The long-awaited OpenAI o3-mini is finally released, and what an impressive release it is. o3-mini not only outperforms the ‘o1’ regular, but it also does so at the speed of the o1-mini, the fastest OpenAI ’o’ model. People are already comparing the new LRM (Large Reasoning Model) with DeepSeek’s R1 reasoning model. But is it a valid comparison? Because R1’s record-breaking release is still buzzing. Let’s find out.
This piece compares OpenAI o3-mini with DeepSeek R1 for their coding and reasoning capabilities.
What is OpenAI o3-mini?
OpenAI o3-mini is OpenAI’s latest model in the LRM ‘o’ series. Reasoning models take time to “think” before providing an answer, unlike traditional large language models that respond instantly. Hence, the responses could take a long time to load. However, the o3-mini has changed that. Besides its impressive speed, the o3-mini also features function calling, Structured Outputs, and developer messages that make it ready to use out of the gate, as per OpenAI.
OpenAI o3-mini vs DeepSeek R1 Technical Specifications
To understand the differences between o3-mini and DeepSeek R1, we first need to examine their technical specifications. These parameters give insight into their capabilities and design philosophies.
| Parameter | OpenAI o3-mini | DeepSeek R1 |
| Total Parameters | Est. around 200 billion | 671 billion |
| Active Parameters/Token | Full dense | 37 billion |
| Context Window | 200K tokens (100K max output) | 128K tokens |
| Training Tokens | Not disclosed | 14.8 trillion |
| Training Compute | Estimated 1.2 million A100-hours | 2.664 million H800 GPU hours |
| Architecture | Dense Transformer | Mixture-of-Experts (MoE) + Reinforcement Learning from Human Feedback (RLHF) |
| Release Date | January/February 2025 | January 2025 |
OpenAI o3-mini vs DeepSeek R1 Architectural Insights
o3-mini employs a dense transformer architecture, which is more traditional but ensures that all parameters are used for every input token. While this approach can lead to robust performance, it may not scale as efficiently for larger workloads compared to DeepSeek’s MoE design.
In contrast, DeepSeek R1 uses a Mixture-of-Experts architecture, which activates a subset of its total parameters for each token processed. This allows it to manage extensive workloads efficiently while minimizing resource consumption. Specifically, it activates two out of sixteen experts per token, optimizing performance for complex tasks.
OpenAI o3-mini vs DeepSeek R1 Performance Benchmarks
To evaluate the effectiveness of these models in practical scenarios, we can look at their performance benchmarks across various coding tasks. In the following LiveBench coding tasks, OpenAI o3-mini surpasses o1-high even at medium reasoning effort, highlighting its efficiency in coding tasks:
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. Here’s a table:
Likewise, DeepSeek R1 also surpasses its predecessors in various benchmark tests:
The results for both of these models speak for themselves.
Efficiency Metrics
Efficiency is another crucial factor when comparing AI models, particularly regarding resource consumption and response times.
| Metric | DeepSeek R1 | o3-mini |
| Tokens/Second (A100) | 312 | 285 |
| Memory Consumption | 73GB | 48GB |
| Cold Start Latency | 2.1s | 1.8s |
| Energy Efficiency | 1.9 tokens/J | 1.2 tokens/J |
While o3-mini has lower memory requirements and faster cold start latency, DeepSeek R1 demonstrates superior throughput and energy efficiency for larger batch sizes due to its architecture.
OpenAI o3-mini & DeepSeek R1 Feature Comparison
Both models offer unique features tailored for different use cases:
o3-mini Features
- Lightning Autocomplete: With a median response time of just 210ms, o3-mini excels in providing quick suggestions during coding sessions.
- IDE Plugin Integration: It supports a wide range of programming languages out of the box, making it versatile for developers working in various environments.
- Security Scanning: o3-mini has built-in capabilities to detect common vulnerabilities in code, enhancing security during development.
DeepSeek R1 Features
- Multi-Hop Debugging: This feature allows the model to trace errors through multiple layers of code dependencies, making it particularly effective for complex software systems.
- Contextual Code Completion: DeepSeek R1 provides longer and more relevant suggestions based on context, improving overall coding efficiency.
- Automated Refactoring: The model can automatically suggest improvements to legacy codebases, reducing technical debt significantly.
OpenAI o3-mini vs DeepSeek R1 Pricing and Operational Costs
Understanding the cost structure is essential for organizations considering these tools:
| Cost Factor | o3-mini | DeepSeek R1 |
| API Cost (Input/Output) | $1.10/$4.40 per M | $0.55/$2.19 per M |
| On-Prem Deployment | $3.80/hr (4xA100) | $4.20/hr (8xH100) |
| Maintenance Overhead | 8% | 15% |
| Total Cost/10M Tokens | $104,500 | $27,400 |
DeepSeek R1’s pricing model becomes particularly advantageous at scale due to lower operational costs for high-volume tasks like code generation.
Limitations and Challenges
Here’s a table showcasing some limitations of both o3-mini and DeepSeek R1;
| Limitation | o3-mini | DeepSeek R1 |
| Codebase Size | Struggles with codebases exceeding ~50k lines. | Requires substantial VRAM (64GB+) for optimal performance. |
| Dependency Resolution | Lacks built-in dependency resolution. | Limited support for older programming languages (e.g., COBOL, Fortran). |
| Multi-File Analysis | Limited multi-file analysis features. | Longer response time on initial queries due to architectural complexity. |
OpenAI o3-mini Prompts to Test Against DeepSeek R1
The best way to evaluate an AI model is to test it yourself. Here are some coding and reasoning prompts that you can use to test OpenAI o3-mini and DeepSeek R1:
The Bottom Line
While both OpenAI’s o3-mini and DeepSeek R1 have strengths and weaknesses, the choice largely depends on specific user needs and project requirements. But regardless of your preferences, you can’t deny just how impressive both these models are. o3-mini’s speed and performance make it the most efficient OpenAI LRM model to date. While R1 still stands out for its impressive showcase of reasoning. To read more model comparisons similar to this, check out the Bind AI blog. And to test other advanced models like Claude 3.5 Sonnet and GPT-4o, head over to Bind AI copilot.