DeepSeek-R1
Release time: 2025/01/20
Features: Designed for code generation and math problems, it is extremely fast and accurate, making it ideal for scenarios that require rapid implementation of technical requirements. Benchmark OpenAI o1, which is now the hottest direction in the field of AI large models, represents the most cutting-edge research reserves.
Applicable people: programmers, developers, science and engineering students.
Application scenarios: Writing code, solving mathematical problems, and optimizing algorithms.
Reference:The hyperlink login is visible.
DeepSeek-V3
Post time: 2024/12/26
Features: Suitable for general knowledge quizzes, text creation and learning aids, with wide coverage but slightly weaker professionalism. The benchmark is GPT4o, which represents the most fundamental general intelligence of large models.
Applicable people: students, creators, daily knowledge inquirs.
Application scenarios: Write articles, find materials, learn new concepts.
Reference:The hyperlink login is visible.
summary
| | | | Using traditional training methods, it mainly relies on large amounts of data to learn. | Employ a reinforcement learning approach that allows the model to improve its capabilities through continuous experimentation and improvement. | | Perform well on general tasks but may be limited in issues that require deep thinking. | Excel in tasks that require deep thinking, such as math, code, and logical reasoning. | | There may be some limitations. | Completely open source, anyone can use and improve it for free. | | Performed well on some tasks. | In mathematical tests, the accuracy rate reached 77.5%, which is comparable to other leading models. | | The generated content is usually easy to read and understand. | Early versions may have mixed multiple languages, but later with improvements, the generated content became more readable. |
- Training method:Imagine you're learning to ride a bike. DeepSeek-V3 is like learning by reading a lot of books on how to ride a bike, while DeepSeek-R1 is learning to ride a bike by constantly practicing, falling and getting up.
- Reasoning ability:If you are given a complex math problem, DeepSeek-R1 is like a classmate who is good at deep thinking, able to derive answers step by step, while DeepSeek-V3 may be better at simple calculation problems.
- Open Source:DeepSeek-R1 is like a public cookbook that anyone can view, use, and improve according to their taste, while DeepSeek-V3's recipes may only be visible to some people.
- Performance:In a math exam, DeepSeek-R1 scored 77.5 points, which is on par with other top students.
- Readability:Initially, DeepSeek-R1 may have written articles in a mix of multiple languages, but it has been improved so that now it writes articles that are easier to read and understand.
The difference between Deepseek V3 and Deepseek R1 is that an R1 will conduct self-reasoning and reflection and give you an answer after a long thought, while Deepseek V3 can give you an answer quickly and will not think for a long time. At present, most experiments show that the output of the model after long thinking is better, but it is also more time-consuming, and sometimes excessive thinking is also performed. |
Posted on 2025-2-5 08:46:09
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Landlord|
Posted on 2025-2-5 09:47:07
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