DeepSeek vs ChatGPT – The Ultimate AI Coding Showdown of 2025

ic_writer ds66
ic_date 2025-01-03
blogs

Introduction

In 2025, the competition between artificial intelligence models has reached new heights, with cutting-edge models like DeepSeek R1 and ChatGPT O3 Mini pushing the boundaries of machine reasoning, generation, and creativity. But beyond benchmarks and theory, what happens when these models face off in a practical, real-world coding challenge?

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To answer that question, we pitted DeepSeek R1 against ChatGPT O3 Mini in a dynamic Python challenge: simulate a ball spinning inside a hexagon under gravity—a task that requires geometric reasoning, physics simulation, animation design, and clean code structuring.

This article provides a comprehensive breakdown of the test environment, model performance, code outputs, strengths and weaknesses, and final verdict. It’s the AI coding battle of the year—and we’re diving in deep.

The Challenge: Simulating a Ball in a Hexagon

Task Description

Both AI models were asked to complete the following:

  • Design a regular hexagon as a boundary.

  • Simulate a ball that bounces or spins inside this hexagon.

  • Apply gravity, collision detection, and realistic motion.

  • Visualize the simulation using Python libraries.

Constraints

  • Time limit: 5 minutes per model.

  • Tools: Python (standard and optional libraries like pygame, matplotlib, numpy)

  • Prompt style: Single-shot, no code correction or clarification allowed.

DeepSeek R1 Performance

Code Output Summary

DeepSeek R1 generated a concise Python script using Pygame to render the simulation. Key features included:

  • A hexagonal bounding box calculated using trigonometry

  • Gravity applied as acceleration

  • Ball physics with velocity and bounce

  • Collision detection using vector math

Strengths

✅ Impressive grasp of geometric math (sin/cos for hexagon) ✅ Code compiles and runs with minor tweaks ✅ Handles motion and collisions convincingly ✅ Efficient loop design

Weaknesses

❌ No adjustable simulation parameters ❌ Hexagon rendering slightly off-centered ❌ No friction or bounce damping

ChatGPT O3 Mini Performance

Code Output Summary

ChatGPT O3 Mini took a slightly different approach, attempting to use matplotlib animation and basic numpy for vector physics. Key features:

  • Hexagon defined via polar coordinates

  • Ball movement inside a polygon

  • Collision angle calculation using dot products

  • Real-time visualization via FuncAnimation

Strengths

✅ More modular function design ✅ Clean code structure with comments ✅ Realistic frame-based animation

Weaknesses

❌ Initial code failed due to misaligned coordinate checks ❌ Missed edge cases (ball escaping the hexagon) ❌ Performance lag with larger frame counts

Comparative Analysis: Side-by-Side Breakdown

FeatureDeepSeek R1ChatGPT O3 Mini
Code Compilation✅ Yes (minor fixes)⚠️ Required debug tweaks
Geometry Accuracy✅ High⚠️ Moderate
Physics Simulation✅ Realistic✅ Realistic (with lag)
Visualization LibraryPygameMatplotlib
Reusability⚠️ Basic✅ High modularity
Animation Smoothness✅ Smooth⚠️ Frame jitter
Customization⚠️ Limited✅ Easily tunable
Average Completion Time (5 tries)~60 seconds~80 seconds

Expert Commentary

AI engineer Sarah Liu commented:

"DeepSeek showed better command over geometry and Pygame mechanics, but ChatGPT's code was cleaner and easier to extend. It’s a classic case of raw power versus elegant design."

Game developer Marco Tan added:

"Both models are capable. DeepSeek feels more like a fast prototyper, while ChatGPT O3 Mini is better if you're building maintainable codebases."

Practical Implications

For Developers:

  • DeepSeek is ideal for prototyping visuals and simulating physics without many tweaks.

  • ChatGPT O3 Mini is excellent for generating modular codebases with strong documentation.

For Educators:

  • Use these outputs to compare AI model strategies and approaches.

  • Great for class examples in game dev or simulation physics.

For AI Researchers:

  • Highlights different architectural biases (MoE in DeepSeek vs dense in ChatGPT Mini)

  • Real-time code challenges show how models reason under tight constraints

Final Verdict: Who Wins?

It’s a tough call, but here’s our verdict based on the challenge:

🥇 Winner: DeepSeek R1 — for its speed, visual completeness, and higher success rate across multiple attempts.

🥈 Runner-Up: ChatGPT O3 Mini — for its clean structure, maintainable logic, and impressive use of matplotlib, despite some execution flaws.

Try It Yourself

Want to see the simulations in action or tweak the prompts?

  • Check out our GitHub repo (link coming soon)

  • Modify the physics, visuals, or prompts and re-run on your local AI tools

Or try them directly on: