OpenAI-o1 on Cursor | First Impressions and Tests vs Claude 3.5
TLDRIn this video, the creator shares their first impressions and tests of OpenAI's new reasoning model, o1, using the coding platform Cursor. They compare o1's performance in building a space game and a Bitcoin trading simulation system with that of Claude 3.5 and GPT-4. The tests reveal that while o1 shows promise in complex reasoning, it is slower and more expensive than Claude 3.5 for certain tasks. The creator expresses excitement for the potential of o1 but concludes that Claude 3.5 provides faster and more efficient solutions for the given tests.
Takeaways
- 😀 OpenAI has released a new reasoning model called 'o1', designed for complex tasks and problem-solving.
- 🔍 The o1 model is part of a series of AI models trained with reinforcement learning for complex reasoning, and it uses 'reasoning tokens' to think before responding.
- 💡 The o1 model produces a long 'Chain of Thought' before generating a response, which is then completed with 'completion tokens'.
- 📊 OpenAI introduced two versions: 'o1 mini' for coding, math, and science tasks, and 'o1 preview' for broader general knowledge and reasoning.
- 💰 The 'o1 preview' is priced at $15 per million tokens, while the 'o1 mini' is five times cheaper at $3 per million tokens.
- 🚀 The video demonstrates setting up and testing the o1 model on Cursor, a code-building platform, comparing it with Claude 3.5 and GPT-4.
- 🕹️ A game development test was conducted, where the o1 model was tasked with creating a space game using Next.js, but the results were slower and less effective compared to Claude 3.5.
- 🛠️ Debugging and code correction were attempted with the o1 model, showing potential for improvement in handling complex coding tasks.
- 📈 The video also tests building a Bitcoin trading simulation system with the o1 model, but the results were not as successful as with Claude 3.5, indicating the model may need more fine-tuning for such tasks.
- 🔎 The host expresses excitement about the potential of the o1 model but concludes that it's still early to determine its best use cases and that further exploration is needed.
Q & A
What is the main topic of the video?
-The main topic of the video is the first impression and testing of OpenAI's new reasoning model, o1, using the development environment Cursor, and comparing its performance with Claude 3.5.
What is the purpose of the o1 model according to the video?
-The o1 model is designed to spend more time thinking before responding, reason through complex tasks, and solve harder problems in science, coding, and other areas compared to previous models.
What are the key features of the o1 model mentioned in the video?
-Key features of the o1 model include the ability to think before answering, produce long internal chains of thought, and use reasoning tokens to break down understanding of prompts and generate responses.
How does the video compare o1 with other models like GPT-40 and Claude 3.5?
-The video compares o1 with GPT-40 and Claude 3.5 by testing them on tasks such as building a game and setting up an API system. It evaluates their performance, speed, and accuracy.
What are the limitations of the o1 model discussed in the video?
-The video mentions that o1 has limitations such as fixed temperatures, inability to stream, and certain functionalities like system messages being unavailable. Additionally, access to the API requires a certain tier and payment history.
What are the different versions of the o1 model presented in the video?
-The video presents two versions of the o1 model: o1 mini, which is faster and cheaper, and o1 preview, which is designed for reasoning about hard problems using broad general knowledge.
How does the video demonstrate the setup of the o1 model with Cursor?
-The video demonstrates the setup of the o1 model with Cursor by showing the process of selecting the model within the settings, adding the model, and using it in the chat and composer sections of Cursor.
What is the pricing for using the o1 model as mentioned in the video?
-The video states that the o1 preview costs $15 per million tokens on input and $60 on output, while the o1 mini is five times cheaper at $3 per million tokens on input and output.
What is the API access requirement mentioned in the video for using the o1 model?
-To access the o1 model API, the video mentions that developers need to be on API usage tier five, which requires a minimum of $1,000 spent over 30 days since the first successful payment, and there is a rate limit of 20 requests per minute.
What are the outcomes of the tests comparing o1 with Claude 3.5 in the video?
-The video shows that Claude 3.5 performed better in the tested scenarios, being faster and more effective in setting up a game and an API system compared to the o1 model, which was slower and had some issues in execution.
Outlines
🤖 Introduction to OpenAI's New Reasoning Model
The speaker expresses excitement about OpenAI's newly released reasoning model called '01'. They plan to conduct a first impression test using the 'cursor' platform to build tests and debug with the 01 model, comparing its performance with CLA 3.5 and GPT-4. Before diving into testing, they review the available information on the 01 model, which is designed to spend more time thinking before responding and to excel in complex reasoning tasks in science, coding, and more. The model uses 'reasoning tokens' to break down its understanding of prompts and generate responses. The speaker also discusses the limitations of the model, such as fixed temperatures and the inability to stream or use system messages. They mention two versions of the 01 model: '01 mini' for coding, math, and science tasks, and '01 preview' for broader reasoning with general knowledge. Pricing for the models is also briefly touched upon, with the 01 preview costing $15 per million tokens and the 01 mini being five times cheaper.
