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.
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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.
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Testing OpenAI ChatGPT 5 and Grok 4
We will compare the two AIs in 10 Python coding test cases:
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:
Challenge 1 Grok 4 Code:
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:
Challenge 2 Grok 4 Code:
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:
Challenge 3 Grok 4 Code:
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:
Challenge 4 Grok 4 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:
Challenge 5 Grok 4 Code:
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:
Challenge 6 Grok 4 Code:
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:
Challenge 7 Grok 4 Code:
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:
Challenge 8 Grok 4 Code:
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:
Challenge 9 Grok 4 Code:
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:
Challenge 10 Grok 4 Code:
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.
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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?
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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.
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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).
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Verdict
While both tools can write functional Python code, the choice may come down to developer preference:
- Choose ChatGPT 5 if you valuedetailed explanations ,step-by-step reasoning ,fast code generation andextensive comments .
- Choose Grok 4 if you preferconcise ,simple code with minimal fluff andslower 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.