Is OpenAI’s o3-mini Better Than DeepSeek-R1?

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!

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

Also Read: DeepSeek R1 vs OpenAI o1 vs Sonnet 3.5: Battle of the Best LLMs

OpenAI o3-mini vs DeepSeek-R1: Performance Benchmarks

• 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.
  
• 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.
  
• 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 <script> tag and displayed inside an IPython HTML cell in Google Colab.”

Response:

You can find the complete code generated by the models, here.

Output of Code:

Comparative Analysis

DeepSeek-R1 took 1m 45s to think and generate the code, while o3-mini did it in just 27 seconds!

Although both the models created well-structured code, which are similar to each other, their animations were quite different. o3-mini’s output featured larger balls on a white background that made it look clearer as compared to DeepSeek-R1’s, which was on a black background.

o3-mini’s code let the colours mix, as per the prompt, until all of them turned brown. On the other hand, DeepSeek-R1’s animation showed the mixing of colour with better accuracy, bringing in colours not mentioned in the prompt. However, R1’s code merged the balls upon collision, which was not what was asked for. So, for this task, o3-mini wins due to accuracy of the response and better clarity of the visual.

Score: OpenAI o3-mini: 1 | DeepSeek-R1: 0

Task 2: Logical Reasoning

In this task, we’ll be asking the models to solve a puzzle based on some clues, using logical reasoning.

Prompt: “Alex, Betty, Carol, Dan, Earl, Fay, George and Harry are eight employees of an organization. They work in three departments: Personnel, Administration and Marketing with not more than three of them in any department.

Each of them has a different choice of sports from Football, Cricket, Volleyball, Badminton, Lawn Tennis, Basketball, Hockey and Table Tennis not necessarily in the same order.

Dan works in Administration and does not like either Football or Cricket.
Fay works in Personnel with only Alex who likes Table Tennis.
Earl and Harry do not work in the same department as Dan.
Carol likes Hockey and does not work in Marketing.
George does not work in Administration and does not like either Cricket or Badminton.
One of those who work in Administration likes Football.
The one who likes Volleyball works in Personnel.
None of those who work in Administration likes either Badminton or Lawn Tennis.
Harry does not like Cricket.

Who are the employees who work in the Administration Department?”

Response:

OpenAI o3-mini (high)	DeepSeek-R1

Comparative Analysis

Both the models managed to give the right answer logically, explaining their thinking process. They both took almost one and a half minutes to get to the answer.

OpenAI’s o3-mini started the analysis based on the simplest and most direct clue. It then went on to assign people to departments, determine their sports, and then finally figure out the answer. In every step, the model listed out the clues which were used and what insights were gained. While explaining its thought process, the model kept rechecking and confirming its deduced insights, making it more reliable. The final response, although longer, was very well explained for anybody to easily understand.

DeepSeek-R1 took a different approach by directly assigning people (and their details) to different departments based on the clues. The thought process was explained in a conversational tone, but was very lengthy. However, the final response, while being well-structured and accurate, lacked any explanation as compared to o3-mini. It only mentioned the clues and insights.

With a better explanation and a more reliable thought process, o3-mini wins this round.

Score: OpenAI o3-mini: 2 | DeepSeek-R1: 0

Task 3: STEM Problem Solving

To test the models’ skills in science, technology, engineering, and mathematics (STEM),  we’ll ask the models to do the calculations of an electric circuit.

Prompt: “In a series RLC circuit with a resistor (R) of 10 ohms, an inductor (L) of 0.5 H, and a capacitor (C) of 100 μF, an AC voltage source of 50 V at 60 Hz is applied. Calculate:

a. The impedance of the circuit
b. The current flowing through the circuit
c. The phase angle between the voltage and the current

Show all steps and formulas used in your calculations.”

Response:

OpenAI o3-mini (high)	DeepSeek-R1

Comparative Analysis

OpenAI’s o3-mini answered the question in a lightning speed of 11 seconds, while DeepSeek-R1 took 80 seconds to give the same response.

Although both the models showed the same calculations, following a similar structure, o3-mini explained its thought process in 6 short steps. Meanwhile DeepSeek-R1 took a lot of time explaining the process and calculations, making it a bit boring or slow.

o3-miini was even smart enough to round off the current value calculated, without being explicitly told to do so. Moreover, o3-mini’s response showed the steps in detail, so I could skip the thought process and get right to the answer. Hence, o3-mini gets my vote for this task too.

Score: OpenAI o3-mini: 3 | DeepSeek-R1: 0

Final Score: OpenAI o3-mini: 3 | DeepSeek-R1: 0

Application Performance Comparison Summary

o3-mini (high) performs better and faster than DeepSeek-R1 in all the tasks – be it coding, STEM-related, or logical reasoning – establishing itself as a superior model. Here are some comparisons and insights based on their practical performance.

Conclusion

Both OpenAI’s o3-mini and DeepSeek’s R1 offer advanced reasoning and coding capabilities, each with distinct advantages. o3-mini is a faster model that seems to have a better understanding of prompts as compared to R1. Also, o3-mini re-checks and verifies its thought process at every step, making it more reliable and accurate.

However, o3-mini comes at a price while DeepSeek-R1 is an open-source model, making it more accessible to users. So for simple everyday tasks that do not advance reasoning, DeepSeek-R1 is a great choice. But for more complex tasks and faster responses, you would want to choose o3-mini. Hence, the choice between the two models depends on specific application requirements, including performance needs, budget constraints, and the necessity for customization.

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K.C. Sabreena Basheer

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.