diff --git a/examples/YOLOv8-Action-Recognition/action_recognition.py b/examples/YOLOv8-Action-Recognition/action_recognition.py
index aad74375..0853981e 100644
--- a/examples/YOLOv8-Action-Recognition/action_recognition.py
+++ b/examples/YOLOv8-Action-Recognition/action_recognition.py
@@ -263,7 +263,7 @@ def crop_and_pad(frame, box, margin_percent):
 
 
 def run(
-    weights: str = "yolov8n.pt",
+    weights: str = "yolo11n.pt",
     device: str = "",
     source: str = "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
     output_path: Optional[str] = None,
@@ -279,7 +279,7 @@ def run(
     Run action recognition on a video source using YOLO for object detection and a video classifier.
 
     Args:
-        weights (str): Path to the YOLO model weights. Defaults to "yolov8n.pt".
+        weights (str): Path to the YOLO model weights. Defaults to "yolo11n.pt".
         device (str): Device to run the model on. Use 'cuda' for NVIDIA GPU, 'mps' for Apple Silicon, or 'cpu'. Defaults to auto-detection.
         source (str): Path to mp4 video file or YouTube URL. Defaults to a sample YouTube video.
         output_path (Optional[str], optional): Path to save the output video. Defaults to None.
@@ -421,7 +421,7 @@ def run(
 def parse_opt():
     """Parse command line arguments."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--weights", type=str, default="yolov8n.pt", help="ultralytics detector model path")
+    parser.add_argument("--weights", type=str, default="yolo11n.pt", help="ultralytics detector model path")
     parser.add_argument("--device", default="", help='cuda device, i.e. 0 or 0,1,2,3 or cpu/mps, "" for auto-detection')
     parser.add_argument(
         "--source",
diff --git a/ultralytics/data/annotator.py b/ultralytics/data/annotator.py
index 5cb0058d..30d02d9d 100644
--- a/ultralytics/data/annotator.py
+++ b/ultralytics/data/annotator.py
@@ -21,7 +21,7 @@ def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="",
 
     Examples:
         >>> from ultralytics.data.annotator import auto_annotate
-        >>> auto_annotate(data="ultralytics/assets", det_model="yolov8n.pt", sam_model="mobile_sam.pt")
+        >>> auto_annotate(data="ultralytics/assets", det_model="yolo11n.pt", sam_model="mobile_sam.pt")
 
     Notes:
         - The function creates a new directory for output if not specified.
diff --git a/ultralytics/engine/model.py b/ultralytics/engine/model.py
index 4f8209c6..c5b63eed 100644
--- a/ultralytics/engine/model.py
+++ b/ultralytics/engine/model.py
@@ -72,16 +72,16 @@ class Model(nn.Module):
 
     Examples:
         >>> from ultralytics import YOLO
-        >>> model = YOLO("yolov8n.pt")
+        >>> model = YOLO("yolo11n.pt")
         >>> results = model.predict("image.jpg")
-        >>> model.train(data="coco128.yaml", epochs=3)
+        >>> model.train(data="coco8.yaml", epochs=3)
         >>> metrics = model.val()
         >>> model.export(format="onnx")
     """
 
     def __init__(
         self,
-        model: Union[str, Path] = "yolov8n.pt",
+        model: Union[str, Path] = "yolo11n.pt",
         task: str = None,
         verbose: bool = False,
     ) -> None:
@@ -106,7 +106,7 @@ class Model(nn.Module):
             ImportError: If required dependencies for specific model types (like HUB SDK) are not installed.
 
