ChatGPT vs. DeepSeek: Which AI Reigns Supreme in Solving Coding Challenges?

Artificial intelligence (AI) has become a game-changer in programming, helping developers write, debug, and optimize code at unprecedented speeds. But not all AI models are created equal. Two major Large Language Models (LLMs) have emerged as contenders in AI-assisted coding: ChatGPT-03-mini, developed by OpenAI, and DeepSeek-R1, an up-and-coming model from China designed for logical reasoning.

A recent study set out to compare these two powerhouses in their ability to tackle competitive programming problems from Codeforces, a popular platform for algorithmic challenges. The results? While both models performed well on easy tasks, ChatGPT outshined DeepSeek on medium-level problems, and both struggled with hard challenges.

So, what does this mean for developers who rely on AI for coding? Let’s dive into the details and uncover the strengths and weaknesses of each model in the world of AI-assisted programming.

Setting the Stage: Why AI in Coding Matters

Imagine you’re a programmer working on a tight deadline. You encounter a tricky bug or need to optimize an algorithm. Instead of spending hours searching Stack Overflow, you turn to an AI assistant that suggests code, explains logic, and even improves performance.

LLMs like ChatGPT and DeepSeek promise exactly that. They convert natural language prompts into executable code, streamlining everything from debugging to software development. But which AI should you trust more?

This study attempts to answer that question by pitting the two models against each other in 29 competitive programming tasks divided into three levels of difficulty:

1. Easy – Straightforward problems with high success rates.
2. Medium – More complex coding tasks requiring deeper logic.
3. Hard – Advanced algorithmic problems that challenge even experienced coders.

Let’s break down the findings.

Showdown: ChatGPT vs. DeepSeek in Programming Tasks

Round 1: Easy Programming Challenges – A Tie Game

When it came to basic coding problems, both ChatGPT and DeepSeek delivered near-flawless results. Their solutions were accepted at a high rate, meaning they produced correct, compilable code.

This suggests that for everyday coding tasks like writing simple scripts, handling loops, or solving basic algorithm problems, both AIs are reliable choices.

✅ Verdict: Use either ChatGPT or DeepSeek for simple tasks – both models get the job done!

Round 2: Medium-Difficulty Challenges – ChatGPT Takes the Lead

Medium-level problems tested the models’ ability to handle more intricate logic and optimization. The results were striking:

• ChatGPT produced correct solutions 54.5% of the time.
• DeepSeek? A mere 18.1% success rate.

What went wrong? DeepSeek often stumbled on logic-heavy tasks, producing incorrect answers or inefficient solutions that exceeded memory limits. ChatGPT, while not perfect, demonstrated a stronger grasp of solving logical problems effectively.

✅ Verdict: If you’re tackling intermediate algorithmic challenges, ChatGPT is the better assistant.

Round 3: Hard Problems – A Struggle for Both

When it came to the hardest coding problems, both AI models hit a wall:

• ChatGPT only succeeded 11.1% of the time.
• DeepSeek had a 0% success rate.

The most common failures?
❌ ChatGPT often returned wrong answers or runtime errors.
❌ DeepSeek suffered from compilation errors and memory overuse.

This suggests that when challenges demand advanced reasoning, AI alone isn’t enough—human expertise is still essential for hard-level algorithmic problems.

✅ Verdict: AI-assisted coding has limits—developers still need to tweak and refine solutions for advanced problems.

Memory and Speed Analysis: Which AI is More Efficient?

Writing correct code is one thing, but what about how efficiently an AI model uses memory and processes tasks?

⏳ Execution Time: ChatGPT is Faster

• ChatGPT generated responses more quickly on most tasks.
• DeepSeek struggled with execution times, sometimes exceeding 4,000 milliseconds—far longer than ChatGPT.

🖥 Memory Usage: DeepSeek is More Conservative

• When DeepSeek generated correct answers, it used less memory than ChatGPT.
• ChatGPT, on the other hand, sometimes consumed excessive memory, likely due to its heavy parameter usage.

✅ Verdict: ChatGPT is faster, but DeepSeek is more memory-efficient—choose based on your priorities!

What This Means for Developers and AI Users

Based on these results, here’s how programmers can optimize their use of AI for coding tasks:

🔹 For simple coding solutions – Either model works fine.
🔹 For intermediate challenges – ChatGPT is the better choice.
🔹 For advanced problems – AI alone won’t cut it; human intervention and debugging are necessary.
🔹 If speed is your priority – ChatGPT responds faster.
🔹 If memory efficiency matters – DeepSeek may be a better option.

How to Get the Most Out of AI for Coding

💡 Use better prompts: Instead of simply asking “Solve this in C++,” try refining your prompts:
✔️ “Solve this using an optimized approach within a time complexity of O(n log n).”
✔️ “Explain your solution and suggest alternative approaches.”

💡 Use AI as a guide, not a coder:
– Always test and debug AI-generated code before using it in production.
– Use AI to understand complex algorithms, but tweak the code manually for efficiency.

💡 Experiment with different models: If DeepSeek continues improving, it may be a strong open-source alternative to ChatGPT down the line.

Key Takeaways

✅ ChatGPT outperformed DeepSeek on intermediate-level tasks (54.5% vs. 18.1% success rate).
✅ Both models excel at solving easy programming problems.
✅ Hard tasks still require human expertise—no AI is fully reliable for advanced algorithm challenges yet.
✅ ChatGPT is faster, while DeepSeek uses less memory.
✅ AI-generated code should always be tested and fine-tuned for accuracy and efficiency.

Final Verdict: Is One AI Better Than the Other?

Yes—ChatGPT currently has the edge in competitive programming. But DeepSeek, with its logical reasoning focus and open-source nature, shows promising potential for cost-effective AI solutions in the future.

For now, if you rely on AI for solving coding challenges, ChatGPT is the model to choose—but don’t ditch your debugging skills just yet! 🚀

What are your thoughts on AI-assisted coding? Have you tested ChatGPT or DeepSeek for programming tasks? Share your experience in the comments! 👇

If you are looking to improve your prompting skills and haven’t already, check out our free Advanced Prompt Engineering course.

This blog post is based on the research article “A Showdown of ChatGPT vs DeepSeek in Solving Programming Tasks” by Authors: Ronas Shakya, Farhad Vadiee, Mohammad Khalil. You can find the original article here.