DeepSeek-R1 - The Chinese AI Powerhouse Outperforming OpenAI's o1 — at 95% Less Cost

Hijab e FatimaPosted on January 24, 2025

22-23 Min Read Time

Summary:

• DeepSeek-R1 - A new contender in the AI market offers advanced reasoning capabilities, outperforming major Global giants like OpenAI-o1 in key benchmarks, at 95% less cost. 
• DeepSeek’s breakthrough signals a shift in the global ‘AI War’ challenging US dominance just after Stargate is facing turmoil - strongly pushing for more cost-efficient AL models. 
• The new model could have significant economic and geopolitical effects, that would redefine AI development worldwide.

Just before the year 2024 ended, the global AI landscape shifted dramatically. The Chinese AI lab launched DeepSeek-R1, a reasoning-focused model that outperformed OpenAI’s o1 on benchmarks like AIME, MATH-500, and SWE-bench. The model also comes at a fraction of the cost - talk about 90-95% less! Built on the foundation of its previous model, DeepSeek-V3, R1 pushes the boundaries of “reasoning,” a frontier many AI labs in Silicon Valley are striving to master.

This wasn’t just a technical achievement—it was a warning shot. Donald Trump started his presidency with a declaration that America must lead the world, but DeepSeek’s breakthrough shows that U.S. dominance in AI isn’t guaranteed. Stargate, a U.S.-backed initiative to drive AI innovation, now faces a serious challenge. With the constant current turmoil and friction in this US tech drive, this “AI war” is heating up. 

According to Alexandr Wang, CEO of Scale AI, China is rapidly closing the gap. They’ve built up resources like Nvidia H100 GPUs, even with strict export controls in place.

DeepSeek started as a hedge fund spin-off, but it’s now leading the trillion-dollar AI race. By focusing on reasoning—a key area for evolving AI intelligence—it’s set a new standard. R1 is available under an MIT license on Hugging Face, making it even more disruptive. DeepSeek isn’t just competing—it’s rewriting the AI playbook.

The Training Approach

Unlike traditional models, DeepSeek-R1 skips the usual supervised fine-tuning (SFT) and dives straight into reinforcement learning. This means it learns to reason and solve problems independently—no hand-holding required. The result is a model that is capable of self-verification, reflection, and creating detailed chain-of-thought (CoT) responses.

This isn’t just theory. DeepSeek-R1 is the first open-source model to prove that advanced reasoning in large language models can be achieved purely with RL. It’s a game-changer for researchers and developers looking to push the boundaries of what AI can do.

DeepSeek-R1 Vs OpenAI-O1 

Let’s take a closer look at how DeepSeek-R1 stacks up against OpenAI-o1 in terms of various benchmarks in this graph:

Source: HuggingFace 

Here is a further comparison breakdown:

1. Performance

• DeepSeek-R1 outperforms OpenAI-o1 on key benchmarks like AIME, MATH-500, and SWE-bench.
• Demonstrates higher accuracy and faster response times for complex problem-solving tasks.
• Excels in logical reasoning and analytical capabilities, making it ideal for reasoning-focused applications.

2. Development Time and Cost

• DeepSeek-R1 was developed significantly faster due to optimized training techniques and efficient computation.
• Its development cost is 90-95% lower than OpenAI-o1, reducing dependency on expensive GPU clusters.
• OpenAI-o1 required years of high-cost iterative training with massive datasets and computational resources.

3. Cost of Use

• Due to its high cost, OpenAI-o1 is only available to large businesses with sizable budgets.
• DeepSeek-R1 offers comparable performance at a fraction of the cost, making it affordable for startups and developers.
• R1 is open-source and available under an MIT license on Hugging Face, democratizing advanced AI.

4. Technical Efficiency

• DeepSeek-R1 uses an optimized architecture to handle complex reasoning with fewer GPUs and lower energy consumption.
• Its computation-focused design allows for “longer thinking” without resource-intensive processes.
• OpenAI-o1 relies on vast computational power for fine-tuning, which means higher costs and less scalability.

DeepSeek-R1 Benchmark Brilliance

DeepSeek-R1 has delivered impressive results across multiple benchmarks, demonstrating its competitive edge in various domains:

• Mathematics - Achieved 79.8% (Pass@1) on AIME 2024 and an outstanding 93% on MATH-500.
• Coding - Ranked in the 96.3rd percentile on Codeforces.
• General Knowledge - Scored 90.8% on MMLU and 71.5% on GPQA Diamond.
• Writing - Secured 87.6% on AlpacaEval 2.0 for question answering.

