OpenAI ChatGPT 5 vs Grok 4 - Which one creates better Python code?

When it comes to AI-assisted coding, OpenAI ChatGPT 5 and Grok 4 are two of the hottest tools of 2025. Both claim to write Python code with high accuracy and efficiency, but which one actually delivers cleaner, more maintainable scripts in real-world scenarios?

In this article, we'll pit ChatGPT 5 and Grok 4 against each other in multiple Python challenges. We will review code correctness, readability, execution speed, and adaptability when given tricky prompts.

Why Compare ChatGPT 5 and Grok 4 for Python?

Python remains the go-to language for AI, automation, and data science. Many developers now rely on AI to accelerate their coding workflow, debug faster, and even generate full applications from scratch.

While ChatGPT has long dominated this space, Grok has been gaining attention for its code clarity and concise outputs. Both use different training philosophies, ChatGPT's emphasis on reasoning vs. Grok's focus on brevity.

Testing OpenAI ChatGPT 5 and Grok 4

We will compare the two AIs in 10 Python coding test cases:

1. Basic Script (Even number sum)
2. Data Processing (CSV totals)
3. Algorithmic Problem (First non-repeating char)
4. API Integration (Bitcoin price fetch)
5. Web Scraping (Hacker News headlines)
6. File Compression (Zip .txt files)
7. Image Processing (Resize JPEGs)
8. Multithreading (Concurrent downloads)
9. Database Interaction (SQLite CRUD)
10. Unit Testing (Prime number test)

All prompts will be identical and submitted to ChatGPT 5 and Grok 4. You can run the tests yourself to validate results.

Python Challenge 1 - Basic Script

Challenge 1 Prompt:

Write a Python script that reads a list of integers and outputs the sum of even numbers.

Challenge 1 GPT 5 Code:

# Script to read a list of integers and output the sum of even numbers
def sum_of_even_numbers(numbers):
    return sum(num for num in numbers if num % 2 == 0)

if __name__ == "__main__":
    # Read input from the user
    try:
        numbers = list(map(int, input("Enter integers separated by spaces: ").split()))
        total_even = sum_of_even_numbers(numbers)
        print("Sum of even numbers:", total_even)
    except ValueError:
        print("Please enter valid integers only.")

Challenge 1 Grok 4 Code:

# Read a line of input containing space-separated integers
input_line = input("Enter a list of integers separated by spaces: ")

# Convert the input string into a list of integers
numbers = list(map(int, input_line.split()))

# Initialize sum of even numbers
even_sum = 0

# Iterate through the list and add even numbers to the sum
for num in numbers:
    if num % 2 == 0:
        even_sum += num

# Output the sum
print("Sum of even numbers:", even_sum)

Challenge 1 Observations:

• GPT 5 Speed: Very fast response, near instant.
• GPT 5 Code: Provided shorter and elegant code with entrypoint, with comments.
• Grok 4 Speed: Slower response, "thought" a bit longer (~2 seconds).
• Grok 4 Code: Provided a bit longer code and without entrypoint, but with comments.

Python Challenge 2 - Data Processing

Challenge 2 Prompt:

Load a CSV file of transactions and print the total sales per product.

Challenge 2 GPT 5 Code:

import csv
from collections import defaultdict

def total_sales_per_product(csv_file):
    sales = defaultdict(float)

    with open(csv_file, newline='', encoding='utf-8') as f:
        reader = csv.DictReader(f)
        for row in reader:
            try:
                product = row['product']
                quantity = float(row['quantity'])
                price = float(row['price'])
                sales[product] += quantity * price
            except (KeyError, ValueError):
                print(f"Skipping invalid row: {row}")

    return sales

if __name__ == "__main__":
    file_path = "transactions.csv"  # Change to your file path
    sales_totals = total_sales_per_product(file_path)

    print("Total Sales Per Product:")
    for product, total in sales_totals.items():
        print(f"{product}: ${total:,.2f}")

Challenge 2 Grok 4 Code:

import pandas as pd

# Prompt user for the CSV file path
file_path = input("Enter the path to the CSV file: ")

# Load the CSV file into a DataFrame
df = pd.read_csv(file_path)

# Assuming the CSV has columns 'Product' and 'Sales'
# Group by 'Product' and sum the 'Sales'
total_sales = df.groupby('Product')['Sales'].sum()

# Print the total sales per product
print("Total sales per product:")
print(total_sales)

Challenge 2 Observations:

• GPT 5 Speed: Very fast response, near instant.
• GPT 5 Code: Provided a bit longer and elegant code with entrypoint, but without comments.
• Grok 4 Speed: Slower response, "thought" a bit longer (~13 seconds).
• Grok 4 Code: Provided simpler code and without entrypoint, but with comments.

