In January 2025, DeepSeek R1 reignited AI enthusiasts’ interest in China’s large language models (LLMs). It has attracted many programmers and researchers to work on it within a short period. At the same time, another AI model, Gemini 2.0 - an advanced generative AI model developed by Google, also specializes in handling complex reasoning tasks.
Then you may wonder which one to choose between DeepSeek R1 and Gemini 2.0. In this post, we’re going to explore DeepSeek R1 vs Gemini 2.0. We’ll focus on their basic performance, strength, challenges, and pricing. Hopefully, this will give you a clue when investing in a favorable AI model.
What Is DeepSeek?
DeepSeek is an innovative AI company launched in May 2023. It targets the development of advanced Large Language Models (LLMs). Then in January 2025, it introduced DeepSeek R1 - a powerful AI model with 671 billion parameters. The training cost for DeepSeek R1 is around $5.6 million, significantly lower than the cost of training GPT 4.
Due to its advanced reasoning capabilities, many users apply it in coding. For example, some programmers use it to debug complex software and generate codes. After its launch, the model generated much attention, reaching the top place in the Apple App Store within five days and achieving more than 3 million app downloads in one week.
What Is Gemini?
In May 2024, Google released Gemini. And then at the end of 2024, Google introduced the latest version - Gemini 2.0 Flash and Gemini 2.0 Pro. Based on a maximum of 2 million token context window, they can handle large volumes of text and data. It supports multimodal input, including audio, images, videos, and text, and can generate text, images, and audio.
As one of Google’s family members, Gemini 2.0 supports using native tools such as Google Search and code execution. Also, this model is gradually integrated into Google’s products and services such as Google Search and Google Workspace. This has greatly enhanced its versatility.
DeepSeek R1 Vs Gemini 2.0 - A Comparative Analysis
As there is significant interest and debate among researchers and practitioners, we decided to make a detailed comparison of DeepSeek R1 and Gemini 2.0. This comparison will mainly focus on their performance, strengths, weaknesses, and price list. By highlighting the use cases, you’ll know how to make the best use of each tool.
User Experience
- DeepSeek R1: Users may find its responses more appealing and informative. It includes links in its search results This is helpful for users who seek to verify the content.
- Gemini 2.0: Its descriptions are more vivid and interesting. It allows users to enjoy a better reading experience. Also, it enables users to get the sources they need.
Architecture and Design
- DeepSeek R1: Based on a dense mixture-of-experts (MoE) architecture, it is developed as an open-source model that has 671 billion parameters in total.
- Gemini 2.0: It applies a hybrid transformer architecture, which is a proprietary model developed by Google.
Context Window
The context window refers to the amount of text or we say “tokens” that an AI model can process at once. A larger context window indicates the model can handle longer inputs.
- DeepSeek R1: While the exact context window size isn’t publicly disclosed, it is estimated to support large context windows, up to 128,000 tokens.
- Gemini 2.0: The capacity of its context window is up to 2 million tokens.
Multimodal Inputs and Outputs
Multimodal inputs and outputs indicate how AI models can process and generate information across various types of data, such as text, images, audio, and videos.
- DeepSeek R1: Technically, DeepSeek R1 isn't multimodal. It mainly focuses on text-based tasks and excels in natural language processing(NLP), knowledge synthesis, and low-latency responses.
- Gemini 2.0: It supports a wide range of input types, including audio, images, videos, and text. It can generate text, images (later), and audio (coming soon) as outputs.
Speed and Efficiency
- DeepSeek R1: It isn’t as fast as Gemini 2.0 Flash, but the difference is not that evident in most cases.
- Gemini 2.0: It’s optimized for speed and efficiency.
Coding Performance
This parameter indicates whether the AI model excels in coding tasks such as generating codes, debugging, and optimizing across different programming languages.
- DeepSeek R1: It performs better in the algorithm. It has achieved an 87% success rate on LeetCode Hard problems compared to Gemini 2.0 Flash’s 82%. Also, DeepSeek R1 excels in debugging, with a 90% accuracy rate.
- Gemini 2.0: It shows stronger performance in framework-specific patterns, with a 91% success rate on Next.js optimization tasks compared to DeepSeek R1’s 62%.
Mathematical Performance
AI models are trained to handle mathematical tasks. This ranges from basic arithmetic to advanced statistical analysis. They can solve equations, perform symbolic computations, and even generate solutions to fix complex problems. On the other hand,
- DeepSeek R1: It excels in complex logical reasoning, including advanced calculus, linear algebra, and symbolic mathematics. Also, it can offer step-by-step solutions to problems.
- Gemini 2.0: As it focuses on speed and efficiency, it is ideal for quick calculations and straightforward mathematical tasks. It performs well in basic arithmetic, algebra, and statistics. It can generate rapid and accurate answers.
Data Privacy and Security
- DeepSeek R1: It may collect user data and store it in China, which may raise concerns among users.
- Gemini 2.0: As one of Google’s products, it follows Google’s data privacy policies. Thus, users don’t need to worry about data security.
Challenges and Limitations
- DeepSeek R1: It may take a long time to generate answers to complex questions. Also, It could struggle with processing niche or specialized mathematical problems. Moreover, some users may have concerns about information and data security.
- Gemini 2.0: While Gemini 2.0 excels in image analysis and web searches, it may be weak at analyzing some types of documents such as PDF files. Also, although it can work on coding tasks, sometimes it may fail to generate effective codes. Additionally, it may have difficulty in handling complex, multi-step reasoning tasks that need deep analysis.
API Pricing
- DeepSeek R1: $0.55 per 1m input tokens and $2.19 per 1m output tokens
- Gemini 2.0: $0.10 per 1m tokens and $0.40 per 1m tokens (for Gemini 2.0 Flash)
How to Enjoy DeepSeek and Gemini for Free?
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Conclusion
In conclusion, DeepSeek R1 excels in advanced mathematical reasoning, resolving logical problems, and addressing complex problems step by step. On the other hand, Gemini 2.0 excels in quick and efficient responses such as quick calculations, basic arithmetic, algebra, and statistical tasks. Both tools have their strengths and limitations. Choosing the one based on your needs and then you can make the best use of them.