These numbers place DeepSeek-R1 in line with industry leaders, like OpenAI and Meta. In some areas, it even surpasses them, proving that open-source models can punch above their weight.

DeepSeek’s founder, Liang Wenfeng is said to be one of the few who puts right and wrong before profits and losses. He explains, 

“If the goal is to make applications, using the Llama structure for quick product deployment is reasonable. But our destination is AGI, which means we need to study new model structures to realize stronger model capability with limited resources. And beyond model structure, we’ve done extensive research in other areas, including data construction and making models more human-like — which are all reflected in the models we released.”

What Sets DeepSeek-R1 Apart

DeepSeek-R1 isn’t just another AI model—it’s a bold step forward in reasoning and problem-solving. While traditional models often rely on supervised fine-tuning (SFT) to guide their learning, DeepSeek-R1 took a completely different route. It embraced a reinforcement learning (RL)-first approach, and that showed results.

1. The RL-First Approach

Most large language models start with SFT to "teach" them the basics. DeepSeek-R1 skipped this step initially. Instead, it was trained purely through RL, allowing it to independently explore reasoning capabilities like chain-of-thought (CoT). 

Why is this revolutionary?

It proves that reasoning doesn’t need a pre-loaded foundation—it can evolve through reinforcement and feedback. By incentivizing reasoning during RL, DeepSeek-R1 developed capabilities like self-verification, reflection, and the ability to generate long and coherent CoTs.

2. Efficiency 

In the current market, efficiency is everything. DeepSeek-R1 is trained at a fraction of the cost of its competitors. Compared to OpenAI’s o1 model, it’s up to 95% more cost-effective without compromising performance.

• The RL-first strategy reduces reliance on massive datasets for supervised learning.
• For startups, researchers, and businesses, this makes advanced AI reasoning accessible without needing OpenAI-level budgets.

3. Scalability 

DeepSeek-R1 isn’t just a research experiment—it’s built to tackle real-world problems. Its design is inherently scalable. Performance improves with longer reasoning steps, reaching up to 52.5% accuracy on AIME with ~100,000 tokens.

• Whether it’s solving math problems or handling complex code, it is very adaptive.
• It enables companies to deploy powerful reasoning models without a big investment.
• Its benchmark scores place it among the best in the world, and in some cases, it even outshines proprietary models.

4. Independent Reasoning 

DeepSeek-R1’s ability to "think" independently is what sets it apart. Its RL-first approach unlocked reasoning capabilities that traditional models only achieve through extensive human-led fine-tuning.

The openness creates great opportunities. DeepSeek-R1’s ability to "think" independently is what sets it apart. Its RL-first approach unlocked reasoning capabilities that traditional models only achieve through extensive human-led fine-tuning. 

The DeepSeek-R1 enables models to learn how to become good at finding new data without requiring human trainers to add new information. It also uses the approach to problem-solving which is in line with the natural process that is almost how humans handle different challenges.

Where DeepSeek-R1 Excels

DeepSeek-R1 is emerging as a practical powerhouse. Its advanced reasoning and problem-solving capabilities are expected to transform industries and tackle real-world challenges. Here’s how it’s expected to make an impact: 

1. Education

Education technology is booming, and DeepSeek-R1 is stepping up as a game-changer.

• It can help students solve complex mathematical problems in real time, with an impressive 93% accuracy on MATH-500.
• Personalized tutoring is now smarter. DeepSeek-R1 generates detailed chain-of-thought (CoT) explanations, to enable learners to truly understand concepts instead of just memorizing answers.
• Platforms like online course providers and interactive apps can integrate the model to enhance their offerings, giving students around the world access to high-quality, AI-driven support.

2. Software Development

Coding has never been more critical, and DeepSeek-R1 shines here too.

• Ranked in the 96.3rd percentile on Codeforces, it’s capable of writing clean, efficient code and debugging complex scripts.
• Startups and enterprises alike can integrate DeepSeek-R1 to accelerate development cycles, automate repetitive tasks, and scale software teams without additional hires.

3. Business Insights with Data Analysis

Big data improves decision-making and DeepSeek-R1 makes it even simpler.

• It excels at analyzing large datasets with high accuracy, scoring 90.8% on the MMLU benchmark.
• Businesses can use DeepSeek-R1 to identify trends, optimize operations, and make data-driven decisions—all without the need for massive internal data teams.
• Retailers like Alibaba are using the model to predict customer buying patterns, while financial firms apply it to risk analysis and fraud detection.

4. Customer Experiences

DeepSeek-R1 is also showing great results in enhancing customer experiences.

• Its 71.5% accuracy on GPQA Diamond means it can provide precise, context-aware responses to customer queries, whether in e-commerce, banking, or healthcare.
• By integrating with chatbots and virtual assistants, businesses can deliver better, faster support, reducing wait times and boosting customer satisfaction.

5. Solving Complex Global Problems

DeepSeek-R1 is a tool for businesses with the potential to address many challenges.

• Climate research teams may use it to simulate environmental models and predict long-term impacts of climate change.
• Healthcare providers may rely on its reasoning abilities to analyze patient data and suggest personalized treatment plans.

DeepSeek Challenges

DeepSeek-R1 has made a big impact, but it’s not perfect. Here are some challenges it faces that could affect its growth and use.

• Businesses and developers might doubt DeepSeek’s reliability and long-term support since it’s new to the global AI market.
• DeepSeek’s low-cost strategy could struggle with highly complex or resource-heavy tasks.
• Its open-source nature under the MIT license could lead to misuse or unethical applications.
• Training on local or limited datasets might cause cultural or contextual biases, making it less effective globally.
• Being a Chinese product, it may face restrictions or scrutiny in Western markets due to political tensions.
• Unlike OpenAI, DeepSeek lacks strong partnerships and platform integrations, which could limit its appeal to developers.
• Competing with well-funded giants like OpenAI and Google could make it tough for DeepSeek to succeed outside China.

In The End

DeepSeek-R1’s emergence signals a major shift in the global AI horizon, with China solidifying its position as a leader in advanced technology. By offering a model that rivals the best in the industry—at a fraction of the cost—China is not only transforming its domestic AI ecosystem but also making a bold statement on the global stage.

The ripple effects will touch industries like education, healthcare, software development, and customer service, pushing growth.

More importantly, DeepSeek-R1 is forcing worldwide players to reconsider the way they are doing things and to employ a different strategy concerning prices and innovation. This is the AI community’s real moment—a reminder that open-source models can create new global benchmarks and make AI  accessible and scalable for everyone.

China’s DeepSeek-R1 proves that the power of innovation lies not just in performance but in accessibility and impact. The world is watching, and the game has changed.

FAQs

1. Does DeepSeek use less energy? What is DeepSeek R1 energy consumption?

DeepSeek is designed to be energy-efficient. It optimizes processing power to reduce DeepSeek R1 energy consumption compared to some AI models.

2. What is DeepSeek development cost? 

The cost depends on factors like infrastructure, training data, and computing power. It varies based on the use case and scale.

3. What is the OpenAI o1 development cost?

OpenAI hasn’t shared exact figures, but developing high-end AI models like O1 requires significant investment in computing and research.

4. What is DeepSeek-R1?

DeepSeek-R1 is an AI model developed by a Chinese company in 2023. It stands out because it’s trained using reinforcement learning with a unique chain-of-thought reasoning approach. This makes it highly efficient, self-correcting, and much cheaper to run—about 5% of the cost of traditional AI models.

Since it’s open-source, developers and businesses can modify and customize it to fit their needs. The open-source community also helps improve the model over time, making it more adaptable and responsive to user needs.

5. What are the key benchmarks where DeepSeek r1 outperforms OpenAI's o1?

DeepSeek R1 has demonstrated some impressive capabilities, particularly in the realm of reasoning and problem-solving. Here are some key benchmarks where it has shown to outperform OpenAI's o1:

6. How does DeepSeek handle complex problem-solving tasks?

DeepSeek R1 is designed with a strong focus on logical reasoning and problem-solving, which are crucial for tackling complex tasks. Here's how it approaches these challenges:   

1. Advanced Architecture

• Mixture of Experts (MoE) - DeepSeek R1 utilizes an MoE architecture. This means it has a network of specialized "expert" modules, and for any given problem, it activates only the most relevant experts.   
• Multi-Level Attention (MLA) - MLA enables the model to process information at different levels of granularity. It can capture both local details and long-range dependencies within a problem, which is essential for understanding complex relationships and contexts.   