         Examples:
-            >>> model = Model("yolov8n.pt")
+            >>> model = Model("yolo11n.pt")
             >>> model = Model("path/to/model.yaml", task="detect")
             >>> model = Model("hub_model", verbose=True)
         """
@@ -168,7 +168,7 @@ class Model(nn.Module):
                 Results object.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> results = model("https://ultralytics.com/images/bus.jpg")
             >>> for r in results:
             ...     print(f"Detected {len(r)} objects in image")
@@ -192,7 +192,7 @@ class Model(nn.Module):
         Examples:
             >>> Model.is_triton_model("http://localhost:8000/v2/models/yolov8n")
             True
-            >>> Model.is_triton_model("yolov8n.pt")
+            >>> Model.is_triton_model("yolo11n.pt")
             False
         """
         from urllib.parse import urlsplit
@@ -217,7 +217,7 @@ class Model(nn.Module):
         Examples:
             >>> Model.is_hub_model("https://hub.ultralytics.com/models/MODEL")
             True
-            >>> Model.is_hub_model("yolov8n.pt")
+            >>> Model.is_hub_model("yolo11n.pt")
             False
         """
         return model.startswith(f"{HUB_WEB_ROOT}/models/")
@@ -274,7 +274,7 @@ class Model(nn.Module):
 
         Examples:
             >>> model = Model()
-            >>> model._load("yolov8n.pt")
+            >>> model._load("yolo11n.pt")
             >>> model._load("path/to/weights.pth", task="detect")
         """
         if weights.lower().startswith(("https://", "http://", "rtsp://", "rtmp://", "tcp://")):
@@ -307,7 +307,7 @@ class Model(nn.Module):
                 information about supported model formats and operations.
 
         Examples:
-            >>> model = Model("yolov8n.pt")
+            >>> model = Model("yolo11n.pt")
             >>> model._check_is_pytorch_model()  # No error raised
             >>> model = Model("yolov8n.onnx")
             >>> model._check_is_pytorch_model()  # Raises TypeError
@@ -338,7 +338,7 @@ class Model(nn.Module):
             AssertionError: If the model is not a PyTorch model.
 
         Examples:
-            >>> model = Model("yolov8n.pt")
+            >>> model = Model("yolo11n.pt")
             >>> model.reset_weights()
         """
         self._check_is_pytorch_model()
@@ -349,7 +349,7 @@ class Model(nn.Module):
             p.requires_grad = True
         return self
 
-    def load(self, weights: Union[str, Path] = "yolov8n.pt") -> "Model":
+    def load(self, weights: Union[str, Path] = "yolo11n.pt") -> "Model":
         """
         Loads parameters from the specified weights file into the model.
 
@@ -367,7 +367,7 @@ class Model(nn.Module):
 
         Examples:
             >>> model = Model()
-            >>> model.load("yolov8n.pt")
+            >>> model.load("yolo11n.pt")
             >>> model.load(Path("path/to/weights.pt"))
         """
         self._check_is_pytorch_model()
@@ -391,7 +391,7 @@ class Model(nn.Module):
             AssertionError: If the model is not a PyTorch model.
 
         Examples:
-            >>> model = Model("yolov8n.pt")
+            >>> model = Model("yolo11n.pt")
             >>> model.save("my_model.pt")
         """
         self._check_is_pytorch_model()
@@ -428,7 +428,7 @@ class Model(nn.Module):
             TypeError: If the model is not a PyTorch model.
 
         Examples:
-            >>> model = Model("yolov8n.pt")
+            >>> model = Model("yolo11n.pt")
             >>> model.info()  # Prints model summary
             >>> info_list = model.info(detailed=True, verbose=False)  # Returns detailed info as a list
         """
@@ -451,7 +451,7 @@ class Model(nn.Module):
             TypeError: If the model is not a PyTorch nn.Module.
 
