AI Ecosystem’s Latest Trends: DeepSeek R1 vs. Gemini 2.0 Comparative Analysis

DeepSeek R1 vs. Gemini 2.0: Cost-Performance Comparison

1. Key Aspects of DeepSeek R1

   • DeepSeek R1 is a cutting-edge AI model that emphasizes robust performance and cost reduction. This model has innovatively optimized data and the learning process, particularly through reinforcement learning.
   • DeepSeek R1 Development Stages: It is designed with an evolved structure based on its predecessor, DeepSeek V3.
   • Cost Calculation Controversy: There is a debate regarding the learning and running costs of DeepSeek R1. The announced cost of $5.57 million only includes the final training process, and there may be misunderstandings because it excludes costs of previous simulations, data generation, and trial and error.

2. Features of Gemini 2.0

   • Gemini 2.0, introduced by Google DeepMind, significantly improved speed and performance through the Flash Thinking method.
   • Price Competitiveness: It offers APIs at a lower cost than DeepSeek R1. For example, the cost per million tokens is lower, and the processing speed is also very fast.
   • Open Source Accessibility: It is provided in a structure that is easily accessible to developers, creating an ecosystem where even small teams can utilize cutting-edge AI models.

Structure of the AI Ecosystem and Misconceptions in the Media

1. Differences in Cost Calculation

   • Many media outlets tend to overinterpret the announced cost reductions of DeepSeek R1, reporting it as an “overwhelming cost advantage” over AI models from big tech companies.
   • The cost structure of DeepSeek R1 needs to be viewed comprehensively, including GPU utilization, data generation, and the reinforcement learning process.

2. Reinforcement Learning-Based Model vs. Traditional Learning Model

   • Reinforcement learning-based models have a structure of generating data and self-learning, making initial settings complex and expensive.
   • In contrast, traditional Supervised Models use data generated by humans for simple learning but require large amounts of data.

3. Perspectives on the Media’s Excessive Hype

• While DeepSeek’s cost reduction announcement is innovative, it’s unreasonable to view it as making existing big tech models “outdated.”
• When comparing models, it is necessary to comprehensively evaluate not only cost and performance but also data scale, GPU utilization, and the ecosystem.

MOE (Mixture of Experts) Architecture and Model Lightweighting

1. What is MOE Architecture?

   • MOE architecture is a structure where different expert models within an AI model are specialized to work on specific tasks. For example, there are separate expert models responsible for solving math problems, generating language, and writing code.
   • Advantages: Because it activates parameters suitable only for specific tasks, it theoretically reduces computing costs.
   • Disadvantages: Because metadata of the entire model is required for the selection process, large-scale GPU clusters or data centers are necessary.

2. MOE and Cost Reduction

   • DeepSeek models claim to have reduced costs while maintaining high performance by applying the MOE architecture.
   • However, achieving these reductions requires a smarter “base model” and large-scale data center infrastructure.

Open Source and Changes in the Industry

1. Open Source Competition

   • Meta and DeepSeek have chosen a strategy of publicly releasing some parts of their models as open source and evolving alongside the developer community.
   • Developers are likely to create customized lightweight models based on these.

2. Evolution of the Ecosystem

   • As lightweighting technologies utilizing open models develop, the barrier to AI utilization is gradually lowering.
   • As individual developers or startups are more likely to build or utilize their own AI models, the competitive structure is becoming more intense.

Conclusion: Innovation Continues

1. Technological Trends

   • DeepSeek R1 and Gemini 2.0 symbolize the pace of AI technology advancement and the potential for market changes.
   • Innovations such as cost reduction, reinforcement learning, and MOE structures will continue to be major central axes in the AI ecosystem.

2. Continuous Expansion of the Ecosystem

   • With the emergence of cheaper yet high-performing AI models, the use of AI is expected to continue to expand.
   • In particular, the open-source ecosystem is proactively developing, driving a positive trend throughout the industry.

[Related Articles at Next-Korea.com]

1. The Future and Market Prospects of AI Open Source
2. Use Cases of Reinforcement Learning (RL) Based Models

*Source : [안될공학 – IT 테크 신기술] 딥씨크 R1 충격적 저비용? 실제 밝힌 내용은 이렇습니다… 그리고 구글은 더 싼 모델 냈어요