Comparing O1 Preview and GPT-4o: A Comprehensive Analysis
Both models exhibit unique strengths and weaknesses, making them suitable for different applications. This article delves into a detailed comparison of these two models, highlighting their capabilities, performance, and areas of improvement.
Performance on Agentic Tasks
O1 Preview and GPT-4o have been evaluated on a variety of agentic tasks, which are designed to test the models' ability to perform complex, real-world tasks autonomously.
- O1 Preview shows a strong performance on contextual subtasks but struggles with primary agentic tasks. For instance, it often fails to complete tasks like creating an authenticated API proxy or loading an inference server in Docker without leaving major parts incomplete [4].
- GPT-4o, on the other hand, also faces challenges with agentic tasks but demonstrates a slightly better ability to self-correct and problem-solve during rollouts [9].
Multilingual Performance
When it comes to multilingual capabilities, O1 Preview has a distinct edge:
- O1 Preview was evaluated using professionally translated test sets in 14 languages, showing a robust performance across different languages [4].
- GPT-4o relies on machine translation, which may not capture the nuances of each language as effectively as human translation [4].
Fairness and Bias
Fairness and bias are critical aspects of AI models, and both O1 Preview and GPT-4o have been rigorously tested in this domain:
- O1 Preview is less prone to selecting stereotyped options compared to GPT-4o. It selects the correct answer 94% of the time on unambiguous questions, whereas GPT-4o does so 72% of the time [6].
- However, O1 Preview is less likely to choose the "Unknown" option for ambiguous questions, which can reduce its performance in scenarios where the correct answer is not clear [6].
Hallucination Rates
Hallucination, or the generation of incorrect information, is a common issue in AI models:
- O1 Preview hallucinates less frequently than GPT-4o. For example, in the SimpleQA dataset, O1 Preview has a hallucination rate of 0.44 compared to GPT-4o's 0.61 [10].
- Despite this, anecdotal feedback suggests that O1 Preview's more detailed answers can sometimes make its hallucinations more convincing, potentially increasing the risk of users trusting incorrect information [10].
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Coding and Problem-Solving
In the realm of coding and problem-solving, both models have shown significant improvements:
- O1 Preview demonstrates a 21 percentage point improvement over GPT-4o in multiple-choice coding problems and a 15 percentage point improvement in coding tasks (pass@1 metric) [8].
- This indicates that O1 Preview is better suited for tasks that require precise and accurate coding capabilities.
Conclusion
In summary, both O1 Preview and GPT-4o have their unique strengths and areas for improvement. O1 Preview excels in multilingual performance, fairness, and coding tasks, while GPT-4o shows better self-correction and problem-solving abilities in agentic tasks. Understanding these nuances can help organizations choose the right model for their specific needs, ensuring optimal performance and reliability.
By leveraging the strengths of each model, we can push the boundaries of what AI can achieve, paving the way for more advanced and capable systems in the future.
[4] Page 32 of "o1-system-card.pdf"
[6] Page 5 of "o1-system-card.pdf"
[8] Page 29 of "o1-system-card.pdf"
[9] Page 21 of "o1-system-card.pdf"
[10] Page 5 of "o1-system-card.pdf"