         Examples:
-            >>> model = Model("yolov8n.pt")
+            >>> model = Model("yolo11n.pt")
             >>> model.fuse()
             >>> # Model is now fused and ready for optimized inference
         """
@@ -483,7 +483,7 @@ class Model(nn.Module):
             AssertionError: If the model is not a PyTorch model.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> image = "https://ultralytics.com/images/bus.jpg"
             >>> embeddings = model.embed(image)
             >>> print(embeddings[0].shape)
@@ -520,7 +520,7 @@ class Model(nn.Module):
                 Results object.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> results = model.predict(source="path/to/image.jpg", conf=0.25)
             >>> for r in results:
             ...     print(r.boxes.data)  # print detection bounding boxes
@@ -581,7 +581,7 @@ class Model(nn.Module):
             AttributeError: If the predictor does not have registered trackers.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> results = model.track(source="path/to/video.mp4", show=True)
             >>> for r in results:
             ...     print(r.boxes.id)  # print tracking IDs
@@ -624,8 +624,8 @@ class Model(nn.Module):
             AssertionError: If the model is not a PyTorch model.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
-            >>> results = model.val(data="coco128.yaml", imgsz=640)
+            >>> model = YOLO("yolo11n.pt")
+            >>> results = model.val(data="coco8.yaml", imgsz=640)
             >>> print(results.box.map)  # Print mAP50-95
         """
         custom = {"rect": True}  # method defaults
@@ -666,7 +666,7 @@ class Model(nn.Module):
             AssertionError: If the model is not a PyTorch model.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> results = model.benchmark(data="coco8.yaml", imgsz=640, half=True)
             >>> print(results)
         """
@@ -716,7 +716,7 @@ class Model(nn.Module):
             RuntimeError: If the export process fails due to errors.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> model.export(format="onnx", dynamic=True, simplify=True)
             'path/to/exported/model.onnx'
         """
@@ -771,8 +771,8 @@ class Model(nn.Module):
             ModuleNotFoundError: If the HUB SDK is not installed.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
-            >>> results = model.train(data="coco128.yaml", epochs=3)
+            >>> model = YOLO("yolo11n.pt")
+            >>> results = model.train(data="coco8.yaml", epochs=3)
         """
         self._check_is_pytorch_model()
         if hasattr(self.session, "model") and self.session.model.id:  # Ultralytics HUB session with loaded model
@@ -836,7 +836,7 @@ class Model(nn.Module):
             AssertionError: If the model is not a PyTorch model.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> results = model.tune(use_ray=True, iterations=20)
             >>> print(results)
         """
@@ -871,7 +871,7 @@ class Model(nn.Module):
             AssertionError: If the model is not a PyTorch model.
 
         Examples:
-            >>> model = Model("yolov8n.pt")
+            >>> model = Model("yolo11n.pt")
             >>> model = model._apply(lambda t: t.cuda())  # Move model to GPU
         """
         self._check_is_pytorch_model()
@@ -896,7 +896,7 @@ class Model(nn.Module):
             AttributeError: If the model or predictor does not have a 'names' attribute.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> print(model.names)
             {0: 'person', 1: 'bicycle', 2: 'car', ...}
         """
@@ -924,7 +924,7 @@ class Model(nn.Module):
             AttributeError: If the model is not a PyTorch nn.Module instance.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> print(model.device)
             device(type='cuda', index=0)  # if CUDA is available
             >>> model = model.to("cpu")
@@ -946,7 +946,7 @@ class Model(nn.Module):
             (object | None): The transform object of the model if available, otherwise None.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> transforms = model.transforms
             >>> if transforms:
             ...     print(f"Model transforms: {transforms}")
@@ -975,9 +975,9 @@ class Model(nn.Module):
         Examples:
             >>> def on_train_start(trainer):
             ...     print("Training is starting!")
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> model.add_callback("on_train_start", on_train_start)
-            >>> model.train(data="coco128.yaml", epochs=1)
+            >>> model.train(data="coco8.yaml", epochs=1)
         """
         self.callbacks[event].append(func)
 
@@ -994,7 +994,7 @@ class Model(nn.Module):
                 recognized by the Ultralytics callback system.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> model.add_callback("on_train_start", lambda: print("Training started"))
             >>> model.clear_callback("on_train_start")
             >>> # All callbacks for 'on_train_start' are now removed
@@ -1024,7 +1024,7 @@ class Model(nn.Module):
         modifications, ensuring consistent behavior across different runs or experiments.
 
         Examples:
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> model.add_callback("on_train_start", custom_function)
             >>> model.reset_callbacks()
             # All callbacks are now reset to their default functions
diff --git a/ultralytics/engine/results.py b/ultralytics/engine/results.py
index 57cc4b04..7d8192d6 100644
--- a/ultralytics/engine/results.py
+++ b/ultralytics/engine/results.py
@@ -676,7 +676,7 @@ class Results(SimpleClass):
 
