The continuous advancement and development of AI technology has brought tremendous progress in areas such as data analytics and machine learning. Among these innovative AI technologies, AI models have become a commonly utilized tool for businesses and developers. And they have energized the market and given a new look to the tech sector.
Among these AI models, DeepSeek R1 vs V3 stood out and beat ChatGPT to become the No. 1 App Store download in a short time. In this post, we'll give you a detailed overview of DeepSeek R1 vs V3, including their definitions, features, pricing, and more so that you can decide which model is better suited to your needs and make an informed decision.
Part 1. Core positioning of DeepSeek R1 vs V3?
To understand the models DeepSeek R1 and DeepSeek V3, we have to mention the company DeepSeek, which is a tech company specializing in the development of AI smart models. The release of DeepSeek R1 and DeepSeek V3 attracted the attention of everyone in the global tech and AI field because of their powerful performance as well as their lower price.
The release of DeepSeek R1 and DeepSeek V3 energized the tech market, refreshed the standards of the industry, and also gave a huge shock to the tech stock market, especially to tech companies led by NVIDIA, sending their stock prices plummeting. But DeepSeek is not yet listed, so there are no DeepSeek stock on the market for now.
1. What is DeepSeek R1?
DeepSeek R1 is a powerful AI reasoning model capable of solving specialized problems and complex tasks. Especially in logical areas such as math and code.
DeepSeek R1 was preceded by the DeepSeek R1-Zero, which added cold-start data to large-scale reinforcement learning to easily address the repetition, poor readability, and confusing language that occurred in the training and use of the DeepSeek R1-Zero model.
2. What is DeepSeek V3?
DeepSeek V3 is a general LLM that focuses more on scale, and is capable of handling a wide range of tasks as a general purpose tool. Based on the MLA and MoE architectures used in the DeepSeek V2, it also combines an auxiliary lossless strategy for load balancing and multi-token prediction for training objectives.
DeepSeek V3 has a total of 671B parameters and 37B activation. The training of DeepSeek V3 consists of a total of 2 parts: Pre-training and Post-training. Because of the advanced MoE architecture, it can only select the appropriate domain and improve the computational efficiency, laying a good foundation in the Pre-training and making DeepSeek V3 smarter and smarter in the Post-training.
DeepSeek V3 is also the default model used in DeepSeek Chat, but you can also choose the DeepSeek R1 model for deep thinking. As DeepSeek advertises, "deep thinking" and "into the unknown". But when you use DeepSeek R1, it doesn't give you a solution right away, it gives you a more customized answer after reasoning and thinking.
Part 2. DeepSeek R1 vs V3: Major Differences
While there are differences between these 2 DeepSeek AI models, they are both open source, and you can use both powerful models for free in Online DeepSeek Chat for cutting-edge AI solutions, and you can likewise get the API keys for both models in the official DeepSeek Technical Reports and DeepSeek API Docs and integrate them into your own projects.
Next, we will explain the differences with DeepSeek R1 vs V3 from different perspectives.
1. Market Positioning and Competition
DeepSeek R1 is positioned as a direct competitor to OpenAI o1. and DeepSeek V3 is a direct competitor to GPT-4o.
2. Speed and Efficiency
As we mentioned above, DeepSeek R1 usually takes longer to think and respond because of the focus on delivering more in-depth answers. DeepSeek V3, on the other hand, benefits from its MoE architecture, which allows it to respond to your commands more quickly. This is one of the reasons why DeepSeek V3 is used as the default model for DeepSeek Chat.
3. Best Application Scenarios
| DeepSeek R1 | DeepSeek V3 | |
|---|---|---|
| Instructions | Short and clear | Long-form content |
| Speed | Slower | Real-time |
| Domain | Specialized verticals such as medical or finance | High-precision output such as creative writing |
| Budget | Priority for reasoning cost | Priority for scalability across different use cases |
4. Pricing
Compared to the development and training costs of models such as ChatGPT, DeepSeek's low cost makes it more affordable. DeepSeek's low cost is an important item in refreshing the industry standard.
The cost of DeepSeek R1 is higher than that of DeepSeek V3, with the main cost increase being the addition of reinforcement learning to the V3 model in R1.
| Price Type | DeepSeek R1 | DeepSeek V3 |
|---|---|---|
| Input | $0.55 per million tokens | $0.14 per million tokens |
| Output | $2.19 per million tokens | $0.28 per million tokens |
If you want to fully learn DeepSeek's pricing and cost implications, you can refer to DeepSeek's official technical report.
Part 3. DeepSeek R1 vs V3: Intuitive comparison table
The table below will show you the differences between DeepSeek R1 and DeepSeek V3 in a more visual way, helping you to quickly navigate and choose the model that best suits your needs.
| Feature | DeepSeek R1 | DeepSeek V3 |
|---|---|---|
| Language Comprehension | Higher in specialized areas | Slightly lower in niche tasks, but more balanced |
| Architecture | Reinforcement Learning (RL) optimized | Mixture-of-Experts (MoE) |
| Reasoning Ability | Advanced | Good |
| Real Applications | Specialized verticals such as medical or finance | High-precision output such as creative writing |
| Price | - | Lower |
| Customization | Limited | More flexible |
| Input | 128K tokens | 128K tokens |
| Output | 32K tokens | 8K tokens |
| # Total Params | 671B | 671B |
| # Activated Params | 37B | - |
| Open Source | Yes | Yes |
Part 4. FAQs about DeepSeek R1 and DeepSeek V3
Question 1. Which model is better suited for coding tasks, DeepSeek R1 vs V3?
DeepSeek R1 performs better in coding tasks where accuracy is more important, such as debugging code. DeepSeek V3 is more generalized.
Question 2. Which model is more flexible in terms of access and customization?
DeepSeek R1 vs V3 in terms of customization options. The DeepSeek V3 with more and more flexible tweaks.
Question 3. How to run DeepSeek V3 or DeepSeek R1 locally on PC?
The DeepSeek API Docs detail how to deploy various DeepSeek models locally using different approaches, including DeepSeek R1 and DeepSeek V3.
Part 5. Conclusion
Overall, R1 uses a hybrid architecture to accelerate reasoning capability and speed. It is more specialized in vertical fields, especially in finance, medical and legal industries, and R1 prioritizes speed and specialization.
DeepSeek V3 adopts MoE architecture, which is optimized for massively parallel processing, and its extensive training makes it capable of a series of creative and general tasks, such as text generation, and V3 puts more emphasis on versatility.
According to the comparison and introduction of DeepSeek R1 vs V3 in this article, I believe you can find a more suitable DeepSeek model for yourself and make a choice more quickly.
