Eigen + MKL or OpenBLAS slower than Numpy/Scipy + OpenBLAS

I'm starting with c++ atm and want to work with matrices and speed up things in general. Worked with Python+Numpy+OpenBLAS before. Thought c++ + Eigen + MKL might be faster or at least not slower.

My c++ code:

#define EIGEN_USE_MKL_ALL
#include <iostream>
#include <Eigen/Dense>
#include <Eigen/LU>
#include <chrono>

using namespace std;
using namespace Eigen;

int main()
{
    int n = Eigen::nbThreads( );
    cout << "#Threads: " << n << endl;

    uint16_t size = 4000;
    MatrixXd a = MatrixXd::Random(size,size);

    clock_t start = clock ();
    PartialPivLU<MatrixXd> lu = PartialPivLU<MatrixXd>(a);

    float timeElapsed = double( clock() - start ) / CLOCKS_PER_SEC; 
    cout << "Elasped time is " << timeElapsed << " seconds." << endl ;
}

My Python code:

import numpy as np
from time import time
from scipy import linalg as la

size = 4000

A = np.random.random((size, size))

t = time()
LU, piv = la.lu_factor(A)
print(time()-t)

My timings:

C++     2.4s
Python  1.2s

Why is c++ slower than Python?

I am compiling c++ using:

g++ main.cpp -o main -lopenblas -O3 -fopenmp  -DMKL_LP64 -I/usr/local/include/mkl/include

MKL is definiely working: If I disable it the running time is around 13s.

I also tried C++ + OpenBLAS which gives me around 2.4s as well.

Any ideas why C++ and Eigen are slower than numpy/scipy?