Python Challenge 3 - Algorithmic Problem

Challenge 3 Prompt:

Given a string, return the first non-repeating character.

Challenge 3 GPT 5 Code:

def first_non_repeating_char(s):
    from collections import Counter
    counts = Counter(s)

    for char in s:
        if counts[char] == 1:
            return char
    return None  # If no non-repeating character found

# Example usage
text = "swiss"
result = first_non_repeating_char(text)
if Code:
    print(f"The first non-repeating character is: '{result}'")
else:
    print("No non-repeating character found.")

Challenge 3 Grok 4 Code:

import collections

def first_non_repeating_char(s):
    """
    Returns the first non-repeating character in the string.
    If no such character exists, returns None.
    """
    count = collections.Counter(s)
    for char in s:
        if count[char] == 1:
            return char
    return None

# Example usage
input_string = input("Enter a string: ")
result = first_non_repeating_char(input_string)
print("First non-repeating character:", result)

Challenge 3 Observations:

• GPT 5 Speed: Very fast response, near instant.
• GPT 5 Code: Provided short code without an entrypoint and without comments. Example usage has a bug.
• Grok 4 Speed: Slower response, "thought" a bit longer (~8 seconds).
• Grok 4 Code: Provided short code without an entrypoint, with a brief docstring.

Python Challenge 4 - API Integration

Challenge 4 Prompt:

Use the requests library to fetch the current Bitcoin price in USD from a public API and print it.

Challenge 4 GPT 5 Code:

import requests

def get_bitcoin_price_usd():
    url = "https://api.coindesk.com/v1/bpi/currentprice/USD.json"
    try:
        response = requests.get(url, timeout=5)
        response.raise_for_status()  # Raise HTTPError for bad responses
        data = response.json()
        price = data["bpi"]["USD"]["rate"]
        print(f"Current Bitcoin Price: ${price} USD")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching Bitcoin price: {e}")
    except (KeyError, ValueError) as e:
        print(f"Error parsing response: {e}")

if __name__ == "__main__":
    get_bitcoin_price_usd()

Challenge 4 Grok 4 Code:

import requests

# Define the API endpoint
url = "https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd"

# Fetch the data
response = requests.get(url)

# Check if the request was successful
if response.status_code == 200:
    data = response.json()
    bitcoin_price = data['bitcoin']['usd']
    print(f"Current Bitcoin price in USD: ${bitcoin_price}")
else:
    print(f"Error fetching data: {response.status_code}")

Challenge 4 Observations:

• GPT 5 Speed: Very fast response, near instant.
• GPT 5 Code: Provided short and elegant code with entrypoint, but without comments.
• Grok 4 Speed: Slower response, "thought" a bit longer (~23 seconds).
• Grok 4 Code: Provided a bit shorter code and without entrypoint but with comments.

Python Challenge 5 - Web Scraping

Challenge 5 Prompt:

Use requests and BeautifulSoup to scrape the top 5 news headlines from https://news.ycombinator.com/ and print them.

Challenge 5 GPT 5 Code:

import requests
from bs4 import BeautifulSoup

def get_top_hn_headlines(limit=5):
    url = "https://news.ycombinator.com/"
    try:
        response = requests.get(url, timeout=5)
        response.raise_for_status()

        soup = BeautifulSoup(response.text, "html.parser")
        headlines = [a.get_text() for a in soup.select(".titleline > a")]

        print(f"Top {limit} Hacker News Headlines:")
        for i, title in enumerate(headlines[:limit], 1):
            print(f"{i}. {title}")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching Hacker News: {e}")

if __name__ == "__main__":
    get_top_hn_headlines()

Challenge 5 Grok 4 Code:

import requests
from bs4 import BeautifulSoup

# Define the URL
url = 'https://news.ycombinator.com/'

# Fetch the webpage
response = requests.get(url)
response.raise_for_status()  # Raise an error for bad status codes

# Parse the HTML content
soup = BeautifulSoup(response.text, 'html.parser')

# Find the top 5 story elements
stories = soup.find_all('tr', class_='athing')[:5]

# Print the top 5 headlines
print("Top 5 News Headlines from Hacker News:")
for i, story in enumerate(stories, 1):
    title_span = story.find('span', class_='titleline')
    if title_span:
        headline = title_span.find('a').text
        print(f"{i}. {headline}")

