DeepSeek-R1 Model Introduction

    DeepSeek R1: A new reasoning model that makes AI decisions more transparent and reliable.DeepSeek-R1 is a large inference model released by DeepSeek, which is comparable to OpenAI's o1 model. Officials claim that its performance on difficult inference problems is very competitive compared to the o1 model. It is designed for complex inference tasks and enhances performance in mathematics, code generation, and logical reasoning. In addition, the model's thinking process is open to the public. DeepSeek-R1 official review PDF:https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf

   DeepSeek-R1What makes it different is its special use of reinforcement learning. To train R1, DeepSeek built on the foundation laid by V3, leveraging its broad capabilities and large parameter space. They performed reinforcement learning by allowing the model to generate a variety of solutions to problem-solving scenarios. A rule-based reward system was then used to evaluate the correctness of answers and reasoning steps. This reinforcement learning approach encouraged the model to refine its reasoning capabilities over time, effectively learning to autonomously explore and develop reasoning paths.

   In terms of performance, R1 performed well in technical fields, especially advanced mathematics and programming competitions, surpassing competitors such as OpenAI o1-preview and Claude 3.5 Sonner. However, it is relatively weak in general knowledge and logical reasoning, for example, GPQA Diamond and Zebra Logic scored lower than OpenAI's similar models.

What is DeepSeek-V3?

   DeepSeek-V3 It is the default model used when interacting with DeepSeek.It is a versatile Large Language Model (LLM) that stands out as a general tool that can handle a variety of tasks.One difference between V3 and R1 is that when chatting with R1, we don’t get responses and answers immediately. The model first uses thought chain reasoning to think about the problem. Only when it finishes thinking, it starts to output the answer.

Differences between V3 and R1?

  *reasoning ability: V3 has no reasoning ability; while R1 is strong in the ability to solve complex problems, logic and distributed reasoning tasks.

  *Speed and efficiency: V3 responds more efficiently, faster, and in real time; R1 takes longer to respond because it focuses more on providing deeper and more structured answers.

  *Memory and context handling: Both can handle up to 64,000 input tokens, but R1 is particularly good at maintaining logic and context over longer interactions.

  *Pricing Differences: V3 is cheaper than R1, and it is important to weigh the costs associated with the model and the budget for our specific needs.

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