o3-mini vs Deepseek-R1

o3-mini and DeepSeek R1 represent two different approaches to AI model architectures, each with its strengths.

o3-mini employs a dense transformer architecture, which is more traditional and ensures that every input token utilizes all the model's parameters. While this approach guarantees robust performance across various tasks, it might not scale as efficiently when handling larger workloads. The entire model's parameters are engaged with each token, which can be resource-intensive for large-scale tasks.

On the other hand, DeepSeek R1 uses a Mixture-of-Experts (MoE) architecture. In this design, only a subset of the model’s parameters is activated for each token processed. Specifically, two out of sixteen experts are activated per token, which allows for more efficient use of resources, making it highly scalable for larger workloads. By activating a small subset of the total parameters at a time, DeepSeek R1 can handle complex tasks more effectively without consuming excessive computational resources.

Some Comparisons to Look Out for : Between o3 Mini and Deepseek R1

Q1 : Snake ball eater game

Prompt : Create a Snake Ball Eater game using HTML, CSS, and JavaScript ion a single html file, featuring a growing snake that eats balls, game over conditions, and a scoring system. Impressive visual design

Q2 : Web Solar System Explorer

Prompt : Using HTML, CSS, and JavaScript in a single HTML file to create a simulation of the solar system. Pay extreme attention to the UI to make it as intuitive as possible. Ensure that every planet appears as a sphere and is labeled with its corresponding name.

Sample Functional Stack Selection and Functional Capabilities Comparison Between o3 Mini and DeepSeek

Q3 : Bouncing Ball Hexagon

Prompt : write a html css java script in single html file where every 5 seconds a new bouncing balls with different color appear within a hexagon, make sure to handle collisions detection properly. make the hexagon slowly rotate. make sure the balls stay within the square.

Q4 : Fully functional chess game

Prompt : Design a fully functional chess game using HTML, CSS, and JavaScript in a single html file, with a responsive board, drag-and-drop piece movement, legal move validation, and check/checkmate detection

I personally love how Deepseek R1 handles the moves and changes the role to black and white ❤️

Q5 : Designing a bouncing game

Prompt : Create an interactive bouncing ball game using HTML, CSS, and JavaScript in a single HTML file. The game should feature stunning animations, a controllable ball speed, and a slider brick. If the ball falls or goes down, the game is over.

Q6 : Ocean Strom

Prompt : Visually interesting shader that can run in twigl-dot-app make it like the ocean in a storm. using html, css, javascript in a single html file

Deepseek R1 couldn't catch the things even after 5 to 6 tries!😐

Q7 : 2D alien shooter game

Prompt : Create a simple 2D alien shooter game where the player controls a spaceship at the bottom of the screen, shooting upwards to defeat waves of aliens that move down the screen. The player should be able to move left and right, shoot bullets, and avoid colliding with aliens. The game should include a scoring system and an end game state when the player loses, using html, css, java script in a single file

Q8 : Pacman clone Pygame

Prompt : Pacman clone, A pacman clone made with python and pygame

Q9 : 2D City

Prompt : Build me an amazing, 2D large, organic and epic floating island city right above you with a ton of detail. Make it a goal, iterate, using html, css, javascript in single html file

Q10 : Crafting Monster Run Game

Prompt : Reproduce the monster run game using html, css, javascript in a single html file

Benchmark performance of DeepSeek-R1

DeepSeek-R1, which incorporates multi-stage training and cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217 on reasoning tasks.

Codeforces Bench o3-mini

In competitive programming on Codeforces, OpenAI’s model o3-mini gets higher Elo scores when it uses more reasoning effort. It consistently performs better than the o1-mini. When it uses a medium level of reasoning effort, o3-mini matches o1’s performance.

GRPO x Deepseek-R1

Group Relative Policy Optimization (GRPO) is an alternative reinforcement learning approach used in DeepSeek-R1-Zero to optimize the policy model without relying on a separate critic model. Instead of estimating value functions, GRPO computes the baseline using group scores.

Key Aspects of GRPO

1. Group Sampling:

   • A group of outputs ({o_1, o_2, \dots, o_G}) is sampled from the old policy (\pi_{\theta_{old}}) for a given query (q).

2. Policy Optimization Objective:

   • The policy (\pi_\theta) is updated by maximizing a clipped PPO-like objective: [ J_{GRPO}(\theta) = \frac{1}{G} \sum_{i=1}^{G} \min \left( \frac{\pi_\theta(o_i | q)}{\pi_{\theta_{old}}(o_i | q)} A_i, \text{clip} \left( \frac{\pi_\theta(o_i | q)}{\pi_{\theta_{old}}(o_i | q)}, 1 - \epsilon, 1 + \epsilon \right) A_i \right) - \beta D_{KL}(\pi_\theta || \pi_{ref}) ]
   • This formulation prevents excessive updates by using a clipping mechanism.