         Examples:
             >>> from ultralytics import YOLO
-            >>> model = YOLO("yolov8n.pt")
+            >>> model = YOLO("yolo11n.pt")
             >>> results = model("path/to/image.jpg")
             >>> for result in results:
             ...     result.save_txt("output.txt")
diff --git a/ultralytics/engine/tuner.py b/ultralytics/engine/tuner.py
index 2f42eb60..0330abb8 100644
--- a/ultralytics/engine/tuner.py
+++ b/ultralytics/engine/tuner.py
@@ -12,7 +12,7 @@ Example:
     ```python
     from ultralytics import YOLO
 
-    model = YOLO("yolov8n.pt")
+    model = YOLO("yolo11n.pt")
     model.tune(data="coco8.yaml", epochs=10, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
     ```
 """
@@ -54,7 +54,7 @@ class Tuner:
         ```python
         from ultralytics import YOLO
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         model.tune(data="coco8.yaml", epochs=10, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
         ```
 
@@ -62,7 +62,7 @@ class Tuner:
         ```python
         from ultralytics import YOLO
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         model.tune(space={key1: val1, key2: val2})  # custom search space dictionary
         ```
     """
diff --git a/ultralytics/models/yolo/detect/predict.py b/ultralytics/models/yolo/detect/predict.py
index 7a1799f2..136f9882 100644
--- a/ultralytics/models/yolo/detect/predict.py
+++ b/ultralytics/models/yolo/detect/predict.py
@@ -14,7 +14,7 @@ class DetectionPredictor(BasePredictor):
         from ultralytics.utils import ASSETS
         from ultralytics.models.yolo.detect import DetectionPredictor
 
-        args = dict(model="yolov8n.pt", source=ASSETS)
+        args = dict(model="yolo11n.pt", source=ASSETS)
         predictor = DetectionPredictor(overrides=args)
         predictor.predict_cli()
         ```
diff --git a/ultralytics/models/yolo/detect/train.py b/ultralytics/models/yolo/detect/train.py
index 5be24c94..e0dbb367 100644
--- a/ultralytics/models/yolo/detect/train.py
+++ b/ultralytics/models/yolo/detect/train.py
@@ -24,7 +24,7 @@ class DetectionTrainer(BaseTrainer):
         ```python
         from ultralytics.models.yolo.detect import DetectionTrainer
 
-        args = dict(model="yolov8n.pt", data="coco8.yaml", epochs=3)
+        args = dict(model="yolo11n.pt", data="coco8.yaml", epochs=3)
         trainer = DetectionTrainer(overrides=args)
         trainer.train()
         ```
diff --git a/ultralytics/models/yolo/detect/val.py b/ultralytics/models/yolo/detect/val.py
index aef60d65..05db8cba 100644
--- a/ultralytics/models/yolo/detect/val.py
+++ b/ultralytics/models/yolo/detect/val.py
@@ -22,7 +22,7 @@ class DetectionValidator(BaseValidator):
         ```python
         from ultralytics.models.yolo.detect import DetectionValidator
 
-        args = dict(model="yolov8n.pt", data="coco8.yaml")
+        args = dict(model="yolo11n.pt", data="coco8.yaml")
         validator = DetectionValidator(args=args)
         validator()
         ```
diff --git a/ultralytics/models/yolo/model.py b/ultralytics/models/yolo/model.py
index 692537dd..63819603 100644
--- a/ultralytics/models/yolo/model.py
+++ b/ultralytics/models/yolo/model.py
@@ -11,7 +11,7 @@ from ultralytics.utils import ROOT, yaml_load
 class YOLO(Model):
     """YOLO (You Only Look Once) object detection model."""
 
-    def __init__(self, model="yolov8n.pt", task=None, verbose=False):
+    def __init__(self, model="yolo11n.pt", task=None, verbose=False):
         """Initialize YOLO model, switching to YOLOWorld if model filename contains '-world'."""
         path = Path(model)
         if "-world" in path.stem and path.suffix in {".pt", ".yaml", ".yml"}:  # if YOLOWorld PyTorch model
diff --git a/ultralytics/nn/autobackend.py b/ultralytics/nn/autobackend.py
index 22d4e6b0..78949cb6 100644
--- a/ultralytics/nn/autobackend.py
+++ b/ultralytics/nn/autobackend.py
@@ -82,7 +82,7 @@ class AutoBackend(nn.Module):
     @torch.no_grad()
     def __init__(
         self,
-        weights="yolov8n.pt",
+        weights="yolo11n.pt",
         device=torch.device("cpu"),
         dnn=False,
         data=None,
diff --git a/ultralytics/utils/benchmarks.py b/ultralytics/utils/benchmarks.py
index fe6e2a65..653f48d3 100644
--- a/ultralytics/utils/benchmarks.py
+++ b/ultralytics/utils/benchmarks.py
@@ -47,7 +47,7 @@ from ultralytics.utils.torch_utils import get_cpu_info, select_device
 
