Such APIs and models of artificial intelligence (AI) have different options available to developers, but one of the noteworthy ones in the market is Deepseek R1 alongside OpenAI o1. Their distinction, however, stems from the pricing model they follow, the resource requirements of the hardware used, the energy they consume, and features targeted towards developers, as well as their KPIs. In this blog, I will delve into the DeepSeek R1 vs OpenAI o1 analysis from a developer’s perspective and highlight the differences that can help you make a better choice.
1. Pricing:
The pricing models for DeepSeek R1 and OpenAI o1 are very distinct from each other, and this is evident in the types of developers and organizations that work with each service provider.
| Aspect | DeepSeek R1 | OpenAI o1 |
| API Access | Affordable, and accessible for startups & developers. | Higher-cost, positioned as a premium service. |
| Self-Hosting | Open-source, allowing developers to self-host. | Proprietary, requiring OpenAI’s cloud services. |
| Fine-Tuning | Affordable fine-tuning for model customization. | Available, but expensive for smaller teams. |
| Compute Usage | Optimized for low-cost training & inference. | Requires significant computing power, increasing expenses. |
| Input Cost (per million tokens) | $0.55 | $15.00 |
| Output Cost (per million tokens) | $2.19 | $60.00 |
| Cost Difference | 96.4% cheaper than OpenAI’s API | – |
DeepSeek R1 vs. OpenAI o1: Price Comparison
2. Hardware Resource Comparison:
The infrastructure on which these models are built is crucial for their scalability and performance. These two models have differing infrastructure on which they are built.
| Aspect | DeepSeek-R1 | OpenAI o1 |
| Compute Infrastructure | Efficient, trains & deploys on modest hardware. | Uses high-end NVIDIA GPUs & TPU clusters, expensive. |
| Memory Usage | Lower VRAM use works on diverse setups. | Requires high memory, limiting flexibility. |
| Number of GPUs Used | Used 2,048 Nvidia H800 GPUs for training. | Used 16,000 Nvidia GPUs for training. |
| Training Scalability | Scales with limited hardware resources. | Needs high-end computing like Azure/NVIDIA GPUs. |
3. Power Efficiency:
There is a growing focus on energy consumption and power efficiency from companies and developers in the quest for enhanced sustainability.
| Aspect | DeepSeek | OpenAI |
| Energy Consumption | Optimized architecture = lower power usage. | Higher power use due to dense model activation. |
| Green Computing | Energy-efficient, and aligns with sustainable practices. | A higher carbon footprint relies on data centers. |
| Scalability & Deployment | Runs on cost-effective GPUs, easier scaling. | Needs enterprise-grade hardware, and limits deployment. |
4. Developer-Focused Features:
For developers, the support and flexibility that is offered by a platform is greatly valued in terms of how effective and tailored their use of the platform can be.
| Aspect | DeepSeek | OpenAI |
| Open-Source | Yes (MIT License), full customization. | No, closed-source, proprietary. |
| API & SDKs | Basic API, community-driven improvements. | Enterprise-grade API, strong documentation. |
| Customizability | Highly flexible, allows easy model adaptation. | Limited customization without enterprise access. |
| Community Support | Strong open-source developer community. | OpenAI-backed, structured enterprise support. |
5. Deployment & Integration:
Regarding the deployment of AI models, developers require greater flexibility along with the ability to integrate other tools and platforms seamlessly.
| Aspect | DeepSeek | OpenAI |
| On-Premise Deployment | Supports on-premise, developers’ control data. | Primarily cloud-based, with limited offline options. |
| Integration with tools | Open integration with various ML frameworks. | Deep integration with Azure, Copilot, & Microsoft tools. |
| Edge Deployment | Optimized for edge computing (lightweight models). | Not built for edge AI, more cloud-focused. |
6. Development Efficiency:
As for the model resources needed to be used to effectively create and deploy a system, it is a major issue for developers.
| Aspect | DeepSeek R1 | OpenAI o1 |
| Development Cost | Developed with a budget of approximately $5.58 million, it is extremely cost-efficient. | Estimated development costs exceed $6 billion, a heavy financial investment. |
| Resource Utilization | Utilized around 2.78 million GPU hours, highlighting efficient resource management. | Comparable models like those from Meta have used approximately 30.8 million GPU hours. |
7. Licensing and Accessibility:
The way technology is licensed affects how developers will be able to make use of and distribute the technology.
| Aspect | DeepSeek R1 | OpenAI o1 |
| Licensing | MIT License = Free commercial use & modifications. | Proprietary, usage restrictions apply. |
| Developer Accessibility | Fully open-source, with no restrictive licensing. | Cloud-based, proprietary, limited customization. |
8. Benchmark Performance:
For developers, benchmarks serve as an utmost important criteria to judge how accurate the model is against real world practices.
| Benchmark | DeepSeek-R1 (%) | OpenAI o1-1217 (%) | Verdict |
| AIME 2024 (Pass@1) | 79.8 | 79.2 | DeepSeek-R1 wins (better math problem-solving) |
| Codeforces (Percentile) | 96.3 | 96.6 | OpenAI-o1-1217 wins (better competitive coding) |
| GPQA Diamond (Pass@1) | 71.5 | 75.7 | OpenAI-o1-1217 wins (better general QA performance) |
| MATH-500 (Pass@1) | 97.3 | 96.4 | DeepSeek-R1 wins (stronger math reasoning) |
| MMLU (Pass@1) | 90.8 | 91.8 | OpenAI-o1-1217 wins (better general knowledge understanding) |
| SWE-bench Verified (Resolved) | 49.2 | 48.9 | DeepSeek-R1 wins (better software engineering task handling) |
Conclusion:
Regarding DeepSeek R1, in comparison to OpenAI o1, a developer will always find greater cost effectiveness, adaptability, and flexibility due to its open source nature, which is perfect for low-budget teams and developers. In contrast, OpenAI o1 offers unparalleled efficiency at specific tasks, but has a much higher cost and is far less flexible in its deployment and customization options.
Deciding between the two comes down to what is more valuable to you; if you’re after a high end, premium service, then this is the best option for you, otherwise if low-cost, flexible open source software is what you need to further your developmental objectives, then this is the ideal software for you.
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