Can AI Solve Your Coding Interview? How Large Language Models Perform on LeetCode Challenges

Introduction

Ever wondered how well AI can tackle those tough LeetCode coding challenges? With the rise of Large Language Models (LLMs) like GPT-4 and GitHub Copilot, developers are increasingly relying on AI to assist in coding, debugging, and problem-solving. But can these models actually compete with human programmers when it comes to solving algorithmic problems quickly and efficiently?

A recent study by researchers Lun Wang, Chuanqi Shi, Shaoshui Du, Yiyi Tao, Yixian Shen, Hang Zheng, and Xinyu Qiu takes an in-depth look at how well LLMs perform on LeetCode problems. Their research evaluates both the correctness and efficiency of AI-generated solutions, comparing them against human-written code.

How the Study Was Conducted

The researchers approached this question systematically, using a three-phase experiment:

Data Collection – They gathered 2,014 LeetCode problems spanning different difficulty levels (Easy, Medium, Hard) and covering a range of topics like data structures, algorithms, and system design.
Code Generation – AI models, including GPT-4, GPT-3.5-turbo, GitHub Copilot, and others, were used to generate multiple solutions for each problem using different settings (control parameters like "temperature" to adjust randomness).
Solution Evaluation – The generated code was submitted to LeetCode’s online judge system, which tested correctness and runtime performance.

By analyzing these AI-generated solutions, the researchers aimed to answer questions like:

How often do LLMs provide the correct solution?
How fast do these AI-generated solutions run compared to human-written code?
Do different AI models perform differently in algorithmic problem-solving?

Can AI Generate Correct Solutions?

To test whether LLMs could solve LeetCode problems correctly, the study used the pass@k metric—which measures the probability of getting a correct solution within k attempts.

Some key findings:

The best-performing AI model achieved a near-perfect score (~98%), showing that LLMs can solve many problems correctly.
GPT-4 and GPT-4-turbo performed well, but were significantly behind the best AI model, suggesting room for improvement.
GitHub Copilot and other smaller models had mixed results, often producing incorrect or suboptimal solutions, proving that not all AI assistants are equally capable.

Why Does This Matter?

For developers using AI for coding help, this means that while models like GPT-4 are quite capable, they’re not perfect. They may generate incorrect or inefficient solutions, requiring human oversight. Relying blindly on AI for coding interviews or real-world applications could be risky.

But Is AI Code Efficient?

Correctness is one thing, but in coding interviews and production software, efficiency matters a lot. A working solution that takes too long to execute is just as bad as a wrong one!

The study evaluated efficiency using three key factors:

Execution Time – How fast does the AI-generated code run?
Memory Usage – How much system memory does it consume?
LeetCode Runtime Percentile Ranking – How does AI-generated code compare to human-written solutions?

The Hard Truth About Performance

The best-performing AI model ranked in the 97-98% percentile, meaning it beat almost all human-written code in terms of speed.
GPT-4 solutions ranked around the 63rd percentile, meaning they were faster than only about 63% of human submissions. This suggests most human programmers still outperform GPT-4 in terms of speed and efficiency!
Models like GitHub Copilot performed even worse, struggling to generate efficient code consistently.

What This Means for Developers

If you’re using LLMs for algorithm-heavy interviews, you should double-check AI solutions for both correctness and efficiency.
AI-generated code might require manual optimization, especially for time-sensitive applications like high-frequency trading, real-time analytics, or large-scale web apps.
Expect different AI models to vary significantly in their performance—bigger models (like GPT-4) tend to perform better but still don’t always beat the best human logic.

How AI Models Compare to Human Programmers

One of the most interesting findings in the study was how AI solutions compared to human-written code at scale. Using LeetCode’s vast dataset of historically submitted code, researchers could directly compare AI-generated solutions with those written by real developers.

The best AI model performed as well as top human programmers, but these elite AI models are not yet widely available for everyday use.
GPT-4 and similar models outperformed average human programmers but did not come close to the very best human-written solutions.
Some AI models performed worse than beginners, meaning blind reliance on AI for coding might do more harm than good for inexperienced developers.

These results highlight an AI-human performance gap—while AI can assist coding, human intuition and experience still play a key role in producing the best solutions.

Can You Improve AI Outputs?

If you're using AI tools for solving coding problems, there are a few strategies to improve the quality of AI-generated solutions:

Refine Your Prompts – Clearly state problem constraints, required optimizations, and expected output format when prompting an AI model.
Test Multiple Variations – Generating multiple responses (like in this study) increases the chances of getting an optimal solution.
Check for Efficiency – Even if the AI provides a working code snippet, benchmark runtimes and review space complexity before using it.
Don't Rely on AI Completely – AI makes mistakes! Always review, debug, and verify solutions before submitting them in an interview or real-world scenario.

Conclusion

This study provides essential insights into the capabilities and limitations of LLMs in solving programming challenges. The key takeaways?

Key Takeaways

✅ LLMs are great at solving coding problems, but they’re not perfect. Even the best models sometimes produce incorrect answers or suboptimal code.

✅ AI-generated code is not always efficient. Many AI solutions are slower than expert human-written code, meaning manual optimization may be necessary.

✅ GPT-4 outperforms average human coders but lags behind top programmers. AI can be a helpful assistant—but human intuition and experience are still crucial for producing the best code.

✅ Not all AI models are equal. Advanced models like GPT-4 perform well, but smaller models (like GitHub Copilot) can struggle with complex tasks.

✅ Better prompts and testing multiple AI solutions improve results. If you're using AI to solve coding problems, refining your prompts and verifying solutions can significantly enhance the quality of AI-generated code.

As AI continues to evolve, it's clear that LLMs will become even more powerful assistants in software development. But for now, if you’re preparing for technical interviews or building performance-critical applications, human expertise is still irreplaceable!

What are your thoughts? Have you used AI tools like ChatGPT or GitHub Copilot for coding? Share your experiences in the comments! 🚀