 
 def benchmark(
-    model=WEIGHTS_DIR / "yolov8n.pt",
+    model=WEIGHTS_DIR / "yolo11n.pt",
     data=None,
     imgsz=160,
     half=False,
@@ -76,7 +76,7 @@ def benchmark(
     Examples:
         Benchmark a YOLO model with default settings:
         >>> from ultralytics.utils.benchmarks import benchmark
-        >>> benchmark(model="yolov8n.pt", imgsz=640)
+        >>> benchmark(model="yolo11n.pt", imgsz=640)
     """
     import pandas as pd  # scope for faster 'import ultralytics'
 
diff --git a/ultralytics/utils/checks.py b/ultralytics/utils/checks.py
index 1f530944..76455e23 100644
--- a/ultralytics/utils/checks.py
+++ b/ultralytics/utils/checks.py
@@ -458,7 +458,7 @@ def check_torchvision():
             )
 
 
-def check_suffix(file="yolov8n.pt", suffix=".pt", msg=""):
+def check_suffix(file="yolo11n.pt", suffix=".pt", msg=""):
     """Check file(s) for acceptable suffix."""
     if file and suffix:
         if isinstance(suffix, str):
diff --git a/ultralytics/utils/downloads.py b/ultralytics/utils/downloads.py
index 6751086a..f356f47b 100644
--- a/ultralytics/utils/downloads.py
+++ b/ultralytics/utils/downloads.py
@@ -425,7 +425,7 @@ def attempt_download_asset(file, repo="ultralytics/assets", release="v8.3.0", **
 
     Example:
         ```python
-        file_path = attempt_download_asset("yolov8n.pt", repo="ultralytics/assets", release="latest")
+        file_path = attempt_download_asset("yolo11n.pt", repo="ultralytics/assets", release="latest")
         ```
     """
     from ultralytics.utils import SETTINGS  # scoped for circular import
diff --git a/ultralytics/utils/files.py b/ultralytics/utils/files.py
index 29c68d48..059e9588 100644
--- a/ultralytics/utils/files.py
+++ b/ultralytics/utils/files.py
@@ -183,7 +183,7 @@ def get_latest_run(search_dir="."):
     return max(last_list, key=os.path.getctime) if last_list else ""
 
 
-def update_models(model_names=("yolov8n.pt",), source_dir=Path("."), update_names=False):
+def update_models(model_names=("yolo11n.pt",), source_dir=Path("."), update_names=False):
     """
     Updates and re-saves specified YOLO models in an 'updated_models' subdirectory.
 
@@ -195,7 +195,7 @@ def update_models(model_names=("yolov8n.pt",), source_dir=Path("."), update_name
     Examples:
         Update specified YOLO models and save them in 'updated_models' subdirectory:
         >>> from ultralytics.utils.files import update_models
-        >>> model_names = ("yolov8n.pt", "yolov8s.pt")
+        >>> model_names = ("yolo11n.pt", "yolov8s.pt")
         >>> update_models(model_names, source_dir=Path("/models"), update_names=True)
     """
     from ultralytics import YOLO
diff --git a/ultralytics/utils/tuner.py b/ultralytics/utils/tuner.py
index 1329bfe6..c60022c0 100644
--- a/ultralytics/utils/tuner.py
+++ b/ultralytics/utils/tuner.py
@@ -28,7 +28,7 @@ def run_ray_tune(
         from ultralytics import YOLO
 
         # Load a YOLOv8n model
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
 
         # Start tuning hyperparameters for YOLOv8n training on the COCO8 dataset
         result_grid = model.tune(data="coco8.yaml", use_ray=True)