Why is adding two std::vectors slower than raw arrays from new[]?

I'm looking around OpenMP, partially because my program need to make additions of very large vectors (millions of elements). However i see a quite large difference if i use std::vector or raw array. Which i cannot explain. I insist that the difference is only on the loop, not the initialisation of course.

The difference in time I refer to, is only timing the addition, especially not to take into account any initialization difference between vectors, arrays, etc. I'm really talking only about the sum part. The size of the vectors is not known at compile time. I use g++ 5.x on Ubuntu 16.04.

edit: I tested what @Shadow said, it got me thinking, is there something going on with optimization? If i compile with -O2, then, using raw arrays initialized, I get back for loop scaling with number of threads. But with -O3 or -funroll-loops, it is as if the compiler kicks in early and optimize before the pragma is seen.

I came up with the following, simple test:

#define SIZE 10000000
#define TRIES 200
int main(){

    std::vector<double> a,b,c;
    a.resize(SIZE);
    b.resize(SIZE);
    c.resize(SIZE);

    double start = omp_get_wtime();
    unsigned long int i,t;
    #pragma omp parallel shared(a,b,c) private(i,t)
    {
    for( t = 0; t< TRIES; t++){
       #pragma omp for
       for( i = 0; i< SIZE; i++){
        c[i] = a[i] + b[i];
       }
    }
    }

    std::cout << "finished in " << omp_get_wtime() - start << std::endl;

    return 0;

}

I compile with

   g++ -O3 -fopenmp  -std=c++11 main.cpp

And get for one threads

>time ./a.out 
 finished in 2.5638
 ./a.out  2.58s user 0.04s system 99% cpu 2.619 total.

For two threads, loop takes 1.2s, for 1.23 total.

Now if I use raw arrays:

 int main(){
    double *a, *b, *c;
    a = new double[SIZE];
    b = new double[SIZE];
    c = new double[SIZE];
    double start = omp_get_wtime();
    unsigned long int i,t;
    #pragma omp parallel shared(a,b,c) private(i,t)
    {
       for( t = 0; t< TRIES; t++)
       {
          #pragma omp for
          for( i = 0; i< SIZE; i++)
          {
             c[i] = a[i] + b[i];
          }
       }
    }

    std::cout << "finished in " << omp_get_wtime() - start << std::endl;

    delete[] a;
    delete[] b;
    delete[] c;

    return 0;
}

And i get (1 thread):

>time ./a.out
 finished in 1.92901 
  ./a.out  1.92s user 0.01s system 99% cpu 1.939 total   

std::vector is 33% slower!

For two threads:

>time ./a.out 
finished in 1.20061                                                              
./a.out  2.39s user 0.02s system 198% cpu 1.208 total   

As a comparison, with Eigen or Armadillo for exactly the same operation (using c = a+b overload with vector object), I get for total real time ~2.8s. They are not multi-threaded for vector additions.

Now, i thought the std::vector has almost no overhead? What is happening here? I'd like to use nice standard library objects.

I cannot find any reference anywhere on a simple example like this.