DeepSeek-R1-Zero or DeepSeek R1 is a Chinese artificial intelligence model that has shaken Silicon Valley since its release. Yes, DeepSeek R1 has emerged as a key player for complicated reasoning, mainly for non-subscribing users.
In this article, we are going to explore everything about DeepSeek R1, its pricing, features and employed models.
What is DeepSeek R1?
DeepSeek R1 is an AI model that follows CoT prompting, and RL trains its data models using 671B parameters. This revolutionary open-source AI model was designed by a Chinese AI firm, Shēndù Qiúsuǒ or 深度求索 within a budget of 6 million USD only. In contrast to conventional AI models, DeepSeek R1 emphasizes real-time problem-solving, mathematical troubleshooting, and logical reasoning. Similarly, where traditional US-based models prefer text understanding and generation, DeepSeek R1 prioritizes improved reasoning and elucidation.
Probably, the majority of you think open-source nature is the main advantage of DeepSeek. But in reality, it lets the dev teams deploy, amend, and discover the model within defined boundaries. Minimal operational cost is another advantage of this model. Compared to OpenAI o1, DeepSeek R1 only bills you 2%. Released on January 20, 2025, this AI model surely created a buzz worldwide.
DeepSeek R1 Price
DeepSeek-Chat charges you $0.014 for 1 million tokens input and $0.28 for 1 million tokens output. On the other hand, users just have to pay $0.55 for 1 million tokens input and $2.19 for 1 million tokens output when it comes to DeepSeek-Reasoner.
Comparison Between Prices of OpenAI O1 and DeepSeek R1
| AI Model | Output Price (per million tokens) | Input Price (per million tokens) |
| OpenAI O1 | $60.00 | $15.00 |
| DeepSeek R1 | $2.19 | $0.55 |
How DeepSeek R1 Works?
Let’s explain this AI model through its key highlights:
Chain-of-Thought (CoT) Prompting
DeepSeek-r1 utilizes CoT or Chain-of-Thought prompt engineering approach rather than Standard Prompting. This technique doesn’t only resemble human reasoning but also optimizes the functioning of language models. CoT deploys LLMs’ expertise to get more logical reasoning, fine-tuning, and model debugging.
In other words, this prompt engineering technique breaks down its reasoning stage by stage. Thus, if you spot any error, you can easily identify where it is and re-engage the model swiftly.
Reinforcement Learning
DeepSeek follows an RL machine learning approach to yield the best outcomes. Reinforcement Learning is basically an ML practice where data is trained on its own but on programmed answers. Hereof, RL acts in accordance with Policy and Reward Function to learn its own.
In layman’s terms, DeepSeek r1 trains its models using RL to refine its Policy to maximize the results. Over time, it learns which Policy streamlines the Reward Function. Besides, you can direct this model to modify its Policy for more precise responses.
However, DeepSeek r1 uses clipping to evaluate how much change is crucial to retain the model’s balance.
Model Distillation
The transition of insights from an extensive pre-configured model to a small-scale model refers to distillation. In this machine learning technique, a small-scale model is called a student, and a large model is known as a teacher.
It means DeepSeek r1 replaces large LLMs that use more GPU, memory and further resources with small LLMs. So, people with limited resources can access the same accuracy with the student model that the teacher model confers. Additionally, researchers found that smaller distilled student models of DeepSeek r1 outperform many teacher models like GPT-4o-0513 and OpenAI-o1-mini.
Is DeepSeek R1 Completely Free?
DeepSeek R1 is free-to-use and open-source for personal use. However, you can also employ this economical AI solution for $2.19 for 1 million output tokens and $0.55 for 1 million input tokens.
Final Words
DeepSeek R1 has emerged as a compelling rival that has given shocks to Silicon Valley on the first day of its release. It surely leverages developers, businesses, AI agents, and newbies. Nonetheless, it remains a new AI model with numerous advantages and drawbacks we must explore.
