diff --git a/doc/conf.py b/doc/conf.py
index f749b188b3274..dbe4213505918 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -502,6 +502,9 @@ def add_js_css_files(app, pagename, templatename, context, doctree):
     "auto_examples/linear_model/plot_ols_ridge_variance": (
         "auto_examples/linear_model/plot_ols_ridge"
     ),
+    "auto_examples/linear_model/plot_sgd_comparison": (
+        "auto_examples/linear_model/plot_sgd_loss_functions"
+    ),
 }
 html_context["redirects"] = redirects
 for old_link in redirects:
diff --git a/doc/modules/sgd.rst b/doc/modules/sgd.rst
index d1f2211bca8d8..9703146fb2de5 100644
--- a/doc/modules/sgd.rst
+++ b/doc/modules/sgd.rst
@@ -194,7 +194,6 @@ algorithm, available as a solver in :class:`LogisticRegression`.
 - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_separating_hyperplane.py`
 - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_iris.py`
 - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_weighted_samples.py`
-- :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_comparison.py`
 - :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py`
   (See the Note in the example)
 
diff --git a/examples/linear_model/plot_sgd_comparison.py b/examples/linear_model/plot_sgd_comparison.py
deleted file mode 100644
index c24ad14a79532..0000000000000
--- a/examples/linear_model/plot_sgd_comparison.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"""
-==================================
-Comparing various online solvers
-==================================
-An example showing how different online solvers perform
-on the hand-written digits dataset.
-"""
-
-# Authors: The scikit-learn developers
-# SPDX-License-Identifier: BSD-3-Clause
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-from sklearn import datasets
-from sklearn.linear_model import (
-    LogisticRegression,
-    PassiveAggressiveClassifier,
-    Perceptron,
-    SGDClassifier,
-)
-from sklearn.model_selection import train_test_split
-
-heldout = [0.95, 0.90, 0.75, 0.50, 0.01]
-# Number of rounds to fit and evaluate an estimator.
-rounds = 10
-X, y = datasets.load_digits(return_X_y=True)
-
-classifiers = [
-    ("SGD", SGDClassifier(max_iter=110)),
-    ("ASGD", SGDClassifier(max_iter=110, average=True)),
-    ("Perceptron", Perceptron(max_iter=110)),
-    (
-        "Passive-Aggressive I",
-        PassiveAggressiveClassifier(max_iter=110, loss="hinge", C=1.0, tol=1e-4),
-    ),
-    (
-        "Passive-Aggressive II",
-        PassiveAggressiveClassifier(
-            max_iter=110, loss="squared_hinge", C=1.0, tol=1e-4
-        ),
-    ),
-    (
-        "SAG",
-        LogisticRegression(max_iter=110, solver="sag", tol=1e-1, C=1.0e4 / X.shape[0]),
-    ),
-]
-
-xx = 1.0 - np.array(heldout)
-
-for name, clf in classifiers:
-    print("training %s" % name)
-    rng = np.random.RandomState(42)
-    yy = []
-    for i in heldout:
-        yy_ = []
-        for r in range(rounds):
-            X_train, X_test, y_train, y_test = train_test_split(
-                X, y, test_size=i, random_state=rng
-            )
-            clf.fit(X_train, y_train)
-            y_pred = clf.predict(X_test)
-            yy_.append(1 - np.mean(y_pred == y_test))
-        yy.append(np.mean(yy_))
-    plt.plot(xx, yy, label=name)
-
-plt.legend(loc="upper right")
-plt.xlabel("Proportion train")
-plt.ylabel("Test Error Rate")
-plt.show()