diff --git a/src/MOI_wrapper.jl b/src/MOI_wrapper.jl
index 3d1d319..e54be93 100644
--- a/src/MOI_wrapper.jl
+++ b/src/MOI_wrapper.jl
@@ -746,6 +746,8 @@ function MOI.get(
     elseif haskey(model.affine_outer_to_inner, ci)
         inner_ci = model.affine_outer_to_inner[ci]
         return _original_function(model.affine_constraint_cache[inner_ci])
+    elseif haskey(model.vector_affine_constraint_cache, ci)
+        return _original_function(model.vector_affine_constraint_cache[ci])
     else
         MOI.throw_if_not_valid(model, ci)
         return MOI.get(model.optimizer, attr, ci)
diff --git a/src/parametric_functions.jl b/src/parametric_functions.jl
index d0f1dee..9048865 100644
--- a/src/parametric_functions.jl
+++ b/src/parametric_functions.jl
@@ -62,7 +62,7 @@ function ParametricQuadraticFunction(
     affine_data = Dict{MOI.VariableIndex,T}()
     sizehint!(affine_data, length(v_in_pv))
     affine_data_np = Dict{MOI.VariableIndex,T}()
-    sizehint!(affine_data, length(v))
+    sizehint!(affine_data_np, length(v))
     for term in v
         if term.variable in v_in_pv
             base = get(affine_data, term.variable, zero(T))
@@ -276,12 +276,11 @@ function _delta_parametric_affine_terms(
     # remember a variable may appear more than once in pv
     for term in quadratic_parameter_variable_terms(f)
         p = p_idx(term.variable_1)
-        if !isnan(model.updated_parameters[p])
+        new_p = model.updated_parameters[p]
+        if !isnan(new_p)
             base = get(delta_terms_dict, term.variable_2, zero(T))
             delta_terms_dict[term.variable_2] =
-                base +
-                term.coefficient *
-                (model.updated_parameters[p] - model.parameters[p])
+                base + term.coefficient * (new_p - model.parameters[p])
         end
     end
     return delta_terms_dict
@@ -725,9 +724,9 @@ function _delta_parametric_constant(
         p_idx_val = p_idx(term.scalar_term.variable)
         output_idx = term.output_index
 
-        if !isnan(model.updated_parameters[p_idx_val])
+        new_param_val = model.updated_parameters[p_idx_val]
+        if !isnan(new_param_val)
             old_param_val = model.parameters[p_idx_val]
-            new_param_val = model.updated_parameters[p_idx_val]
             delta_constants[output_idx] +=
                 term.scalar_term.coefficient * (new_param_val - old_param_val)
         end
@@ -772,10 +771,9 @@ function _delta_parametric_affine_terms(
         p_idx_val = p_idx(term.scalar_term.variable_1)
         var = term.scalar_term.variable_2
         output_idx = term.output_index
-        if haskey(model.updated_parameters, p_idx_val) &&
-           !isnan(model.updated_parameters[p_idx_val])
+        new_param_val = model.updated_parameters[p_idx_val]
+        if !isnan(new_param_val)
             old_param_val = model.parameters[p_idx_val]
-            new_param_val = model.updated_parameters[p_idx_val]
             delta_coef =
                 term.scalar_term.coefficient * (new_param_val - old_param_val)
             base = get(delta_terms_dict, (var, output_idx), zero(T))
diff --git a/test/test_MathOptInterface.jl b/test/test_MathOptInterface.jl
index cc9d005..bc15cf5 100644
--- a/test/test_MathOptInterface.jl
+++ b/test/test_MathOptInterface.jl
@@ -2276,6 +2276,25 @@ function test_get_constraint_function_vector()
     return
 end
 
+function test_get_constraint_function_vector_affine()
+    # MOI.get(ConstraintFunction) for a parametric
+    # VectorAffineFunction constraint must return the original function
+    # (with parameter VariableIndex terms), not the solver-side function
+    # (with parameters already substituted into constants).
+    model = POI.Optimizer(MOI.Utilities.Model{Float64}())
+    x = MOI.add_variable(model)
+    p, pc = MOI.add_constrained_variable(model, MOI.Parameter(3.0))
+    terms = [
+        MOI.VectorAffineTerm(1, MOI.ScalarAffineTerm(2.0, x)),
+        MOI.VectorAffineTerm(1, MOI.ScalarAffineTerm(5.0, p)),
+    ]
+    f = MOI.VectorAffineFunction(terms, [0.0])
+    ci = MOI.add_constraint(model, f, MOI.Zeros(1))
+    f2 = MOI.get(model, MOI.ConstraintFunction(), ci)
+    @test canonical_compare(f, f2)
+    return
+end
+
 function test_multiplicative_dual_error()
     model = POI.Optimizer(MOI.Utilities.Model{Float64}())
     x = MOI.add_variable(model)