3. KL Divergence Constraint:

   • The Kullback-Leibler (KL) divergence penalty ensures that the updated policy (\pi_\theta) does not deviate too much from a reference policy (\pi_{ref}).

4. Advantage Computation:

   • Instead of using a critic, the advantage (A_i) is computed from the rewards of the sampled group: [ A_i = \frac{r_i - \text{mean}({r_1, r_2, \dots, r_G})}{\text{std}({r_1, r_2, \dots, r_G})} ]
   • This normalizes the advantage values based on the group's performance.

By using GRPO, DeepSeek-R1-Zero reduces computational costs and eliminates the need for a separate critic model, making training more efficient.

Reproducing R1 ft.GRPOTrainer

To reproduce DeepSeek R1 using GRPOTrainer, follow the script provided in the link below:

📒GRPO Based Fine-tuning Script

Features Comparison

o3-mini Features

• Lightning Autocomplete: Median response time of 210ms, providing quick suggestions during coding sessions.
• IDE Plugin Integration: Supports a wide range of programming languages out of the box, making it versatile for various development environments.
• Security Scanning: Built-in capabilities to detect common vulnerabilities in code, enhancing security during development.

DeepSeek R1 Features

• Multi-Hop Debugging: Traces errors through multiple layers of code dependencies, effective for complex software systems.
• Contextual Code Completion: Provides longer and more relevant suggestions based on context, improving coding efficiency.
• Automated Refactoring: Automatically suggests improvements to legacy codebases, reducing technical debt significantly.

o3-mini vs DeepSeek R1 Pricing and Operational Costs

Limitations and Challenges

Papers about R1 & o3 mini

• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

• o3-mini vs DeepSeek-R1: Which One is Safer?

The paper "O3-MINI VS DEEPSEEK-R1: Which One is Safer?" evaluates the safety of two large language models (LLMs): OpenAI's o3-mini and DeepSeek-R1. The authors employed their automated safety testing tool, ASTRAL, to generate and assess 1,260 unsafe test inputs across various categories, writing styles, and topics. The findings indicate that DeepSeek-R1 responded unsafely to 11.98% of the prompts, whereas o3-mini responded unsafely to only 1.19%. This suggests that, based on this assessment, DeepSeek-R1 is less aligned with human values and safety standards compared to o3-mini.

For a detailed comparison of the safety performance of both models, refer to the table below:

Conclusion: Choosing Between o3-mini and DeepSeek R1

The comparison between o3-mini and DeepSeek R1 highlights two distinct philosophies in AI model design, each excelling in specific scenarios. Here’s a distilled verdict based on their strengths and limitations:

Architectural Trade-offs

• o3-mini’s dense transformer architecture ensures consistent performance across diverse tasks, making it ideal for environments where reliability and security (e.g., vulnerability scanning) are critical. However, its resource-intensive nature limits scalability for massive workloads.
• DeepSeek R1 leverages MoE and RLHF to activate only a subset of parameters per token, achieving superior efficiency (37B active parameters vs. o3-mini’s 200B dense). This makes it better suited for large-scale, dynamic applications like real-time game development or multi-file codebases.

Performance Highlights

• Coding Tasks:
  • o3-mini shines in structured tasks (e.g., chess game logic, IDE plugins) with cleaner code separation and tile-based rendering.
  • DeepSeek R1 adopts visually rich designs (neon aesthetics, 3D effects) and supports advanced features like animation speed control, appealing to interactive applications.
• Benchmarks:
  • DeepSeek R1 matches o3-mini in reasoning tasks (GRPO optimization) but lags in competitive programming (Codeforces Elo).
  • o3-mini’s training compute (1.2M A100-hours) is half of R1’s (2.66M H800-hours), yet R1’s MoE design compensates with better token efficiency.

Operational Considerations

• Cost: DeepSeek R1 offers lower API costs ($0.55/$2.19 per M I/O) but demands higher VRAM for on-prem deployment.
• Scalability: R1’s MoE architecture future-proofs it for growing workloads, while o3-mini’s dense model faces hardware constraints.

Future Outlook

Both models target a 2025 release, with o3-mini focusing on security and IDE integration, while DeepSeek R1 emphasizes dynamic scalability and multi-hop debugging. As AI-driven development evolves, the choice hinges on priorities:

• Choose o3-mini for tasks requiring deterministic outputs, low maintenance, and embedded security.
• Opt for DeepSeek R1 for resource-efficient, visually complex applications demanding adaptive scalability.

End of the article... Thank you for reading.🤗