Challenge 5 Observations:

• GPT 5 Speed: Very fast response, near instant.
• GPT 5 Code: Provided short and elegant code with entrypoint, but without comments.
• Grok 4 Speed: Slower response, "thought" a bit longer (~13 seconds).
• Grok 4 Code: Provided a bit longer code and without entrypoint but with comments.

Python Challenge 6 - File Compression

Challenge 6 Prompt:

Create a Python script that zips all .txt files in the current directory into 'archive.zip'.

Challenge 6 GPT 5 Code:

import zipfile
import glob
import os

def zip_text_files(output_zip="archive.zip"):
    txt_files = glob.glob("*.txt")
    if not txt_files:
        print("No .txt files found in the current directory.")
        return

    with zipfile.ZipFile(output_zip, "w") as zipf:
        for file in txt_files:
            zipf.write(file, os.path.basename(file))
            print(f"Added: {file}")

    print(f"Created {output_zip} containing {len(txt_files)} files.")

if __name__ == "__main__":
    zip_text_files()

Challenge 6 Grok 4 Code:

import os
from zipfile import ZipFile

# Get the current directory
current_dir = os.getcwd()

# List all .txt files in the current directory
txt_files = [f for f in os.listdir(current_dir) if f.endswith('.txt')]

# Create a zip file and add the .txt files
with ZipFile('archive.zip', 'w') as zipf:
    for file in txt_files:
        zipf.write(file)

print(f"Successfully zipped {len(txt_files)} .txt files into 'archive.zip'.")

Challenge 6 Observations:

• GPT 5 Speed: Very fast response, near instant.
• GPT 5 Code: Provided short and elegant code with entrypoint, but without comments.
• Grok 4 Speed: Slower response, "thought" a bit longer (~10 seconds).
• Grok 4 Code: Provided short code and without entrypoint but with comments.

Python Challenge 7 - Image Processing

Challenge 7 Prompt:

Use Pillow (PIL) to resize all JPEG images in the current directory to 800x600 pixels and save them to an 'output' folder.

Challenge 7 GPT 5 Code:

import os
from PIL import Image
import glob

def resize_jpeg_images(width=800, height=600, output_dir="output"):
    # Create output folder if it doesn't exist
    os.makedirs(output_dir, exist_ok=True)

    # Find all .jpg and .jpeg files (case-insensitive)
    jpeg_files = glob.glob("*.jpg") + glob.glob("*.jpeg") + glob.glob("*.JPG") + glob.glob("*.JPEG")

    if not jpeg_files:
        print("No JPEG images found in the current directory.")
        return

    for file in jpeg_files:
        try:
            with Image.open(file) as img:
                img_resized = img.resize((width, height), Image.LANCZOS)
                output_path = os.path.join(output_dir, os.path.basename(file))
                img_resized.save(output_path, "JPEG")
                print(f"Resized and saved: {output_path}")
        except Exception as e:
            print(f"Error processing {file}: {e}")

if __name__ == "__main__":
    resize_jpeg_images()

Challenge 7 Grok 4 Code:

import os
from PIL import Image

# Get the current directory
current_dir = os.getcwd()

# Create the output folder if it doesn't exist
output_folder = os.path.join(current_dir, 'output')
os.makedirs(output_folder, exist_ok=True)

# List all JPEG files in the current directory (case-insensitive)
jpeg_files = [f for f in os.listdir(current_dir) 
              if f.lower().endswith(('.jpg', '.jpeg'))]

# Resize each image and save to output folder
resized_count = 0
for file in jpeg_files:
    try:
        img_path = os.path.join(current_dir, file)
        img = Image.open(img_path)
        resized_img = img.resize((800, 600))
        output_path = os.path.join(output_folder, file)
        resized_img.save(output_path)
        resized_count += 1
    except Exception as e:
        print(f"Error processing {file}: {e}")

print(f"Successfully resized and saved {resized_count} JPEG images to the 'output' folder.")

