diff --git a/docs/src/tutorials/algorithms/benders_decomposition.jl b/docs/src/tutorials/algorithms/benders_decomposition.jl
index 376a4fd19ff..7ca882bedee 100644
--- a/docs/src/tutorials/algorithms/benders_decomposition.jl
+++ b/docs/src/tutorials/algorithms/benders_decomposition.jl
@@ -239,34 +239,27 @@ y_optimal = optimal_ret.y
 lazy_model = Model(GLPK.Optimizer)
 @variable(lazy_model, x[1:dim_x] >= 0, Int)
 @variable(lazy_model, θ >= M)
-@objective(lazy_model, Min, θ)
+@objective(lazy_model, Min, c_1' * x + θ)
 print(lazy_model)
 
 # What differs is that we write a callback function instead of a loop:
 
-k = 0
-
-"""
-    my_callback(cb_data)
-
-A callback that implements Benders decomposition. Note how similar it is to the
-inner loop of the iterative method.
-"""
+number_of_subproblem_solves = 0
 function my_callback(cb_data)
-    global k += 1
+    status = callback_node_status(cb_data, lazy_model)
+    if status != MOI.CALLBACK_NODE_STATUS_INTEGER
+        ## Only add the constraint if `x` is an integer feasible solution
+        return
+    end
     x_k = callback_value.(cb_data, x)
     θ_k = callback_value(cb_data, θ)
-    lower_bound = c_1' * x_k + θ_k
+    global number_of_subproblem_solves += 1
     ret = solve_subproblem(x_k)
-    upper_bound = c_1' * x_k + c_2' * ret.y
-    gap = (upper_bound - lower_bound) / upper_bound
-    print_iteration(k, lower_bound, upper_bound, gap)
-    if gap < ABSOLUTE_OPTIMALITY_GAP
-        println("Terminating with the optimal solution")
-        return
+    if θ_k < (ret.obj - 1e-6)
+        ## Only add the constraint if θ_k violates the constraint
+        cut = @build_constraint(θ >= ret.obj + -ret.π' * A_1 * (x .- x_k))
+        MOI.submit(lazy_model, MOI.LazyConstraint(cb_data), cut)
     end
-    cut = @build_constraint(θ >= ret.obj + -ret.π' * A_1 * (x .- x_k))
-    MOI.submit(model, MOI.LazyConstraint(cb_data), cut)
     return
 end
 
@@ -276,8 +269,12 @@ set_attribute(lazy_model, MOI.LazyConstraintCallback(), my_callback)
 
 optimize!(lazy_model)
 
-# Note how this problem also takes 4 iterations to converge, but the sequence
-# of bounds is different compared to the iterative method.
+# For this model, the callback algorithm required more solves of the subproblem:
+
+number_of_subproblem_solves
+
+# But for larger problems, you can expect the callback algorithm to be more
+# efficient than the iterative algorithm.
 
 # Finally, we can obtain the optimal solution: