What is DeepSeek R1 Model, and How it Ranks Against OpenAI's o1?

AI models are evolving fast, and every few months, a new name emerges within the AI ecosystem. DeepSeek R1 is one such model that has quickly gained attention for its strong reasoning capabilities and cost-efficient design. Naturally, this has raised questions about how it compares with established models like OpenAI’s o1. 

As interest in reasoning-focused AI continues to grow, understanding where DeepSeek R1 fits has become increasingly important. In this blog, we break down how R1 works, where it’s used, and why it’s becoming a model worth watching.

What is DeepSeek R1? 

DeepSeek R1 is a state-of-the-art Large Language Model (LLM) developed by the Chinese AI firm DeepSeek. Designed to rival industry leaders like OpenAI’s GPT-4o (o1), it combines real-time data processing, multi-domain adaptability, and long-context reasoning to deliver high-performance solutions for enterprises and Developers. 

Built on a sophisticated Mixture of Experts (MoE) architecture, R1 is optimised for efficiency, scalability, and precision. These attributes position it as a formidable competitor in the global AI race. 

What Does DeepSeek Do? 

Founded in 2023, DeepSeek specialises in creating AI-driven solutions for industries ranging from healthcare to finance. The company’s mission is to democratise access to advanced AI tools while prioritising cost-efficiency and user accessibility. Beyond R1, DeepSeek offers: 

1) Custom AI integration for enterprise workflows 

2) Cloud-based AI services for Subject Matter Experts (SMEs) 

3) Open-source frameworks to foster Developer innovation 

With a focus on bridging the gap between academic research and commercial applications, DeepSeek has rapidly gained traction in Asia and is now expanding its footprint in Western markets. 

How Does DeepSeek-R1 Work? 

For a better understanding of What is DeepSeek R1, let us take a look at how DeepSeek R1 works:

1) Mixture of Experts Architecture 

Unlike traditional monolithic models, R1 employs a Mixture of Experts (MoE) framework, where multiple specialised sub-models (“experts”) collaborate to handle tasks. This approach offers two key advantages: 

1) Efficiency: Only relevant experts activate per task, reducing computational costs by up to 70%. 

2) Scalability: Experts can be independently trained or updated, enabling seamless adaptation to new domains. 

For example, when processing a medical query, R1’s healthcare-focused experts take precedence, ensuring accuracy without taxing unrelated components. 

2) Reinforcement Learning and Supervised Fine-Tuning 

R1’s training pipeline involves: 

1) Supervised Fine-tuning (SFT): Initial training on high-quality, domain-specific datasets. 

2)  Reinforcement Learning from Human Feedback (RLHF): Iterative refinement based on human evaluator inputs to align outputs with ethical and practical standards. 

This dual-phase training ensures R1 balances technical proficiency with contextual awareness, a critical factor in applications like legal Document Analysis or Customer Service automation. 

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Key Features of DeepSeek R1 

Let us look at a few key features that let DeepSeek R1 standout in this evolving market space:
 

1) Real-time Data Processing 

R1 processes and analyses data in real time, making it ideal for time-sensitive sectors like finance. For instance, it can evaluate stock market trends and generate trading insights within milliseconds—a 40% speed improvement over earlier models. 

2) Multi-domain Applicability 

From diagnosing rare diseases in healthcare to optimising supply chains in logistics, R1’s versatility stems from its modular design. Early adopters include Peking University Hospital, which reported a 25% reduction in diagnostic errors during trials. 

3) Long Context Handling 

R1 supports context windows of up to 128,000 tokens, enabling it to manage lengthy documents such as research papers or legal contracts. This capability is bolstered by advanced memory management algorithms that retain critical information over extended interactions. 

4) User-friendly Interface 

DeepSeek prioritises accessibility with tools like: 

1) Drag-and-drop model customisation. 

2) API integration kits for non-developers. 

3) Interactive dashboards for performance analytics. 

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DeepSeek R1 Use Cases

DeepSeek R1's capabilities indicate strong potential across multiple applications, including the following:
 

1) Software Development

R1 can support Developers by generating code snippets, identifying bugs and explaining complex Programming concepts in a clear, practical manner. It can help improve development speed and code quality by reducing repetitive manual effort.

2) Mathematics

Its ability to solve and explain advanced mathematical problems makes it valuable for academic research, learning support and analytical problem-solving. This helps with a clearer understanding of complex equations and theoretical concepts.
 

3) Content Creation, Editing and Summarisation

R1 can produce high-quality written content, refine existing material, and create concise summaries, benefiting sectors such as marketing, legal services, and communications. This ensures consistency, clarity and time-efficient content workflows.
 

