Comparing DeepSeek R1 and OpenAI O1: Which AI Model Comes Out on Top?

As a leader in Data & AI, I do try to stay updated on the latest Generative AI (GenAI) models in the market, and I must admit that DeepSeek's models and OpenAI O1 have genuinely impressed me. In recent weeks, several customers have asked for my personal opinion, so I’ve decided to share my own comparative analysis —solely from my own perspective and not on behalf of Microsoft.

DeepSeek and OpenAI O1 belong to a new generation of GenAI known as reasoning models. Unlike traditional GenAI models like GPT-3.5, GPT-4, or GPT-4o, which generate content by identifying patterns, these new models are built to think critically, analyze data, and make more informed decisions, making them more reliable for handling complex tasks.

DeepSeek and OpenAI O1 belong to a new generation of GenAI known as reasoning models.

What is DeepSeek?

DeepSeek, developed by the Chinese AI startup DeepSeek AI (founded in May 2023), is a cutting-edge AI model recognized for its rapid processing speed and strong contextual understanding.

Two versions of DeepSeek are currently available:

• DeepSeek V3, released on December 26, 2024, supports an input context window of 128K tokens, and an output of up to 8K tokens.
• DeepSeek R1, launched five days ago (January 20, 2025), also support 128K tokens as input and expands the output capacity to 32K tokens.

Both models excel in Chinese language tasks due to their specialized training.

Similar to Microsoft’s Shi 3.5 MoE and Mistral 7bx8 MoE, DeepSeek employs a Mixture of Experts (MoE) architecture, activating only a subset of its parameters per task. This design enhances computational efficiency, delivering high performance while consuming fewer resources than traditional dense models.

Why Is DeepSeek Gaining Popularity? While the ongoing U.S.-China rivalry may remind some of the Cold War tensions between the U.S. and the USSR, the real reason DeepSeek is making waves is its impressive performance. According to third-party benchmarks, DeepSeek-R1 appears to match OpenAI’s O1, but at a lower cost—utilizing fewer, more affordable GPUs (incl a cluster of 2048 NVIDIA H800 GPUs) and seemingly requiring less training data. This combination of efficiency and performance is driving its growing recognition in the AI space.

Note: DeepSeek unlocked Nvidia chips through highly optimized low-level enhancements. Additionally, V3 and R1 is compatible with NVIDIA and AMD GPUs. Contrary to popular belief, I do believe this is also good news for Nvidia, as organizations, labs, and universities will increasingly request GPUs for testing and training cutting-edge models.

The developer community and I also appreciate that DeepSeek’s models are open-source and released under an MIT license.

As of now, DeepSeek’s models are not yet available in the Azure AI model catalog. Therefore, customers must fill out a request form and wait until enough requests are made. In the meantime, Azure customers typically buy GPU capacity to deploy and run the model themselves, using Azure AI Infra VM Portfolio. However, I’ve learned that V3 and R1 requires a lot of hardware which may impact organization's cash-flow due to the initial investment. The recommended setup is a GPU cluster with 8 x H200 GPUs, which gives over 1TB of VRAM. Alternatively, for testing, customers can use a single ND H200 v5 virtual machine.

What is OpenAI O1?

OpenAI O1 is another model from OpenAI , a U.S.-based AI research organization founded in December 2015 with key investors such as 微软 , 英伟达 , 花旗 , 摩根大通 , SoftBank Group Corp. , and 富达 . O1 is part of OpenAI latest series of Generative AI models, focusing on deep reasoning and structured outputs. This model offers advanced features that help developers apply reasoning to tasks like inventory management, customer support, financial analysis, and more.

The O1 model introduces several innovative features:

• Expanded Context Window: A 200K-token context window allows for more detailed and nuanced responses.
• Extensive Outputs: With a maximum output of 100K tokens, OpenAI O1 can manage more detailed and extensive responses.
• Structured Outputs: The model supports structured responses constrained by JSON schemas, improving precision in output formatting.
• Reasoning Effort Parameter: Developers can adjust cognitive load with low, medium, and high reasoning levels, optimizing performance for different tasks.

Like all OpenAI models, O1 is available as a managed service through the Azure AI Foundry which also provides users with a robust set of capabilities to help organizations measure, mitigate, and manage AI risks across the AI development lifecycle for traditional machine learning and generative AI applications.

The Azure AI Foundry Model Catalog offers more than 1,600 foundation models (LLMs and SLMs) from Databricks , Deci AI, Hugging Face ,? Meta , Microsoft Research, Mistral AI , 英伟达 , the mentioned OpenAI , Stability AI and Cohere - enabling Azure customers to choose the best model for their use case.??

Azure OpenAI co-develops the APIs with OpenAI, ensuring seamless compatibility and integration with other Azure services. This provides enterprise-grade security, responsible AI features, and Microsoft's reliability. Additionally, Azure OpenAI Services offers private networking, regional availability, and AI content filtering for safer and more controlled use.

