Google Gemini 2.0 Pro vs DeepSeek-R1: Coding Battle

Google has been making waves with all its new Gemini 2.0 experimental models. Be it handling complex tasks, logical reasoning, or coding, Google has a new model specially designed for it! The most efficient of them all is the Google Gemini 2.0 Pro Experimental model. While it may be the most capable in the Gemini 2.0 family, is it good enough to compete against leading models like DeepSeek-R1 and o3-mini? Let’s have a Gemini 2.0 Pro Experimental vs DeepSeek-R1 coding battle and test these models on different coding tasks like creating javascript animations and building Python games, to see who’s a better coder.

What is Google Gemini 2.0 Pro Experimental?

Google’s Gemini 2.0 Pro Experimental is Google’s latest AI model, built for complex tasks. It offers superior performance in coding, reasoning, and comprehension. With a context window of up to 2 million tokens, it processes intricate prompts with ease. Moreover, the model integrates with Google Search and code execution tools to provide accurate, up-to-date information.

Gemini 2.0 Pro Experimental is now available in Google AI Studio, Vertex AI, and the Gemini app for Gemini Advanced users.

Also Read: Gemini 2.0 – Everything You Need to Know About Google’s Latest LLMs

What is DeepSeek-R1?

DeepSeek-R1 is a cutting-edge AI model developed by the Chinese AI startup DeepSeek. It is an open-source model designed to deliver high efficiency in reasoning and problem-solving. This advanced model excels in coding, mathematics, and scientific tasks, offering improved accuracy and faster response times.

DeepSeek-R1 is freely accessible through the DeepSeek AI platform and its associated API services.

Gemini 2.0 Pro Experimental vs DeepSeek-R1: Benchmark Comparison

Before we get into the hands-on action, let’s have a look at how these two models have performed in standard benchmark tests. So, here are the performance scores of both Gemini 2.0 Pro Experimental and DeepSeek-R1 in various tasks across subjects.

Source: livebench.ai

Also Read: Is Google Gemini 2.0 Pro Experimental Better Than OpenAI o3-mini?

Gemini 2.0 Pro Experimental vs DeepSeek-R1: Performance Comparison

Let’s now try out these models and see if they match up to their benchmarks. We’ll give 3 different prompts to both Gemini 2.0 Pro Experimental and DeepSeek-R1, testing their coding abilities. For each prompt, we’ll run the code generated by the models and compare them based on the quality of the final output. Based on the performance, we’ll score the models 0 or 1 for each task and then tally them to find the winner.

Here are the three coding tasks we are going to try out:

1. Designing a Javascript Animation
2. Building a Physics Simulation Using Python
3. Creating a Pygame

So, let the battle begin, and may the best model win!

Task 1: Designing a Javascript Animation

Prompt: “Create a javascript animation where the word “CELEBRATE” is at the centre with fireworks happening all around it.”

Response by DeepSeek-R1

Response by Gemini 2.0 Pro Experimental

Output of generated codes

Comparative Analysis

DeepSeek-R1 created a beautiful visual of vibrant fireworks around the word ‘CELEBRATE’. Although vertical in nature, the video does bring out a sense of celebration. On the other hand, Gemini 2.0 Pro Experimental barely meets the requirements of the prompt. It created a minimalist visual of the word surrounded by colourful splatters. So, clearly DeepSeek-R1 has done it better.

Score: Gemini 2.0 Pro Experimental: 0 | DeepSeek-R1: 1

Task 2: Building a Physics Simulation Using Python

Prompt: ”Write a python program that shows a ball bouncing inside a spinning pentagon, following the laws of Physics, increasing its speed every time it bounces off an edge.”

Response by DeepSeek-R1

Response by Gemini 2.0 Pro Experimental

Output of generated codes

Comparative Analysis

Both of them have created similar visuals with a red ball inside a spinning pentagon, accelerating as it bounces off the edges. In both the simulations, the ball moves out of the pentagon when it crosses the maximum speed. However, in Gemini 2.0 Pro’s output, the ball still remains within the space and moves from corner to corner, still following the principles of Physics. Meanwhile, in DeepSeek-R1’s simulation, the ball flies out of the scene completely. Hence, Gemini 2.0 Experimental wins this round.

Score: Gemini 2.0 Pro Experimental: 1 | DeepSeek-R1: 1

Task 3: Creating a Pygame

Prompt: “I am a beginner at coding. Write me a code to create an autonomous snake game where 10 snakes compete with each other. Make sure all the snakes are of different colour.”

Response by DeepSeek-R1

Response by Gemini 2.0 Pro Experimental

Output of generated codes

Comparative Analysis

DeepSeek-R1 seems to have gotten it wrong this time as its visual output shows tiny squares instead of snakes, moving around aimlessly! Meanwhile, Gemini 2.0 Pro Experimental created a proper snakes game where 10 snakes of different colours are moving towards the same food. It even added a clear scoring chart at the end of the game, showcasing better contextual understanding and reasoning capabilities. The grid drawn in the background also adds to the game-viewing experience, allowing the viewer follow the movement of the snakes. And so, we have a clear winner for this round – Gemini 2.0 Pro Experimental!

Score: Gemini 2.0 Pro Experimental: 2 | DeepSeek-R1: 1

Final Score: Gemini 2.0 Pro Experimental: 2 | DeepSeek-R1: 1

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Conclusion

After testing Google’s Gemini 2.0 Pro Experimental and DeepSeek-R1 across multiple coding tasks, we can see that both models have strengths of their own. DeepSeek-R1 excelled in visual creativity with its impressive JavaScript animation and the way it got the colours and the shapes right in the other tasks. On the other hand, Gemini 2.0 Pro Experimental demonstrated superior physics simulation accuracy and a well-structured Pygame implementation.

However, based on our task-based evaluation, Gemini 2.0 Pro Experimental has indeed proved itself to be a better coding model. Its ability to generate structured, functional, and visually accurate code gives it an edge in real-world coding applications.

As AI models continue evolving, it will be interesting to see how they refine their coding capabilities further. Whether you prioritize logic, efficiency, or creativity, choosing the right model ultimately depends on the specific task at hand!

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Sabreena Basheer is an architect-turned-writer who’s passionate about documenting anything that interests her. She’s currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.