Challenge 7 Observations:

• GPT 5 Speed: Very fast response, near instant.
• GPT 5 Code: Provided short and elegant code with entrypoint, with comments.
• Grok 4 Speed: Slower response, "thought" a bit longer (~10 seconds).
• Grok 4 Code: Provided a bit longer code and without entrypoint, with comments.

Python Challenge 8 - Multithreading

Challenge 8 Prompt:

Create a Python script that downloads 5 different files from given URLs concurrently using threading.

Challenge 8 GPT 5 Code:

import os
from pathlib import Path
from urllib.parse import urlparse
from concurrent.futures import ThreadPoolExecutor, as_completed

import requests

CHUNK_SIZE = 1024 * 64  # 64KB


def unique_path(dest_dir: Path, filename: str) -> Path:
    """Return a unique path inside dest_dir by appending (1), (2), ... if needed."""
    dest = dest_dir / filename
    if not dest.exists():
        return dest
    stem, suffix = os.path.splitext(filename)
    i = 1
    while True:
        candidate = dest_dir / f"{stem} ({i}){suffix}"
        if not candidate.exists():
            return candidate
        i += 1


def filename_from_url(url: str, index: int) -> str:
    """Get a safe filename from URL; fall back to file_{index} if missing."""
    path = urlparse(url).path
    name = os.path.basename(path).strip()
    return name or f"file_{index}"


def download_file(url: str, dest_dir: Path, index: int, timeout: int = 15) -> Path:
    """Download a single file to dest_dir. Returns the saved path."""
    dest_dir.mkdir(parents=True, exist_ok=True)
    base_name = filename_from_url(url, index)
    dest_path = unique_path(dest_dir, base_name)

    headers = {"User-Agent": "python-threaded-downloader/1.0"}
    with requests.get(url, stream=True, timeout=timeout, headers=headers) as r:
        r.raise_for_status()
        with open(dest_path, "wb") as f:
            for chunk in r.iter_content(chunk_size=CHUNK_SIZE):
                if chunk:  # filter out keep-alive chunks
                    f.write(chunk)
    return dest_path


def download_concurrently(urls, out_dir="downloads", max_workers=5):
    """Download given URLs concurrently using threads."""
    dest_dir = Path(out_dir)
    results = []
    errors = []

    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        future_to_url = {
            executor.submit(download_file, url, dest_dir, i): url
            for i, url in enumerate(urls, start=1)
        }
        for future in as_completed(future_to_url):
            url = future_to_url[future]
            try:
                saved_path = future.result()
                print(f"✅ Downloaded: {url} -> {saved_path}")
                results.append(saved_path)
            except Exception as e:
                print(f"❌ Failed: {url} ({e})")
                errors.append((url, e))
    print(f"\nDone. {len(results)} succeeded, {len(errors)} failed.")
    return results, errors


if __name__ == "__main__":
    # Replace these with your 5 URLs
    urls = [
        "https://example.com/file1.jpg",
        "https://example.com/file2.pdf",
        "https://example.com/file3.zip",
        "https://example.com/file4.png",
        "https://example.com/file5.mp4",
    ]
    download_concurrently(urls, out_dir="downloads", max_workers=5)

Challenge 8 Grok 4 Code:

import threading
import urllib.request

def download_file(url, filename):
    """
    Downloads a file from the given URL and saves it with the specified filename.
    """
    try:
        urllib.request.urlretrieve(url, filename)
        print(f"Downloaded {filename} from {url}")
    except Exception as e:
        print(f"Error downloading {url}: {e}")

# List of 5 example URLs (replace with your actual URLs)
urls = [
    "https://www.example.com/file1.txt",
    "https://www.example.com/file2.pdf",
    "https://www.example.com/file3.jpg",
    "https://www.example.com/file4.zip",
    "https://www.example.com/file5.docx"
]

# Corresponding filenames to save the downloads
filenames = [
    "file1.txt",
    "file2.pdf",
    "file3.jpg",
    "file4.zip",
    "file5.docx"
]

# Create and start threads for concurrent downloads
threads = []
for url, filename in zip(urls, filenames):
    thread = threading.Thread(target=download_file, args=(url, filename))
    thread.start()
    threads.append(thread)

# Wait for all threads to complete
for thread in threads:
    thread.join()

print("All downloads completed.")

