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  • AI Faceoff: ChatGPT vs DeepSeek – Who’s Better at Coding Challenges?

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21 Mar

AI Faceoff: ChatGPT vs DeepSeek – Who’s Better at Coding Challenges?

  • By Stephen Smith
  • In Blog
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AI Faceoff: ChatGPT vs DeepSeek – Who’s Better at Coding Challenges?

In today’s tech-savvy world, the way we approach problem-solving in programming is undergoing a radical change, thanks to the rise of artificial intelligence (AI). With tools like ChatGPT and DeepSeek entering the scene, the competitive landscape has never been more electrifying. But what does this mean for developers, coders, or anyone interested in tech?

A recent study from the Centre for the Science of Learning & Technology at the University of Bergen puts these two heavyweights to the test in the realm of coding. They evaluated how well ChatGPT-03-mini and DeepSeek-R1 can tackle programming tasks, specifically focusing on their problem-solving skills. Let’s dive into the details!

The Challenge

Why We’re Curious About AI in Coding

Large Language Models (LLMs) like ChatGPT have been buzzing in the tech community for their potential to aid programming tasks. Whether you’re a seasoned coder or just dipping your toes in the coding pool, LLMs can help streamline tasks, offer personalized feedback, or even generate entire code snippets. This makes coding more accessible to everyone.

On the flip side, DeepSeek, a rising contender from China, has been making headlines for its impressive capabilities, especially in logical reasoning and deductive thinking. With its recent version, DeepSeek-R1, the company claims to produce results comparable to ChatGPT’s—at a lower cost. Sounds intriguing, right?

The study in question took this showdown to a whole new level by assessing both models using programming tasks from Codeforces, a well-known platform for competitive coding challenges. Out of 29 selected tasks, they tested both AI models on varying levels of difficulty—easy, medium, and hard.

The Results Are In!

Easy Tasks: Friendly Rivalry

When it came to easy-level tasks, both ChatGPT and DeepSeek performed well. They both managed an 100% acceptance rate, meaning all their outputs were correct. This indicates that both models can handle simpler coding challenges quite well—a reassuring fact for beginners or those looking to automate straightforward coding tasks.

Medium Tasks: The Heat Ramps Up

However, the competition got spicy with medium-level tasks where ChatGPT outshone DeepSeek by a notable margin. ChatGPT achieved a 54.5% acceptance rate compared to DeepSeek’s 18.1%. This means ChatGPT found success in more than half of the medium tasks, showcasing its improved performance in navigating more complex programming questions.

The results suggest that if you’re looking for reliable assistance for medium-difficulty coding problems, ChatGPT might still hold the crown.

Hard Tasks: Not Quite There Yet

But hold your horses—both models took a nosedive when faced with the hard tasks. The success rate dwindled significantly, with ChatGPT only managing a 11.1% acceptance rate and DeepSeek scoring a 0%. This brings to light an important fact: even advanced AI models still struggle with complex coding challenges. So, while AI can assist in many tasks, it’s clear that human expertise remains crucial for high-complexity problems.

Breaking Down Performance

Memory and Time Efficiency

When we look at the performance metrics, memory and time efficiency play a vital role in how well these models perform. ChatGPT showed more optimized memory usage and quicker execution times while handling the easy and medium tasks. DeepSeek, although technically correct in some cases, exhibited higher memory consumption. This suggests that while it might produce results, it could often lead to inefficiencies that could slow down the coding process.

So, What Does This Mean?

  1. Accessibility: The findings reinforce that AI tools like ChatGPT make programming more accessible for novices, who can use these models to enhance their coding skills without significant prior experience.

  2. Human Touch Needed: For more complex tasks, there’s a clear message: coding still requires human intuition and expertise. Even as these models improve, they can’t fully replace human coders.

  3. Room for Growth: Both models show potential but also have areas needing further development, especially in handling complex tasks smoothly.

Practical Implications

For Beginners: Embrace AI

If you’re just starting your coding journey, utilizing AI tools can accelerate your learning. Whether it’s for homework help or project support, tools like ChatGPT can lessen the pressure of learning programming from scratch. Don’t hesitate to experiment with prompts and see what outcomes different models can deliver!

For Developers: Level Up Your Skills

For more seasoned programmers, using AI can enhance productivity. Think of it as having a co-pilot alongside you as you navigate through extensive and intricate codebases. Use these models to optimize your coding routines, but always ensure you verify and refine the code they generate.

For Researchers and Educators: Innovate Curriculum

The insights from this study can provide a wealth of information for educators and researchers looking to enhance programming curricula. Whether it’s developing courses that integrate AI tools effectively or creating research that focuses on improving these models’ performances, there are numerous directions to take.

Key Takeaways

  • AI Models in Coding: Both ChatGPT and DeepSeek show promise in solving programming tasks, particularly for simpler problems.
  • Medium Difficulty Showdown: ChatGPT significantly outperformed DeepSeek on medium-level tasks, suggesting its better-suited for non-trivial coding issues.
  • Hard Task Dilemma: Regardless of brand, both AI models struggle with complex problems without human intervention.
  • Real-World Applications: For novice coders, these tools can make learning easier; for pros, they can enhance efficiency but should not replace human oversight.
  • Future Directions: Continued development of LLMs must focus on enhancing their capabilities for high-complexity programming challenges.

AI is changing the way we understand coding, but it’s still clear that a balance between man and machine is essential. Embrace these advancements, experiment with their application, and continue honing your skills in this ever-evolving tech landscape!

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.

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Stephen Smith
Stephen is an AI fanatic, entrepreneur, and educator, with a diverse background spanning recruitment, financial services, data analysis, and holistic digital marketing. His fervent interest in artificial intelligence fuels his ability to transform complex data into actionable insights, positioning him at the forefront of AI-driven innovation. Stephen’s recent journey has been marked by a relentless pursuit of knowledge in the ever-evolving field of AI. This dedication allows him to stay ahead of industry trends and technological advancements, creating a unique blend of analytical acumen and innovative thinking which is embedded within all of his meticulously designed AI courses. He is the creator of The Prompt Index and a highly successful newsletter with a 10,000-strong subscriber base, including staff from major tech firms like Google and Facebook. Stephen’s contributions continue to make a significant impact on the AI community.

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