diff --git a/Project.toml b/Project.toml
index 84c81511..77f40590 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "ForwardDiff"
 uuid = "f6369f11-7733-5829-9624-2563aa707210"
-version = "1.4.0"
+version = "1.4.1"
 
 [deps]
 CommonSubexpressions = "bbf7d656-a473-5ed7-a52c-81e309532950"
diff --git a/ext/ForwardDiffStaticArraysExt.jl b/ext/ForwardDiffStaticArraysExt.jl
index ec8cbc83..bf0ef99a 100644
--- a/ext/ForwardDiffStaticArraysExt.jl
+++ b/ext/ForwardDiffStaticArraysExt.jl
@@ -21,8 +21,6 @@ using DiffResults: DiffResult, ImmutableDiffResult, MutableDiffResult
     end
 end
 
-@inline static_dual_eval(::Type{T}, f::F, x::StaticArray) where {T,F} = f(dualize(T, x))
-
 # To fix method ambiguity issues:
 function LinearAlgebra.eigvals(A::Symmetric{<:Dual{Tg,T,N}, <:StaticArrays.StaticMatrix}) where {Tg,T<:Real,N}
     return ForwardDiff._eigvals(A)
@@ -54,12 +52,12 @@ end
 
 @inline function ForwardDiff.vector_mode_gradient(f::F, x::StaticArray) where {F}
     T = typeof(Tag(f, eltype(x)))
-    return extract_gradient(T, static_dual_eval(T, f, x), x)
+    return extract_gradient(T, f(dualize(T, x)), x)
 end
 
 @inline function ForwardDiff.vector_mode_gradient!(result, f::F, x::StaticArray) where {F}
     T = typeof(Tag(f, eltype(x)))
-    return extract_gradient!(T, result, static_dual_eval(T, f, x))
+    return extract_gradient!(T, result, f(dualize(T, x)))
 end
 
 # Jacobian
@@ -84,7 +82,7 @@ end
 
 @inline function ForwardDiff.vector_mode_jacobian(f::F, x::StaticArray) where {F}
     T = typeof(Tag(f, eltype(x)))
-    return extract_jacobian(T, static_dual_eval(T, f, x), x)
+    return extract_jacobian(T, f(dualize(T, x)), x)
 end
 
 function extract_jacobian(::Type{T}, ydual::AbstractArray, x::StaticArray) where T
@@ -94,7 +92,7 @@ end
 
 @inline function ForwardDiff.vector_mode_jacobian!(result, f::F, x::StaticArray) where {F}
     T = typeof(Tag(f, eltype(x)))
-    ydual = static_dual_eval(T, f, x)
+    ydual = f(dualize(T, x))
     result = extract_jacobian!(T, result, ydual, length(x))
     result = extract_value!(T, result, ydual)
     return result
@@ -102,7 +100,7 @@ end
 
 @inline function ForwardDiff.vector_mode_jacobian!(result::ImmutableDiffResult, f::F, x::StaticArray) where {F}
     T = typeof(Tag(f, eltype(x)))
-    ydual = static_dual_eval(T, f, x)
+    ydual = f(dualize(T, x))
     result = DiffResults.jacobian!(result, extract_jacobian(T, ydual, x))
     result = DiffResults.value!(Base.Fix1(value, T), result, ydual)
     return result
diff --git a/test/AllocationsTest.jl b/test/AllocationsTest.jl
index 36df8eba..af8d6e77 100644
--- a/test/AllocationsTest.jl
+++ b/test/AllocationsTest.jl
@@ -1,6 +1,7 @@
 module AllocationsTest
 
 using ForwardDiff
+using StaticArrays
 
 include(joinpath(dirname(@__FILE__), "utils.jl"))
 
@@ -44,4 +45,25 @@ end
     @test iszero(allocs_jacobian!())
 end
 
+@testset "allocation-free nested StaticArray jacobian" begin
+    # test that nested jacobians of StaticArrays do not allocate. 
+    # This is a regression test for issue #798, where the inner jacobian was allocating
+    toy_f(x) = SVector(x[1]^2 * x[2], sin(x[1]) + x[2]^3)
+
+    function toy_J_flat(x::SVector{2,T}) where {T}
+        y = ForwardDiff.jacobian(toy_f, x)
+        return SVector{2,T}(y[1], y[2])
+    end
+
+    function toy_nested_jacobian(x0::SVector{2,T}) where {T}
+        return ForwardDiff.jacobian(toy_J_flat, x0)
+    end
+
+    function allocs_jacobian()
+        x0 = SVector(1.0, 2.0)
+        return @allocated toy_nested_jacobian(x0)
+    end
+    @test iszero(allocs_jacobian())
+end
+
 end