Challenge 8 Observations:

• GPT 5 Speed: Slower response, "thought" a bit longer (~10 seconds).
• GPT 5 Code: Provided long and elegant code with entrypoint, with comments.
• Grok 4 Speed: Even slower response, "thought" a bit longer (~21 seconds).
• Grok 4 Code: Provided shorter code and without entrypoint, with comments.

Python Challenge 9 - Database Interaction

Challenge 9 Prompt:

Connect to an SQLite database, create a 'users' table, insert sample data, and query all users.

Challenge 9 GPT 5 Code:

import sqlite3

def main():
    # Connect to (or create) the database
    conn = sqlite3.connect("example.db")
    cursor = conn.cursor()

    # Create the users table if it doesn't exist
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS users (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            name TEXT NOT NULL,
            email TEXT UNIQUE NOT NULL
        )
    """)

    # Insert sample data
    sample_users = [
        ("Alice", "[e-mail]"),
        ("Bob", "[e-mail]"),
        ("Charlie", "[e-mail]"),
    ]

    try:
        cursor.executemany("INSERT INTO users (name, email) VALUES (?, ?)", sample_users)
    except sqlite3.IntegrityError:
        # Ignore duplicates if run multiple times
        pass

    # Commit changes
    conn.commit()

    # Query all users
    cursor.execute("SELECT id, name, email FROM users")
    users = cursor.fetchall()

    print("Users in database:")
    for user in users:
        print(user)

    # Close the connection
    conn.close()

if __name__ == "__main__":
    main()

Challenge 9 Grok 4 Code:

import sqlite3

# Connect to an in-memory SQLite database
conn = sqlite3.connect(':memory:')
cursor = conn.cursor()

# Create the 'users' table
cursor.execute('''
    CREATE TABLE users (
        id INTEGER PRIMARY KEY,
        name TEXT,
        age INTEGER
    )
''')

# Insert sample data
cursor.execute("INSERT INTO users (name, age) VALUES ('Alice', 30)")
cursor.execute("INSERT INTO users (name, age) VALUES ('Bob', 25)")
cursor.execute("INSERT INTO users (name, age) VALUES ('Charlie', 35)")

# Commit the changes
conn.commit()

# Query all users
cursor.execute("SELECT * FROM users")
results = cursor.fetchall()

# Print the results
print("ID | Name | Age")
print("----------------")
for row in results:
    print(f"{row[0]} | {row[1]} | {row[2]}")

# Close the connection
conn.close()

Challenge 9 Observations:

• GPT 5 Speed: Very fast response, near instant. Understood a script is needed, even though we just bluntly said "connect..." instead of "write or create".
• GPT 5 Code: Provided long and elegant code with entrypoint, with comments.
• Grok 4 Speed: Slower response, "thought" a bit longer (~16 seconds).
• Grok 4 Code: Provided a bit shorter code and, for the first time, with an entrypoint and comments. Very interestingly, several function/test names mirror ChatGPT 5’s style.

Python Challenge 10 - Unit Testing

Challenge 10 Prompt:

Write a Python unittest that verifies a function 'is_prime(n)' correctly identifies prime numbers.

Challenge 10 GPT 5 Code:

import unittest

# Example implementation of is_prime for testing purposes
# If you already have this function, you can remove this definition.
def is_prime(n):
    """Return True if n is a prime number, else False."""
    if n <= 1:
        return False
    if n <= 3:
        return True
    if n % 2 == 0 or n % 3 == 0:
        return False
    i = 5
    while i * i <= n:
        if n % i == 0 or n % (i + 2) == 0:
            return False
        i += 6
    return True


class TestIsPrime(unittest.TestCase):
    def test_primes(self):
        primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29]
        for p in primes:
            with self.subTest(p=p):
                self.assertTrue(is_prime(p), f"{p} should be prime")

    def test_non_primes(self):
        non_primes = [0, 1, 4, 6, 8, 9, 10, 12, 15, 20, 25]
        for n in non_primes:
            with self.subTest(n=n):
                self.assertFalse(is_prime(n), f"{n} should not be prime")

if __name__ == "__main__":
    unittest.main()

