While specific architectural details about GPT-4.5 are not extensively documented in the search results, we can infer some key differences between GPT-4.5 and GPT-4o based on the available information:
Architecture and Training
- GPT-4o is designed with native multimodality, meaning it processes text, vision, and audio inputs within a single neural network. This architecture allows for faster and more efficient handling of multimodal tasks compared to GPT-4, which relies on external models like Dall-E for image processing[1].
- GPT-4.5 builds upon the foundation of GPT-4o, incorporating new training techniques such as Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). These methods aim to improve the model's performance by making responses feel more natural and aligning them better with user intent. Additionally, GPT-4.5 uses Scalable Alignment, where smaller models generate training data for larger models, enhancing efficiency and nuance in following instructions[5].
Performance and Capabilities
- GPT-4o is noted for its speed and efficiency, particularly in tasks requiring quick responses, such as customer service or real-time data analysis. It generates responses at a rate of 103 tokens per second, making it suitable for applications where speed is crucial[4].
- GPT-4.5 shows significant improvements over GPT-4o in specific areas like math and science, with gains of 27.4% and 17.8%, respectively. It also offers moderate enhancements in multilingual and multimodal performance. This suggests that GPT-4.5 is more reliable for factual reasoning and complex tasks[5].
Multimodality and Multitasking
- GPT-4o is designed to handle multiple data types (text, images, audio) within its core architecture, which enhances its performance in multimodal tasks compared to GPT-4[1].
- GPT-4.5 likely inherits this multimodal capability from GPT-4o, with additional improvements in handling diverse data types more effectively. However, specific details on how GPT-4.5 enhances multimodality beyond GPT-4o are not explicitly mentioned in the available information.
In summary, while both models are advanced and capable, GPT-4.5 focuses on refining performance through enhanced training methods and improving specific capabilities like math and science reasoning. GPT-4o, on the other hand, excels in speed and native multimodality, making it suitable for real-time applications and tasks requiring diverse data processing.
Citations:
[1] https://www.techtarget.com/searchenterpriseai/feature/GPT-4o-vs-GPT-4-How-do-they-compare
[2] https://www.amitysolutions.com/blog/chatgpt-35-vs-chatgpt-4
[3] https://www.kommunicate.io/blog/chatgpt-4-vs-chatgpt-3-5-key-differences/
[4] https://ttms.com/the-new-era-of-chatgpt-what-makes-o1-preview-different-from-gpt-4o/
[5] https://www.vellum.ai/blog/gpt-4-5-is-here-heres-how-good-this-model-is
[6] https://www.linkedin.com/pulse/5-key-differences-between-gpt-4-gpt-4o-ekenedilichukwu-goodness-zfj1f
[7] https://community.openai.com/t/gpt-4-vs-gpt-4o-which-is-the-better/746991
[8] https://neoteric.eu/blog/gpt-4o-vs-gpt-4-vs-gpt-3-5-comparison-in-real-world-scenarios/