2. Training Methodology

• Reinforcement Learning (RL) - DeepSeek R1 was initially trained using RL. This means it is learned by trial and error, generating multiple solutions to problems and receiving feedback on their correctness.   
• Self-Verification and Reflection - The model is designed to self-verify its outputs and reflect on its reasoning process.   

3. Key Capabilities

• Chain-of-Thought (CoT) Reasoning - DeepSeek R1 is adept at CoT reasoning. It can break down complex problems into smaller, more manageable steps, and then solve each step sequentially.   
• Long-Context Handling - DeepSeek R1 can handle long-context tasks, meaning it can maintain coherence and focus over extended sequences of information. This is crucial for understanding complex narratives, solving problems with multiple constraints, and maintaining context throughout a problem-solving process.

7. How does DeepSeek r1's performance compare to OpenAI's o1 in real-world applications?

DeepSeek-R1 has been effectively applied in various real-world scenarios, particularly in the automotive industry. Major Chinese electric vehicle manufacturers, including BYD, Geely, and Great Wall, have integrated DeepSeek's AI technology into their in-car systems. This integration enhances autonomous driving capabilities and overall vehicle performance, meeting the growing consumer demand for AI-enhanced, self-driving cars.

DeepSeek-R1's cost-efficiency and open-source framework make it particularly appealing for large-scale deployments and projects requiring customization.

8. How does the cost of deep seek r1 compare to OpenAI o1 for large-scale applications?

DeepSeek-R1 is significantly more cost-effective than OpenAI o1 across all pricing categories:

Insight: DeepSeek-R1 is far cheaper to run, making it ideal for large-scale applications with budget constraints.

9. How does the cost of DeepSeek r1 impact its adoption in industries?

DeepSeek-R1 is affordable and open-source, making it a great choice for businesses with tight budgets.

• SMEs can use it without expensive subscription fees, unlike OpenAI's o1.
• Industries like EVs and robotics prefer it since they can fine-tune it without licensing costs.
• Universities and researchers benefit from its free access for AI experiments.
• Large companies can run it on-premises, avoiding API fees and regulatory issues.
• Custom AI solutions in healthcare, finance, and legal sectors are easier to build without vendor restrictions.

10. How does the cost of training DeepSeek-r1 compare to other AI models

Training DeepSeek-R1 is more cost-effective than many proprietary AI models. Here’s how it compares:

• Lower Compute Costs - DeepSeek-R1 was trained using 2 trillion tokens on 192 A100 GPUs, making it cheaper than massive models like GPT-4, which likely required thousands of GPUs over a longer period.
• Open-Source Advantage - Unlike OpenAI’s models, businesses can train and fine-tune DeepSeek-R1 without high API fees or licensing costs.
• Efficiency vs. Scale - While models like LLaMA 3 or GPT-4 may have more optimizations, DeepSeek-R1 balances performance and affordability, making it viable for companies with smaller budgets.

11. How does the integration of DeepSeek r-1 impact user experience

Integrating DeepSeek-R1 can improve user experience in multiple ways.

• Faster and More Customizable Responses – Since it's open-source, businesses can fine-tune it for specific needs, improving accuracy and relevance.
• Lower Costs, More Accessibility – Without expensive licensing fees, more companies and developers can integrate AI into their apps.
• Better Privacy Control – Unlike cloud-based models (e.g., OpenAI’s GPT), DeepSeek-R1 can run on-premises, giving users more control over data security.
• Multilingual and Domain-Specific Use Cases – Businesses can train it in different languages or industry-specific jargon, leading to a better user experience in diverse fields like healthcare, finance, or education.

12. How does DeepSeek r1 handle complex problem-solving tasks

DeepSeek-R1 handles complex problem-solving tasks through its reinforcement learning (RL) approach combined with chain-of-thought reasoning. Here's how it works:

• Self-Correction – It continuously learns from past mistakes and adapts, improving accuracy over time without needing manual updates.
• Reasoning Across Multiple Steps – It can break down intricate problems into smaller parts and solve them step by step, like solving a puzzle.
• Flexibility – DeepSeek-R1 is trained to handle diverse problem types, from mathematical calculations to strategic planning, making it suitable for complex domains like finance or engineering.
• Cost-Effective Learning – Its low-cost operation allows it to continuously improve on large-scale tasks without blowing the budget.