💻 Setting Up and Testing the 01 Model with Cursor
The speaker proceeds to set up and test the 01 model using the 'cursor' platform. They demonstrate how to select the 01 models within the cursor settings and how to use them in the chat and composer sections. They then构思 a test by creating a simple game and comparing the results from different models. The game involves controlling a spaceship, firing bullets, and destroying asteroids, with the objective of surviving as long as possible. The speaker uses a Next.js setup for the game and provides a prompt for the AI to follow. They begin by testing with CLA 3.5, implementing the game, and running into some compilation issues. After fixing these, they attempt to run the game but encounter further problems, such as a lack of score updating and crashing. Despite these issues, the controls are deemed responsive, and the speaker expresses satisfaction with the asset implementation.
🚀 Testing 01 Mini Model on Game Development
The speaker switches to testing the '01 mini' model on the spaceship game development. They follow a similar process as with CLA 3.5, using the same prompt and running the test. The response from the 01 mini model is slower, and upon implementing the provided code, they find that the game does not function correctly. There are issues with score updating, sound, and player movement. The speaker attempts to debug and fix these issues but ultimately concludes that the 01 mini model did not perform well for this task, compared to CLA 3.5, which was faster and more efficient.
📊 Experimenting with 01 Preview Model for Complex Tasks
The speaker moves on to test the '01 preview' model, which is designed for more complex reasoning tasks. They set up a timer to measure the response time and request the folder structure for the game project. After implementing the code from the 01 preview model, they encounter multiple errors and issues, including undefined variables and missing assets. Despite several attempts to fix these issues, the game still does not function as intended, with no bullet firing or collision detection. The speaker expresses disappointment with the 01 preview model's performance for this task and concludes that CLA 3.5 provided a better initial solution.
🔍 Building a Bitcoin Trading Simulation System
The speaker outlines a new test involving the construction of a two-part system. The first part involves building an API endpoint that fetches Bitcoin prices from the CoinGecko API, with the ability to extract older prices for up to two months. The second part of the system connects to this API and uses the data to test different trading algorithms through backtesting simulations. The goal is to generate three distinct strategies to determine which performs best in making money on Bitcoin. The speaker begins by using CLA 3.5 to build this system, following a clear set of instructions and successfully setting up Docker and other components. They run the simulation and analyze the results, which show varying levels of profit and trade numbers for different strategies.
📉 Testing 01 Preview Model on Bitcoin Trading System
The speaker then attempts to build the same Bitcoin trading system using the 01 preview model. They follow a similar process, requesting a folder structure and implementing the code provided by the model. However, they encounter issues with the system crashing and not fetching prices as expected. After making adjustments and running the system multiple times, they only manage to get partial results, with only one strategy showing a profit while the others do not perform as expected. The speaker concludes that the 01 preview model did not provide a satisfactory solution for this task, unlike CLA 3.5, which worked out of the box. They express a desire to further explore and understand the best use cases for the new reasoning models.
📝 Final Thoughts and Future Exploration
In the conclusion, the speaker shares their first impressions of the new reasoning models from OpenAI. They appreciate the potential of these models but acknowledge the need for further exploration to understand their best use cases. They express excitement about the possibilities these models offer and plan to continue experimenting with them. The speaker also invites viewers to share their experiences and use cases in the comments. They mention their intention to create more videos exploring the models and thank the audience for joining the discussion.
Mindmap
Keywords
💡OpenAI-o1
💡Cursor
💡Reasoning Model
💡Chain of Thought
💡Reinforcement Learning
💡API
💡Claude 3.5
💡Docker
💡CoinGecko API
💡Backtesting
Highlights
OpenAI has released a new reasoning model called 'o1'.
The 'o1' model is designed to spend more time thinking before responding.
The model can reason through complex tasks and solve harder problems in science, coding, and more.
OpenAI introduced 'reasoning tokens' for the model to think and break down understanding of the prompt.
The 'o1' series models are large language models trained with reinforcement learning for complex reasoning.
The 'o1' model produces an answer with visible completion tokens after generating reasoning tokens.
There are limitations with the 'o1' model, such as fixed temperatures and no streaming or system messages.
The 'o1 mini' is a faster and cheaper version of 'o1', ideal for tasks that don't require extensive general knowledge.
The 'o1 preview' is designed for reasoning about hard problems using broad general knowledge.
Pricing for 'o1 preview' is $15 per million tokens, and 'o1 mini' is five times cheaper.
Developers with API usage tier five can start prototyping with the 'o1' API.
The rate limit for the 'o1' API is 20 requests per minute.
The video demonstrates setting up and using the 'o1' model with the Cursor code editor.
The 'o1' model is tested against Claude 3.5 and GPT-4 for building a space game in Next.js.
Claude 3.5 produced a working space game faster than the 'o1' models.
The 'o1 mini' model failed to produce a working space game, despite providing detailed code explanations.
The 'o1 preview' model also failed to produce a fully working space game, with issues like missing assets and no collision detection.
The video concludes that for the space game task, Claude 3.5 outperformed the 'o1' models in speed and effectiveness.
The 'o1' models may be better suited for more complex reasoning tasks than building simple games.
The video suggests that further exploration is needed to understand the best use cases for the 'o1' models.