DeepSeek R1 VS V3 Comparison🔥

Shelly updated on Feb 28, 2025 | Home > Chat PDF Tutorials with AI Solutions | min read

The release date of DeepSeek-V3 was December 26, 2024, but it was on January 20, 2025, that DeepSeek was pushed to the masses. The majority of DeepSeek users currently use the default R1 version. That is to say, few users are familiar with DeepSeek-V3. Thus, if you want to explore DeepSeek R1 VS V3, please read this post to find answers. Similarly, if you want to compare DeepSeek and ChatGPT, please go to another page.

deepseek r1 0 v3 base

What is the difference between DeepSeek-V3 and DeepSeek-R1

The DeepSeek V3 and R1 versions have varied features when it comes to handling various types of activities. The following form shows us the major features of DeepSeek-R1 and V3.

Features	DeepSeek-R1	DeepSeek-V3
🧩Base Model	DeepSeek-V3-Base	DeepSeek-V3-Base
🎯Primary Goal	Enhance reasoning capabilities via URL	Develop a general-purpose LLM with broad applicability
👉Model Type	Dense reasoning model explicitly designed for reinforcement learning (RL) challenges	Mixture-of-Experts (MoE) model with 671B total parameters (37B activated per token)
▶️Training Objective	Focused on reasoning alignment and a long Chain of Thought (CoT)	Integrated multi-token prediction and general-purpose training
⚒️Training Strategy	Two-stage fine-tuning Uses RL with GRPO for efficient policy optimization	Pipeline parallelism with FP8 mixed precision
💻Computer efficiency	Optimized for reasoning tasks with reduced GPU requirements	Optimized for reasoning tasks with reduced GPU requirements
🚩Infrastructure	Mid-sized GPU clusters for RL optimization	Trained using 2048 NVIDIA H800 GPUs with NVLink and InfiniBand

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DeekSeek R1 and V3 Comparison in Different Aspects

Some users ask how to select the DeekSeek R1 or VS. In a word, your project's requirements determine the choice between DeepSeek R1 and V3. Let's review the differences between DeepSeek-R1 and DeepSeek-V3 from the following aspects. However, if your DeekSeek becomes busy, you can go to another page to find solutions.

1️⃣R1 Excels at Problem Solving and Research

In short, R1 excels in complicated problem-solving, research, and maintaining context across prolonged encounters.

Its capacity to keep context over extended interactions makes it ideal for jobs such as coding difficulties, mathematical proofs, and scientific inquiry.

If you're working on a research project that requires analyzing massive datasets, R1 can assist you in identifying patterns, developing hypotheses, and even proposing fresh methods to your research concerns. Its step-by-step reasoning can reveal crucial insights that a more general-purpose model, such as V3, may overlook.

deepseek r1 v3 workflow

2️⃣V3 is Good at Writing, Content Creation, and Simple Coding

DeepSeek V3 is the go-to solution for jobs such as article writing, creative content generation, and assistance with basic coding. Its Mixture-of-Experts architecture enables it to generate human-like prose rapidly and effectively.

Assume you are working on a content marketing campaign. V3 can help you develop ideas, produce drafts, and recommend ways to improve your existing content. Its quickness and fluency make it an excellent partner for these types of tasks.

About DeepSeek-R1

DeepSeek-R1 is a Mixture-of-Experts (MoE) model with 671 billion parameters and 37 billion activated parameters per token, trained using large-scale reinforcement learning focusing on reasoning capabilities.

It includes two RL stages for identifying better reasoning patterns and aligning with human preferences and two SFT stages for seeding reasoning and non-reasoning abilities. The model performs similarly to OpenAI-o1 on math, programming, and reasoning tasks.

About DeepSeek-V3

What is DeepSeek-V3? DeepSeek-V3 is an open-source Mixture-of-Experts (MoE) model with 671 billion parameters and 37 billion activated parameters per token.

It has revolutionary load balancing and multi-token prediction capabilities and was trained on 14.8 trillion tokens. The model exhibits cutting-edge performance across benchmarks while keeping training expenses of approximately 2.788 million H800 GPU hours low. It has reasoning skills derived from DeepSeek-R1 and has a 128K context window.

Conclusion

After reading the above content, you may have a clearer understanding of the DeepSeek r1 vs v3. In summary, R1 outperforms in complicated problem-solving scenarios involving arithmetic, logic, and coding, while V3 thrives at producing fluent, human-like prose for creative writing and content development. You can select a suitable model based on your needs.

FAQs about DeepSeek

Given that this model is drawing increasing users, this section also lists some relevant topics.

1. When did DeepSeek-V3 come out?

DeepSeek-V3, which will be released in December 2024, employs a mixture-of-experts architecture that can handle a wide range of jobs. The model contains 671 billion parameters and a context length of 128,000. DeepSeek-R1.

2. Is DeepSeek-R1 better than OpenAI o1?

Both models perform at or near human-expert levels, with DeepSeek-R1 scoring slightly higher (97.3% on MATH-500) than OpenAI o1 (96.4%).

3. What's the price comparison between DeepSeek R1 and V3?

Regarding the input cost of processing tokens in your prompts, DeepSeek R1 is $0.55 per million tokens, while the DeepSeek V3 is $0.14 per million tokens.
When it comes to the output cost for tokens generated by the model, DeepSeek R1 is $2.19 per million tokens, while the DeepSeek V3 is $0.28 per million tokens.

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About the Author

Shelly

"I hope my articles can help solve your technical problems. If you are interested in other articles, you can check them at the bottom of this page. Meanwhile, she likes sharing some AI solutions about PDFs to help you enjoy a convenient and digital life ."

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