4) Customer Service

R1 can power intelligent chatbots that interact with users, respond to enquiries, and provide assistance without requiring direct human intervention. This improves response times and ensures consistent customer support availability.

5) Data Analysis

It can process large datasets, uncover meaningful insights, and generate detailed reports to support data-driven business decisions. This helps organisations identify trends, patterns and performance gaps more effectively.
 

6) Education

R1 can function as a digital tutor, simplifying complex topics, answering questions, and delivering personalised learning support across a wide range of subjects. This boosts learner engagement and supports self-paced education.

How is DeepSeek R1 Affecting the AI Industry? 

DeepSeek R1 is impacting the AI industry in the following ways:

1) DeepSeek R1 has challenged the “brute force scaling” approach that dominated AI development, showing strong performance with far lower training costs. 

2) This has sparked industry-wide debate about efficiency versus sheer model size. 

3) By being open source and affordable, R1 has broadened access to advanced AI technology. Researchers, Developers and smaller organisations can now build and experiment without huge infrastructure costs. 

4) DeepSeek’s emergence caused notable stock market reactions and prompted investors to reconsider AI valuation and capital allocation strategies.
 

Who is Behind DeepSeek? 

DeepSeek was founded by a team of AI veterans from Tsinghua University and Baidu, with backing from venture capital firms like Sequoia China. The company’s R&D hub in Shenzhen employs over 500 Engineers and Researchers, many specialising in neural network optimisation and Quantum Computing.   

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Why DeepSeek is Targeting American Companies Like Nvidia? 

DeepSeek’s collaboration with Nvidia aims to co-develop AI-specific GPUs tailored for MoE architectures. This partnership reduces reliance on foreign hardware and positions DeepSeek to challenge US dominance in AI infrastructure. With China’s AI market projected to grow, such alliances could redefine global supply chains.  
 

How is DeepSeek R1 Different From Other Models?

DeepSeek has positioned its R1 model alongside some of the most advanced language models available now, including OpenAI’s GPT-4o and o1, Meta’s Llama 3.1. Let's explore where it stands amongst its market competitors, so you understand What is DeepSeek R1 even better:
 

1) Capabilities

DeepSeek R1 performs at a level comparable to leading models. It shows particularly strong results in Coding and Mathematics, outperforming several competitors in these areas. While its English proficiency appears weaker, it still shows solid performance in long-context handling.
 

2) Cost

One of R1’s most notable benefits is cost efficiency. It was reportedly trained using fewer and less expensive AI chips which results in lower development and operational costs. Despite requiring less computational power, R1 delivers performance that rivals larger models.
 

3) Accessibility

DeepSeek R1 is partially open source and freely accessible, similar to Llama 3.1 and Qwen2.5. This gives the users more flexibility to customise and integrate the model. In contrast, models like GPT-4o and Claude 3.5 Sonnet have stricter licensing and access limitations.
 

4) Nationality

Unlike most comparable models developed in the United States, DeepSeek R1 originates from China. As a result, it operates within local regulatory frameworks, which influence how it responds to certain topics.
 

5) Privacy Risks

As with all AI systems, privacy concerns exist around data handling and misuse. While DeepSeek R1 has attracted some scrutiny regarding data governance and regulatory oversight, its rapid adoption continues. It shows that many users prioritise performance and accessibility when choosing AI tools.
 

DeepSeek R1 vs OpenAI o1: Benchmark Performance

Here are the key comparisons between DeepSeek R1 and OpenAI o1:

1) Accuracy: OpenAI’s o1 model slightly outperforms DeepSeek R1 on the Massive Multitask Language Understanding (MMLU) benchmark, with o1 at 91.8% versus R1 at 90.8%.

2) Coding Benchmarks: On coding tests, o1 holds a slight edge (96.6 percentile vs 96.3), but both models perform strongly.

3) Cost: DeepSeek R1’s API pricing is significantly lower than OpenAI’s o1. Reported figures show R1 costs around £0.41 per million input tokens and £1.62 per million output tokens, whereas o1 pricing often sits around £1.62 to £44.40 per million tokens.

Conclusion 

Learning What is DeepSeek R1 is vital because it isn’t just another LLM; it’s a strategic disruptor reshaping AI’s competitive landscape. By blending efficiency, transparency, and versatility, it challenges incumbents like OpenAI while empowering businesses to harness AI sustainably. As the industry evolves, R1’s success will hinge on its ability to strike a balance innovation and ethical responsibility. 

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