Comparative Analysis

I am not an expert here, but based on my observations, there are five categories that I think each organization should evaluate when comparing both models: performance, capabilities, architecture, cost, and use case.

Performance and Capabilities

• DeepSeek: Optimized for speed and efficiency, particularly excelling in Chinese language tasks and large-scale data analysis. Unlike O1, DeepSeek's models does not support image processing.
• OpenAI O1: Designed for complex reasoning, it provides deeper analytical responses and greater control over cognitive processing.

It's important to note that the reasoning model consistently performed best with minimal prompting. I found that providing too much context can overwhelm the model’s reasoning abilities, so we should rethink the use of advanced prompt engineering techniques.

Organizations evaluating DeepSeek have observed that it is more sensitive to prompts than OpenAI O1, with its performance declining when instructions are unclear or overly complex.

Architectural Differences

• DeepSeek: V3 uses a Mixture-of-Experts (MoE) model with 671B total parameters, activating only a subset of parameters (37B) for efficiency. MoE models combine multiple smaller models into one, which can deliver greater improvements in model quality with faster inferencing. In the other hand, R1 took V3 as base model and improved uses Reinforcement Learning (RL).
• OpenAI O1: Employs a dense model architecture with an expanded context window and structured outputs, making it ideal for intricate problem-solving.

Cost

DeepSeek appears to be more cost-effective than OpenAI while maintaining the same benchmark performance.

Ideal Use Cases

• DeepSeek: Best suited for real-time applications, language translation, and high-speed inference scenarios.
• OpenAI O1: Excels in scientific research, advanced coding, and tasks requiring structured responses and deep reasoning.

Best Practices for Leveraging Reasoning Models Effectively

By now you should know that reasoning models can be a powerful tool for solving complex problems and improving decision-making. Here are some best practices from subject matter experts to maximize their effectiveness:

1. Prompting Strategy For complex tasks, use zero-shot or single-instruction prompts to leverage the model's internal reasoning. Avoid few-shot prompting by limiting examples to one or two and testing them thoroughly. For example:"Summarize the key points of the following business proposal in 150 words". This simple instruction helps the model provide relevant responses without extra context.
2. Encouraging Deep Reasoning: For intricate tasks, ask the model to engage in more detailed reasoning, as research shows this improves outcomes. Also, use built-in reasoning for tasks that involve five or more Chain-of-Thought (CoT) steps, as reasoning models outperform non-reasoning models such as GTP4 in these cases.
3. Task Complexity Management: For simple tasks, avoid using CoT to ensure faster and more accurate results.
4. Consistency and Reliability: Keep prompts clear and concise, especially for structured tasks or code generation, to maintain consistent and reliable outputs.
5. Cost and Latency Optimization: For high-stakes tasks, run multiple iterations and select the most consistent result, assuming cost and latency are manageable. For simpler tasks, opt for non-reasoning models to reduce costs and minimize latency.

My "Initial" Conclusion

As I continue exploring DeepSeek and OpenAI O1, it’s clear that both models bring unique strengths to the table. DeepSeek offers fast processing and efficiency, while OpenAI O1 excels in advanced reasoning, structured outputs, and handling complex contexts—making it ideal for tasks requiring deep analysis in fields like science, coding, and mathematics. That said, between these two models, I do recommend OpenAI O1 as the stronger option for tasks demanding deep analytical thinking and structured outputs.

Another key takeaway is that an effective AI strategy shouldn't depend on a single model. Instead, finding the right mix ensures a balance of capability, cost (especially with O1), and business value.

Not every use case requires a reasoning-intensive model like OpenAI O1 or DeepSeek. Many organizations, particularly those focused on affordability, are adopting a hybrid approach—pairing models like O1 with 4o-mini (which remains cheaper than DeepSeek) to optimize performance while managing total cost of ownership (TCO). This strategy helps align AI investments with budget constraints and expected ROI, avoiding unnecessary reliance on high-cost reasoning models for every task.

Additionally, Microsoft Azure customers can easily integrate OpenAI models like GPT-4o, 4o-mini, and O1 through API calls, allowing flexible deployment without the complexity of managing AI infrastructure, unlike DeepSeek. For businesses, this translates to reduced capital expenditure on training infrastructure and better time-to-market.

I'd love to hear your insights — how do these models compare in your experience? Feel free to share your thoughts or reach out with any questions!

Update: January 29, 2025 4:30 PM Pacific Time

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Backed by Microsoft’s innovation and trusted cloud infrastructure, DeepSeek R1 is now more accessible than ever for organizations looking to leverage cutting-edge AI with confidence.

Learn more here: https://azure.microsoft.com/en-us/blog/deepseek-r1-is-now-available-on-azure-ai-foundry-and-github