Deepseek R1 [Tested]: Is it Actually Worth the HYPE?

Prompt Engineering
21 Jan 202519:57

TLDRIn this video, the Deepseek R1 model is tested across coding, mathematics, and reasoning tasks to evaluate its capabilities. It performs impressively, often outperforming OpenAI's O1, especially in reasoning tasks like the modified trolley and Monty Hall problems. The model demonstrates human-like reasoning, addressing tricky problems with accurate conclusions. Despite some limitations in handling certain paradoxes, Deepseek R1 is a strong contender, offering a cost-effective, open-source alternative to closed models. However, censorship concerns are addressed, with the open-source nature allowing users more control over content generation. Overall, Deepseek R1 stands out for its performance in both coding and reasoning.

Takeaways

  • ๐Ÿ˜€ DeepSeek R1 performs strongly in independent tests, showing it's one of the best open-weight models available, even surpassing O1 in some aspects.
  • ๐Ÿ˜€ The model excels in coding, mathematics, and reasoning, with a performance just behind O1 in the Live Bench test and achieving 57% on the Polyot Benchmark.
  • ๐Ÿ˜€ DeepSeek R1 is completely open source and has an API cost almost 50 times less than O1, making it more accessible.
  • ๐Ÿ˜€ In coding tests, DeepSeek R1 successfully created a web page with a button to display random jokes and change the background, as well as a web app to generate images using an external API.
  • ๐Ÿ˜€ The model's reasoning capabilities were tested with modified versions of famous paradoxes, and it was able to correctly identify the changes and provide accurate answers.
  • ๐Ÿ˜€ DeepSeek R1 demonstrated a human-like thought process in its internal monologue, which is more robust and detailed compared to other LLMs.
  • ๐Ÿ˜€ The model was able to create a detailed tutorial to visually explain the Pythagorean theorem using the Minim package, despite some initial setup issues.
  • ๐Ÿ˜€ In the misguided attention test, DeepSeek R1 was able to focus on the language of the prompt rather than relying on its training data, showing strong reasoning abilities.
  • ๐Ÿ˜€ The model's performance in editing tasks was better than O1, with about 97% task completion.
  • ๐Ÿ˜€ Despite some concerns about censorship in models from China, DeepSeek R1 is open source, allowing users to potentially run the model and get responses without the imposed guardrails.

Q & A

  • What is DeepSeek R1?

    -DeepSeek R1 is an open-source AI model that has been tested for its capabilities in coding, reasoning, and other tasks. It is considered one of the best open-weight models available, even outperforming some proprietary models in certain areas.

  • How does DeepSeek R1 compare to the O1 model in terms of performance?

    -DeepSeek R1 is just behind the O1 model in terms of coding, mathematics, and reasoning capabilities. However, it is completely open-source and its API cost is almost 50 times less than that of the O1 model.

  • What are some of the tests conducted on DeepSeek R1?

    -The tests conducted on DeepSeek R1 include coding problems, reasoning tasks, and its ability to understand tricky questions. Specific tests mentioned include creating a web page with specific features, generating an image using an external deepseek API, and explaining the Pythagorean theorem visually.

  • How does DeepSeek R1 perform in coding tasks?

    -DeepSeek R1 performs very well in coding tasks. It was able to generate code for creating a web page with a button that changes the background, shows random jokes, and displays animations. It also provided detailed documentation and structure for a web app that uses an external API to generate images.

  • What is the controversy behind DeepSeek R1?

    -The controversy behind DeepSeek R1 is related to censorship, especially since it is a model from China. Some users may bring up concerns about the model's ability to respond to certain topics or historical facts due to potential political biases.

  • How does DeepSeek R1 handle reasoning tasks?

    -DeepSeek R1 handles reasoning tasks impressively. It was able to correctly identify and reason through modified versions of famous paradoxes like the trolley problem and the Monty Hall problem, showing its ability to focus on the language of the prompt rather than relying on training data.

