OpenAI o3-mini vs DeepSeek-R1: Which is Better?
The AI landscape has recently been invigorated by the release of OpenAI’s o3-mini, which stands as a tough competition to DeepSeek-R1. Both of them are advanced language models designed to enhance reasoning & coding capabilities. However, they differ in architecture, performance, applications, and accessibility. In this OpenAI o3-mini vs DeepSeek-R1 comparison, we will be looking into these parameters and also comparing the models based on their performance in various applications involving logical reasoning, STEM problem-solving, and coding. So let’s begin and may the best model win!
Table of Contents
- OpenAI o3-mini vs DeepSeek-R1: Model Comparison
- Architecture and Design
- Features Comparison
- OpenAI o3-mini vs DeepSeek-R1: Performance Benchmarks
- OpenAI o3-mini vs DeepSeek-R1: Application-based Comparison
- Task 1: Coding
- Task 2: Logical Reasoning
- Task 3: STEM Problem Solving
- Application Performance Comparison Summary
- Conclusion
- Frequently Asked Questions
OpenAI o3-mini vs DeepSeek-R1: Model Comparison
OpenAI’s o3-mini is a streamlined version of the o3 model, emphasizing efficiency and speed without compromising advanced reasoning capabilities. DeepSeek’s R1, on the other hand, is an open-source model that has garnered attention for its impressive performance and cost-effectiveness. The release of o3-mini is seen as OpenAI’s response to the growing competition from open-source models like DeepSeek-R1.
Learn More: OpenAI o3-mini: Performance, How to Access, and More
Architecture and Design
OpenAI o3-mini: Built upon the o3 architecture, o3-mini is optimized for faster response times and reduced computational requirements. It maintains the core reasoning abilities of its predecessor, making it suitable for tasks requiring logical problem-solving.
DeepSeek-R1: It is an open-source model developed by DeepSeek, a Chinese AI startup. It has been recognized for its advanced reasoning capabilities and cost-effectiveness, offering a competitive alternative to proprietary models.
Also Read: Is Qwen2.5-Max Better than DeepSeek-R1 and Kimi k1.5?
Features Comparison
| Feature | OpenAI o3-mini | DeepSeek-R1 |
| Accessibility | Available through OpenAI’s API services; requires API key for access. | Freely accessible; can be downloaded and integrated into various applications. |
| Transparency | Proprietary model; source code and training data are not publicly available. | Open-source model; source code and training data are publicly accessible. |
| Cost | .10 per million input tokens; .40 per million output tokens. |
.14 per million input tokens (cache hit); .55 per million input tokens (cache miss); .19 per million output tokens. |
Also Read: DeepSeek R1 vs OpenAI o1 vs Sonnet 3.5: Battle of the Best LLMs
OpenAI o3-mini vs DeepSeek-R1: Performance Benchmarks
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Logical Reasoning Tasks: In the Graduate-Level Google-Proof Q&A (GPQA) benchmark, o3-mini (medium) and o3-mini (high) outperform DeepSeek-R1. This demonstrates its superior performance in detailed and factual question-answering tasks.
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Mathematical Reasoning: In the American Invitational Mathematics Examination (AIME) benchmark, o3-mini (high) outperforms DeepSeek-R1 by over 10%, showcasing its dominance in mathematical problem-solving.
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Coding Capabilities: In competitive programming, o3-mini (high) achieves a Codeforces rating of 2,029, surpassing DeepSeek-R1’s rating of 1,820. This indicates o3-mini’s superior performance in coding tasks.
OpenAI o3-mini vs DeepSeek-R1: Application-based Comparison
For this comparison, we will be testing out DeepSeek’s R1 and OpenAI’s o3-mini (high) which are currently the best coding and reasoning models of these developers, respectively. We will be testing the models on coding, logical reasoning, and STEM-based problem-solving. For each of these tasks, we will give the same prompt to both the models, compare their responses and score them. The aim here is to find out which model is better for what application.
Note: Since o3-mini and DeepSeek-R1 are both reasoning models, their responses are often long, explaining the entire thought process. Hence, I will only be showing you snippets of the output and explaining the responses in my analysis.
Task 1: Coding
First, let’s start by comparing the coding capabilities of o3-mini and DeepSeek-R1, by asking it to generate a javascript code for an animation. I want to create a visual representation of colour mixing, by showing primary coloured balls, mixing with each other upon collision. Let’s see if the generated code runs properly and what quality of outputs we get.
Note: Since I’ll be testing out the code on Google Colab, I’ll be adding that to the prompt.
Prompt: “Generate JavaScript code that runs inside a Google Colab notebook using an IPython display. The animation should show six bouncing balls in a container with the following features:
- Two blue, two red, and two yellow balls moving randomly and bouncing off walls
- Color mixing: When two balls collide, they mix based on additive color blending (e.g., yellow blue = green, red blue = purple, red yellow = orange)
- If a mixed-color ball collides again, it continues to mix further (e.g., green red = brown)
- Physics-based motion with smooth updates
Ensure that the JavaScript code is embedded in an HTML
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