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Benchmarking ChatGPT, Qwen, and DeepSeek on Real-World AI Tasks

Which AI Model Outperforms in Coding, Mechanics, and Algorithmic Precision— Which Model Delivers Real-World Precision?

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Wasn’t able to paste code due to reddit. I compared and ran various tests from puzzles to humanized writing all with comparison
Read full article here: Benchmarking ChatGPT, Qwen, and DeepSeek on Real-World AI Tasks | by HarshVardhan jain | Feb, 2025 | Medium

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https://preview.redd.it/ltq3cx45fyge1.jpg?width=4000&format=pjpg&auto=webp&s=5ee46b4ea0e4eedea01d86d6f0211ec2569113f1

The wealthy tech giants in the U.S. once dominated the AI market but DeepSeek’s release caused waves in the industry, sparking massive hype. However, as if that wasn’t enough, Qwen 2.5 emerged — surpassing DeepSeek in multiple areas. Like other reasoning models such as DeepSeek-R1 and OpenAI’s O1, Qwen 2.5-Max operates in a way that conceals its thinking process, making it harder to trace its decision-making logic

This article puts ChatGPT, Qwen, and DeepSeek through their paces with a series of key challenges ranging from solving calculus problems to debugging code. Whether you’re a developer hunting for the perfect AI coding assistant, a researcher tackling quantum mechanics, or a business professional, today I will try to reveal which model is the smartest choice for your needs (and budget)

Comparative Analysis of AI Model Capabilities:-

1. Chatgpt

ChatGPT, developed by OpenAI still remains a dominant force in the AI space, built on the powerful GPT-5 architecture and fine-tuned using Reinforcement Learning from Human Feedback (RLHF). It’s a reliable go-to for a range of tasks, from creative writing to technical documentation, making it a top choice for content creators, educators, and startups However, it’s not perfect. When it comes to specialized fields, like advanced mathematics or niche legal domains, it can struggle. On top of that, its high infrastructure costs make it tough for smaller businesses or individual developers to access it easily

2. Deepseek

Out of nowhere, DeepSeek emerged as a dark horse in the AI race challenging established giants with its focus on computational precision and efficiency.

Unlike its competitors, it’s tailored for scientific and mathematical tasks and is trained on top datasets like arXiv and Wolfram Alpha, which helps it perform well in areas like optimization, physics simulations, and complex math problems. DeepSeek’s real strength is how cheap it is ( no china pun intended 😤). While models like ChatGPT and Qwen require massive resources, Deepseek does the job with way less cost. So yeah you don’t need to get $1000 for a ChatGPT subscription

3. Qwen

After Deepseek who would’ve thought another Chinese AI would pop up and start taking over? Classic China move — spread something and this time it’s AI lol

Qwen is dominating the business game with its multilingual setup, excelling in places like Asia, especially with Mandarin and Arabic. It’s the go-to for legal and financial tasks, and it is not a reasoning model like DeepSeek R1, meaning you can’t see its thinking process. But just like DeepSeek, it’s got that robotic vibe, making it less fun for casual or creative work. If you want something more flexible, Qwen might not be the best hang

Testing Time: Comparing the 3 AI’s with Real-World Issues

To ensure fairness and through evaluation, let’s throw some of the most hyped challenges like tough math problems, wild physics stuff, coding tasks, and tricky real-world questions

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1. Physics: The Rotating Ball Problem

To kick things off, let’s dive into the classic “rotating ball in a box” problem, which has become a popular benchmark for testing how well different AI models handle complex task

Challenge: Simulate a ball bouncing inside a rotating box while obeying the laws of physics

Picture a 2d shape rotating in space. Inside, a ball bounces off the walls, staying within the boundaries and no external force. At first glance, it might seem simple, but accounting for gravity, constant rotation, and precise collision dynamics makes it a challenging simulation. You’d be surprised at how differently AI models tackle it

Prompt:-

Write a Python script that simulates a yellow ball bouncing inside a rotating square. The ball should bounce realistically off the square’s edges, with the square rotating slowly over time The ball must stay within the square's boundaries as the box rotates. Box Rotation: The box should rotate continuously. Ball Physics: The ball reacts to gravity and bounces off the box’s walls. Ball Inside Boundaries: Make sure the ball doesn’t escape the box's boundaries, even as the box rotates. Realistic Physics: Include proper collision detection and smooth animation Use Python 3.x with Pygame or any similar library for rendering 

Results:

https://preview.redd.it/m6kyfkksfyge1.png?width=426&format=png&auto=webp&s=088ec05bf54817c882271f15faf9c9d2fa9aec23

1. ChatGPT’s Output: Fast but Flawed

With Chatgpt I had high expectations. But the results? Let’s just say they were… underwhelming. While DeepSeek took its time for accuracy, ChatGPT instantly spat out a clean-looking script. The ball didn’t bounce realistically. Instead, it glitched around the edges of the box, sometimes getting stuck in the corners or phasing through the walls. It is clear that ChatGPT prefers speed over depth, delivers a solution that works — but only in the most basic sense

Chatgpt’s Code:

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Wasn’t able to paste code due to reddit. I compared and ran various tests from puzzles to humanized writing all with comparison
Read full article here: Benchmarking ChatGPT, Qwen, and DeepSeek on Real-World AI Tasks | by HarshVardhan jain | Feb, 2025 | Medium

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