Significant price difference while calculating call option price using Monte Carlo approach in C++ and Python?

I am trying to calculate the price of a european call option using the Monte Carlo approach. I coded the algorithm in C++ and Python. As far as I know the implementation is correct and as N (the number of trials) gets bigger, the price should converge to a similar value in both the programs.

My problem is that as N gets bigger, say just from 1000 to 10000 trials the prices converge to two different values. In C++ the price converges towards the value of 3.30 while with Python it converges towards 3.70.

I think that gap of 0.40 is too wide, I should get more similar resutls. Why is this gap so big? What did I do wrong? I cannot seem to find my mistake.

Here is the code I used:

Python

import numpy as np
import matplotlib.pyplot as plt


def stoc_walk(p,dr,vol,periods):
    w = np.random.normal(0,1,size=periods)
    for i in range(periods):
        p += dr*p + w[i]*vol*p
    return p

s0 = 10;
drift = 0.001502
volatility = 0.026
r = 0.02
days = 255
N = 10000
zero_trials = 0

k=12
payoffs = []

for i in range(N):
    temp = stoc_walk(s0,drift,volatility,days)
    if temp > k:
        payoff = temp-k
        payoffs.append(payoff*np.exp(-r))
    else:
        payoffs.append(0)
        zero_trials += 1

payoffs = np.array(payoffs)
avg = payoffs.mean()

print("MONTE CARLO PLAIN VANILLA CALL OPTION PRICING")
print("Option price: ",avg)
print("Initial price: ",s0)
print("Strike price: ",k)
print("Daily expected drift: ",drift)
print("Daily expected volatility: ",volatility)
print("Total trials: ",N)
print("Zero trials: ",zero_trials)
print("Percentage of total trials: ",zero_trials/N)

C++

//Call option Monte Carlo evaluation;

#include <iostream>
#include <random>
#include <math.h>
#include <chrono>

using namespace std;

/*  double stoc_walk: returns simulated price after periods

    p = price at t=t0
    dr = drift
    vol = volatility
    periods (days)
*/
double stoc_walk(double p,double dr,double vol,int periods)
{
    double mean = 0.0;
    double stdv = 1.0;

    /* initialize random seed: */
    int seed = rand() %1000 + 1;
    //unsigned seed = std::chrono::system_clock::now().time_since_epoch().count();
    std::default_random_engine generator(seed);
    std::normal_distribution<double> distribution(mean,stdv);

    for(int i=0; i < periods; i++)
    {
        double w = distribution(generator);
        p += dr*p + w*vol*p;
    }
    return p;
}

int main()
{
    //Initialize variables
    double s0 = 10;             //Initial price
    double drift = 0.001502;    //daily drift
    double volatility = 0.026;  //volatility (daily)
    double r = 0.02;            //Risk free yearly rate
    int days = 255;             //Days
    int N = 10000;              //Number of Monte Carlo trials
    double zero_trials = 0;

    double k = 12;               //Strike price
    int temp = 0;                //Temporary variable
    double payoffs[N];           //Payoff vector
    double payoff = 0;

    srand (time(NULL));         //Initialize random number generator

    //Calculate N payoffs
    for(int j=0; j < N; j++)
    {
        temp = stoc_walk(s0,drift,volatility,days);
        if(temp > k)
        {
            payoff = temp - k;
            payoffs[j] = payoff * exp(-r);
        }
        else
        {
            payoffs[j] = 0;
            zero_trials += 1;
        }
    }

    //Average the results
    double sum_ = 0;
    double avg_ = 0;
    for(int i=0; i<N; i++)
    {
        sum_ += payoffs[i];
    }
    avg_ = sum_/N;

    //Print results
    cout << "MONTE CARLO PLAIN VANILLA CALL OPTION PRICING" << endl;
    cout << "Option price: " << avg_ << endl;
    cout << "Initial price: " << s0 << endl;
    cout << "Strike price: " << k << endl;
    cout << "Daily expected drift: " << drift*100 << "%" << endl;
    cout << "Daily volatility: " << volatility*100 << "%" << endl;
    cout << "Total trials: " << N << endl;
    cout << "Zero trials: " << zero_trials << endl;
    cout << "Percentage of total trials: " << zero_trials/N*100 << "%";

    return 0;
}