  • What are the limitations of DeepSeek R1 in reasoning tasks?

    -While DeepSeek R1 performs well in many reasoning tasks, it sometimes struggles with simpler problems or when the initial conditions of a problem are not clearly understood. For example, it had difficulty with a modified version of Schrรถdinger's cat paradox and a simple river crossing problem.

  • Can DeepSeek R1 generate responses for politically sensitive topics?

    -DeepSeek R1 has a guardrail that prevents it from generating responses for politically sensitive topics. However, since it is an open-source model, users can potentially modify it to generate responses for such topics, unlike closed-source models.

  • What are some of the strengths of DeepSeek R1?

    -The strengths of DeepSeek R1 include its strong performance in coding and reasoning tasks, its open-source nature which allows for customization, and its significantly lower API costs compared to proprietary models.

  • What are some of the weaknesses of DeepSeek R1?

    -The weaknesses of DeepSeek R1 include occasional struggles with simpler reasoning tasks and the presence of guardrails that limit its ability to respond to certain topics, which may be seen as a form of censorship.

Outlines

00:00

๐Ÿ˜€ Deep Seek R1: A Promising Open-Weight Model

The video script begins with an introduction to Deep Seek R1, an open-weight model that has shown impressive performance in independent tests. The speaker highlights that Deep Seek R1 is nearly on par with the O1 model by OpenAI in terms of coding, mathematics, and reasoning capabilities. The model is open source and has significantly lower API costs. The speaker then demonstrates Deep Seek R1's capabilities through various coding and reasoning tasks. For coding, the model successfully generates code for a simple web page that displays random jokes and animations. For reasoning, it tackles complex problems like the trolley problem with a twist, where the people on the track are already dead, and it correctly identifies the ethical dilemma. The model's internal thought process is noted to be very human-like, distinguishing it from other models.

05:00

๐Ÿ˜Ž Deep Seek R1: Advanced Reasoning and Problem-Solving

The second paragraph delves deeper into the reasoning capabilities of Deep Seek R1. The speaker tests the model on a modified version of the Monty Hall problem, where the initial choice of the door is revealed to have a goat. The model correctly identifies the change and concludes that switching or sticking with the remaining doors gives a 50% chance of winning the car. The model also tackles a modified Schrรถdinger's cat paradox, where the cat is already dead, but it struggles to fully recognize this detail, reverting to classical quantum mechanics calculations. The speaker also tests the model on a simpler problem of transferring a goat across a river with a wolf and cabbage, but the model provides an overly complicated solution, indicating it might be relying too much on its training data.

10:02

๐Ÿค“ Deep Seek R1: Handling Complex Scenarios and Censorship

The third paragraph continues the exploration of Deep Seek R1's reasoning abilities with more complex scenarios. The model is tested on a problem involving a farmer, a wolf, a goat, and a cabbage, where the goal is to transfer the goat across the river. The model provides a detailed but overly complicated solution, suggesting it might not be fully focusing on the specific details of the problem. The script also touches on the issue of censorship in models from China, noting that Deep Seek R1, like other models, has certain guardrails that prevent it from responding to certain topics. The speaker argues that this is not unique to Chinese models and that all models have their creators' biases. The video concludes with an invitation to try out the model and a mention of future tests on distilled versions of the model.

Mindmap

Keywords

๐Ÿ’กDeepSeek R1

DeepSeek R1 is a powerful reasoning model developed by DeepSeek. It is designed to perform complex reasoning tasks and has shown strong performance in various benchmarks, comparable to OpenAI's o1 model[^1^]. In the video, the speaker tests DeepSeek R1 on different tasks such as coding, reasoning, and editing, and is impressed by its ability to understand and execute complex instructions[^2^].

๐Ÿ’กOpenAI o1

OpenAI o1 is a well-known model developed by OpenAI, known for its strong performance in reasoning and language tasks. In the context of the video, DeepSeek R1 is compared to OpenAI o1, and it is found that DeepSeek R1 performs very well, even surpassing o1 in some aspects like coding and editing[^1^].

