ChatGPT vs Qwen vs DeepSeek

ChatGPT vs DeepSeek vs Qwen

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ChatGPT vs Qwen vs DeepSeek.

A comprehensive study compares the performance of ChatGPT, Qwen, and DeepSeek across various real-world AI applications, including language understanding, data analysis, and complex problem-solving.

This article benchmarks three AI models—ChatGPT, Qwen, and DeepSeek—across various tasks, including physics simulations, problem-solving, and creative writing. DeepSeek excels in precision and complex calculations, making it ideal for scientific and engineering applications. Qwendemonstrates strong problem-solving speed and multilingual capabilities, suitable for business and legal tasks. ChatGPT, while proficient in creative writing, struggles with complex problems, requiring multiple attempts for solutions. The comparison highlights the unique strengths and weaknesses of each model, guiding users towards the most appropriate AI tool based on their specific needs. Ultimately, the article advocates for choosing AI models based on task-specific requirements rather than solely focusing on general performance.

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

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

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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

— — — — — — — — — — — —

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

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

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#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. 

#2 Deepseek

DeepSeek’s output left me genuinely amazed. While ChatGPT was quick to generate code, DeepSeek took 200 seconds just to think about the problem. DeepSeek didn’t just write a functional script; it crafted a highly optimized, physics-accurate simulation that handled every edge case flawlessly.

#3 Qwen’s Output: A Disappointing Attempt

If ChatGPT’s output was underwhelming, Qwen’s was downright disappointing. Given Qwen’s strong reputation for handling complex tasks, I really had high expectations for its performance. But when I ran its code for the rotating ball simulation, the results were far from what I expected. Like ChatGPT, Qwen generated code almost instantly — no deep thinking.

The ball was outside the box for most of the simulation, completely defying the laws of physics.The box itself was half out of frame, so only a portion of it was visible on the canvas.

2. Comparing ChatGPT, Qwen, and DeepSeek’s Responses to a Classic Pursuit Puzzle

When it comes to solving real-world problems, not all AI models are created equal. To test their capabilities, I presented a classic pursuit problem:

“A valuable artifact was stolen. The owner began pursuit after the thief had already fled 45 km. After traveling 160 km, the owner discovered the thief remained 18 km ahead. How many additional kilometers must the owner travel to catch the thief?”

1. ChatGPT’s Response

ChatGPT took 3 attempts to arrive at the correct answer. Initially, it misinterpreted the problem but eventually corrected itself, demonstrating persistence though lacking efficiency in its first tries

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2. DeepSeek’s Response

DeepSeek also answered correctly on the first try but took slightly longer than Qwen. It delivered a detailed, step-by-step solution with clear reasoning, proving its strength in deep thinking and accuracy

2. Qwen’s Response

Qwen answered correctly on the first try and did so faster than DeepSeek. It provided a concise and accurate solution without unnecessary steps, showcasing strong problem-solving speed and precision.

Conclusion

While all three AIs eventually answered correctly, Qwen stood out for its speed and efficiency, while DeepSeek showcased its methodical approach. ChatGPT required multiple attempts

Humanizing AI Content: The Human Side of AI

While speed and efficiency are often celebrated in AI, the real game-changer is emotional intelligence — the ability to understand, interpret, and respond to human emotions. While AI models like DeepSeek excel in precision and logic, and ChatGPT shines in creativity. Let’s test it out

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Prompt: Write a messy emotional love letter

— — — — — — — —

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Chatgpt:

Deepseek:

Qwen:

Interestingly, when tested for human-like originality, all three models — ChatGPT, DeepSeek, and Qwen — struggled to break free from their AI-generated patterns. Note: all three began their responses with the same robotic line: “I don’t even know where to start”. Any how I had high expectations with Chatgpt but Qwen won!

Key Takeaways:

DeepSeek: The go-to for research and critical thinking, outperforming others in precision and depth.

Qwen: Matched DeepSeek in solving the classic riddle on the first try and won in humanized content, making it a strong all-rounder.

ChatGPT: Took multiple tries to solve the riddle but remains a top choice for creative tasks and human-like interactions.

Final Verdict: Who Should Use Which AI?

  • Researchers: DeepSeek
  • Engineers: DeepSeek
  • Writers: ChatGPT or Qwen
  • Lawyers: Qwen with chatgpt
  • Educators: ChatGPT
  • Content Creators: Qwen and deep-thinking from Deepseek

What this means: The benchmarking results provide critical insights into the strengths and limitations of each model, helping businesses and developers choose the best AI solution for specific tasks. This also highlights the rapid evolution of AI capabilities in real-world scenarios. [Listen] [2025/02/03]

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

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