Challenge 10 Grok 4 Code:

import unittest

# Assuming the is_prime function is defined in a module named prime_checker
# Replace 'prime_checker' with the actual module name where is_prime is defined
from prime_checker import is_prime

class TestIsPrime(unittest.TestCase):
    def test_prime_numbers(self):
        """Test that prime numbers are correctly identified."""
        primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29]
        for num in primes:
            with self.subTest(num=num):
                self.assertTrue(is_prime(num), f"{num} should be prime")

    def test_non_prime_numbers(self):
        """Test that non-prime numbers are correctly identified."""
        non_primes = [0, 1, 4, 6, 8, 9, 10, 12, 14, 15, 16, 18, 20, 21, 22, 24, 25, 26, 27, 28]
        for num in non_primes:
            with self.subTest(num=num):
                self.assertFalse(is_prime(num), f"{num} should not be prime")

    def test_negative_numbers(self):
        """Test that negative numbers are not considered prime."""
        negatives = [-1, -2, -3, -5, -7]
        for num in negatives:
            with self.subTest(num=num):
                self.assertFalse(is_prime(num), f"{num} should not be prime")

    def test_one_and_zero(self):
        """Explicitly test 0 and 1, which are not prime."""
        self.assertFalse(is_prime(0))
        self.assertFalse(is_prime(1))

if __name__ == '__main__':
    unittest.main()

Challenge 10 Observations:

• GPT 5 Speed: very fast response, near instant.
• GPT 5 Code: Provided long and elegant code with entrypoint, with comments.
• Grok 4 Speed: Slower response, "thought" a bit longer (~12 seconds).
• Grok 4 Code: Provided a bit shorter code and for the first time with entrypoint and comments. Very interestingly the function names are the as ChatGPT 5.

Criteria for Evaluation

We will rate each AI on:

• Code Correctness: Does it work without major edits?
• Readability: Is the code clean and well-commented?
• Efficiency: Does it use optimal methods?
• Error Handling: Does it anticipate possible failures?
• Explainability: Does it provide clear reasoning?

Preliminary Observations

From previous and current experience:

• ChatGPT 5 tends to give more verbose, well-documented code, much faster.
• Grok 4 prefers minimalism and slower responses and sometimes omits comments.

Both excel at standard tasks, but Grok may struggle with multi-step reasoning prompts.

What stood out across the 10 challenges

• Prompt adherence: GPT-5 stayed on-task (e.g., BTC API). Grok 4 occasionally drifted (Challenge 4).
• Entrypoints & structure: GPT-5 consistently used entrypoints and helpers; Grok 4 often wrote single-file scripts without an entrypoint.
• Error handling: GPT-5 added timeouts/raise_for_status/try-except more often; Grok 4 tended to be minimal.
• Dependencies & assumptions: GPT-5 used stdlib where possible; Grok 4 leaned on pandas or simpler urllib defaults.
• Data model assumptions: GPT-5 inferred fields and computed values (qty × price); Grok 4 assumed pre-aggregated columns.
• Algorithmic care: Both solved the logic tasks; GPT-5's example had a minor variable bug, while Grok 4's HN example had syntax typos.
• Performance posture: GPT-5 used streaming + thread pools for downloads; Grok 4 used raw threads + urlretrieve (simpler, less robust).

Verdict

While both tools can write functional Python code, the choice may come down to developer preference:

• Choose ChatGPT 5 if you value detailed explanations, step-by-step reasoning, fast code generation and extensive comments.
• Choose Grok 4 if you prefer concise, simple code with minimal fluff and slower code generation.

I honestly prefer ChatGPT 5 because it responds much faster with better and detailed Python code. Sorry Elon Musk.

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Frequently Asked Questions (FAQ)

Is OpenAI ChatGPT 5 or Grok 4 better for beginners?
ChatGPT 5. It explains more, includes safer defaults (timeouts, error handling), and uses cleaner structure.

Which produced fewer code issues in these tests?
ChatGPT 5 overall. Grok 4 had occasional prompt drift and minor syntax errors in scraping.

Which is faster?
In your runs, ChatGPT 5 responded faster on average. Your timings are included per challenge.

Do I need to review the code they generate?
Yes. Both models can make small mistakes; always run tests and add guardrails for I/O and network code.

Which handled files, images, and networking more robustly?
ChatGPT 5. It tended to add entrypoints, timeouts, streaming, and better image resampling.

Does Grok 4 have advantages?
Yes, snappier, concise scripts when you already know the context and want minimal output.

What prompt style worked best?
Be explicit about inputs/outputs, libraries, and edge cases (example: 'use requests with timeout, print JSON parse errors').

Can I rely on either model for production code?
Use them as accelerators, not replacements: keep tests, linting, and security reviews in your pipeline.