.timeout(300)
-@pytest.mark.parametrize('model_name', list_models())
+@pytest.mark.parametrize('model_name', list_models(exclude_filters='*efficientnet_l2*'))
 @pytest.mark.parametrize('batch_size', [1])
 def test_model_forward(model_name, batch_size):
   """Run a single forward pass with each model"""
diff --git a/timm/models/registry.py b/timm/models/registry.py
index c15f5414..2b8a3717 100644
--- a/timm/models/registry.py
+++ b/timm/models/registry.py
@@ -42,12 +42,14 @@ def _natural_key(string_):
     return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
 
 
-def list_models(filter='', module='', pretrained=False):
+def list_models(filter='', module='', pretrained=False, exclude_filters=''):
     """ Return list of available model names, sorted alphabetically
 
     Args:
         filter (str) - Wildcard filter string that works with fnmatch
         module (str) - Limit model selection to a specific sub-module (ie 'gen_efficientnet')
+        pretrained (bool) - Include only models with pretrained weights if True
+        exclude_filters (str or list[str]) - Wildcard filters to exclude models after including them with filter
 
     Example:
         model_list('gluon_resnet*') -- returns all models starting with 'gluon_resnet'
@@ -58,7 +60,14 @@ def list_models(filter='', module='', pretrained=False):
     else:
         models = _model_entrypoints.keys()
     if filter:
-        models = fnmatch.filter(models, filter)
+        models = fnmatch.filter(models, filter)  # include these models
+    if exclude_filters:
+        if not isinstance(exclude_filters, list):
+            exclude_filters = [exclude_filters]
+        for xf in exclude_filters:
+            exclude_models = fnmatch.filter(models, xf)  # exclude these models
+            if len(exclude_models):
+                models = set(models).difference(exclude_models)
     if pretrained:
         models = _model_has_pretrained.intersection(models)
     return list(sorted(models, key=_natural_key))