All About AI - OpenAI o3-mini vs DeepSeek R1 - First TESTS and Impressions
The video provides a detailed comparison between the O3 Mini and Deep Seek R1 models across several AI tasks, including coding tests, video editing, text extraction, AI agent orchestration, and reasoning challenges. The O3 Mini model, despite its larger token output capacity, struggled with some tasks like 3D animation coding but excelled in AI agent orchestration, providing more accurate instructions to agents. Deep Seek R1 performed better in initial coding tests and reasoning tasks, such as the river crossing puzzle and the 'between the lines' question, demonstrating its ability to break free from training data biases. Both models successfully completed a video editing task and a PDF URL extraction task. The O3 Mini's large token output was tested, but it did not reach its theoretical maximum, highlighting the balance between reasoning and output tokens. Overall, O3 Mini showed promise in orchestration tasks, while Deep Seek R1 was more consistent in reasoning and coding challenges.
Key Points:
- O3 Mini excels in AI agent orchestration, providing accurate task assignments.
- Deep Seek R1 outperforms in coding and reasoning tasks, showing better problem-solving.
- Both models successfully handle video editing and PDF URL extraction tasks.
- O3 Mini's large token output capacity is not fully utilized, indicating room for optimization.
- Deep Seek R1 demonstrates ability to overcome training data biases in reasoning tasks.
Details:
1. 🔍 Introduction and Model Overview
- The comparison focuses on OpenAI's O3 Mini and Deep Seek R1 models through a series of structured tests.
- Tests include evaluating coding capabilities, creating 3D animations, editing small video clips, extracting text, and orchestrating AI agent tasks.
- A crucial test involves the 'Breaking Free' challenge from training data, specifically the river crossing puzzle, to assess model adaptability.
- O3 Mini's output window of 100,000 tokens will be tested against Deep Seek R1's 8,000 token capacity to evaluate handling of extensive output tasks.
- The objective is to understand task assignment efficiency and large response handling by each model.
2. 💲 Pricing Details & Initial Coding Tests
2.1. Pricing Details
2.2. Initial Coding Setup
2.3. Coding Test Results
3. 🔗 PDF URL Extraction & Results Analysis
3.1. Video and Audio Processing with Python
3.2. PDF URL Extraction with HTML
4. 🤖 AI Agent Orchestration Performance
4.1. Initial Code Comparison
4.2. AI Agent Orchestration Setup
4.3. Execution and Results with O3 Mini
4.4. Execution and Results with Deep Seek
5. 🧩 Puzzle Solving: Breaking Free from Training Data
5.1. Introduction to the Puzzle
5.2. Objective and Challenge
5.3. Test Execution
5.4. Results for deeps R1
5.5. Results for O3 Mini
6. 🧠 Analyzing Contextual Understanding
- The exercise evaluates models' contextual understanding by embedding clues in a narrative, such as a character carrying blue paint and receiving a message to rush to the hospital.
- Deeps R1 model correctly interpreted the clues, linking the blue paint to a nursery for a baby boy and the hospital message to labor, showcasing strong contextual inference.
- O3 mini model misinterpreted the clues, suggesting an unrelated accident scenario, indicating weaknesses in contextual clue processing.
- Highlighting the importance of contextualization, the test reveals how well models can make accurate predictions by understanding hidden clues in narratives.