๐Ÿ’กCoding

Coding refers to the process of writing computer programs. In the video, DeepSeek R1 is tested on its ability to generate code for specific tasks, such as creating a web page with a button that shows random jokes and changes the background color. The model demonstrates its capability to understand and execute coding tasks effectively[^2^].

๐Ÿ’กReasoning

Reasoning is the process of drawing conclusions or making logical inferences based on available information. The video highlights DeepSeek R1's strong reasoning capabilities by testing it on various reasoning tasks, including modified versions of famous paradoxes like the trolley problem and the Monty Hall problem. The model shows its ability to understand and reason through these complex problems[^2^].

๐Ÿ’กAPI

API stands for Application Programming Interface, which is a set of rules and protocols for building and interacting with software applications. The video mentions that DeepSeek R1 has an API that costs almost 50 times less than OpenAI o1, making it a more affordable option for developers[^2^].

๐Ÿ’กBenchmark

A benchmark is a standard or point of reference against which things may be compared or assessed. In the context of the video, DeepSeek R1 is evaluated against various benchmarks such as the AER Benchmark and the Polyot Benchmark to measure its performance in coding, mathematics, and reasoning capabilities[^2^].

๐Ÿ’กMisguided Attention

Misguided Attention refers to the ability of a model to focus on the correct aspects of a problem, rather than being misled by common patterns in its training data. The video tests DeepSeek R1 on modified versions of famous problems to see if it can pick up on the small changes and reason correctly, rather than relying on its training data[^2^].

๐Ÿ’กControversy

Controversy refers to a prolonged public dispute or debate, usually involving differing opinions or points of view. The video mentions some controversy behind the DeepSeek R1 model, although it does not go into detail about the nature of the controversy[^2^].

๐Ÿ’กEditing

Editing refers to the process of making changes to text, code, or other content to improve its quality, accuracy, or readability. The video notes that DeepSeek R1 performs better than OpenAI o1 in editing tasks, with about 97% of tasks completed correctly[^2^].

๐Ÿ’กCensorship

Censorship is the suppression or prohibition of any parts of books, films, news, etc. that are considered obscene, politically unacceptable, or a threat to security. The video addresses the issue of censorship when it comes to models from China, noting that DeepSeek R1, like other models, has certain guardrails to prevent it from responding to certain topics[^2^].

Highlights

Independent tests show DeepSeek R1 performs strongly, even better than O1 in some aspects.

DeepSeek R1 is one of the best open-weight models available, with API costs 50 times less than O1.

On the AER Benchmark, DeepSeek R1 scored about 57%, just behind the O1 model.

DeepSeek R1 excels in editing tasks, achieving about 97% task completion.

The model demonstrates strong reasoning capabilities, even in tricky questions with misguided attention.

DeepSeek R1 generated accurate and functional code for a web page with a button to show random jokes and animations.

The model provided detailed documentation and a bash command to create a web app structure for generating images using an external API.

DeepSeek R1 successfully created a tutorial to visually explain the Pythagorean theorem using Minim.

In the modified trolley problem, DeepSeek R1 correctly identified the key difference that the five people are already dead.

The model provided a correct analysis and conclusion for the modified Monty Hall problem, giving a 50% probability for switching or sticking with the door.

DeepSeek R1 showed impressive reasoning in the modified Schrรถdinger's cat paradox, although it initially missed the detail that the cat is already dead.

The model provided a correct and straightforward solution for measuring exactly six liters using a 6L and 12L jug.

DeepSeek R1 demonstrated the ability to focus on the language of the prompt rather than relying on training data examples.

The model showed a human-like internal monologue in its reasoning process, unlike other LLMs.

DeepSeek R1 is an impressive model for coding and reasoning tasks, with the added benefit of being open-source.