The rapid evolution of large language models has brought two notable contenders to the forefront: OpenAI’s o3-mini and DeepSeek R1. While both target enterprise and developer use cases, their architectures, performance profiles, and cost structures diverge significantly. Below is a detailed analysis based on verified technical specifications and benchmark results.
Survey| Parameter | o3-mini | DeepSeek R1 |
| Total parameters | Estimated 200 billion | 671 billion |
| Active parameters/token | Full dense | 37 billion |
| Context window | 200K tokens | 128K tokens |
| Training tokens | Not disclosed | 14.8 trillion |
| Training compute | Estimated 1.2M A100-hours | 2.664M H800 GPU-hours |
| Architecture | Dense Transformer | Mixture-of-Experts (MoE) |
| Release date | Jan/Feb 2025 | January 2025 |
| API cost (input/output) | $9.50/$38 per M tokens | $0.55/$2.19 per M tokens |
| AIME 2024 score | 83.6% | 79.8% |
| Codeforces percentile | Comparable to o1 | 96.3% |
| GPQA diamond score | Matches o1 | 87.6% |
| SWE-bench verified | Up to 61% | Not disclosed |
| Energy efficiency | 1.2 tokens/J | 1.9 tokens/J |
Performance and specialisation
DeepSeek R1 excels in mathematical reasoning and coding tasks. It scores 97.3% on the MATH-500 benchmark, solving advanced problems with near-perfect accuracy, and ranks in the 96.3rd percentile on Codeforces, a platform for competitive programming. Its general knowledge capabilities, measured by the MMLU benchmark, reach 90.8%, outperforming many industry-leading models.
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The o3-mini focuses on practical applications like software development. It resolves 61% of software engineering tasks on the SWE-bench test, making it suitable for tools like coding assistants. While OpenAI hasn’t disclosed its math scores, the model reduces errors by 24% compared to its predecessor, offering reliability for technical workflows.
Architectural design
The o3-mini uses a dense transformer, a traditional design where all 200 billion parameters process every input. This ensures consistent performance but demands more computational power.
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DeepSeek R1 on the other hand uses a Mixture-of-Experts (MoE) architecture. Despite having 671 billion total parameters, only 37 billion are activated per task. This selective approach reduces energy use by 40% compared to dense models, making R1 more efficient for large-scale deployments.
Training and efficiency
DeepSeek R1 is trained on 14.8 trillion tokens over 2.66 million GPU-hours, this open-source model costs just $6 million per training cycle. Its efficiency stems from techniques like multi-token prediction, which streamlines learning.
o3-mini was built using 1.2 million A100 GPU-hours, its training data remains undisclosed. The model is fine-tuned for science and engineering tasks, prioritising accuracy in fields like data analysis.
Cost and accessibility
DeepSeek R1 is significantly cheaper to operate. At $0.55 per million input tokens, it costs 17x less than the o3-mini’s $9.50 rate. For businesses processing millions of tokens daily, this difference can save thousands monthly.
Also read: Deepseek to Qwen: Top AI models released in 2025
However, the o3-mini offers free access via ChatGPT, appealing to smaller teams or experimental projects. Its integration with tools like GitHub Copilot also simplifies coding workflows.
Practical applications
o3-mini is ideal for analysing lengthy documents (e.g., legal contracts or research papers) due to its 200K-token input capacity. Its structured output support (JSON) suits API automation and data pipelines.
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DeepSeek R1 will be better for cost-sensitive tasks like batch data processing or multilingual support. Its open-source MIT license allows custom modifications, though users must manage privacy risks.
Final recommendation
- Choose DeepSeek R1 for cost efficiency, math-intensive tasks, or custom AI solutions.
- Opt for o3-mini if you need low-latency coding support, long-form analysis, or enterprise-grade security.
Both models push the boundaries of AI capabilities, but their strengths cater to different needs. As they evolve, expect advancements in energy efficiency, coding accuracy, and real-world adaptability.
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Sagar Sharma
A software engineer who happens to love testing computers and sometimes they crash. While reviving his crashed system, you can find him reading literature, manga, or watering plants. View Full Profile
