Introduction
OpenAI has introduced its new model, o1, based on the much – anticipated “strawberry” architecture. As a ChatGPT Plus user, I had the chance to explore this innovative model firsthand. In this article, we will conduct a thorough comparison between GPT – 4o and OpenAI o1, covering their performance, capabilities, and more, to help users and developers make informed decisions.
Purpose of the Comparison: GPT – 4o vs OpenAI o1
GPT – 4o is a versatile, multimodal model capable of handling text, speech, and video inputs, making it suitable for general – purpose tasks. It powers the latest ChatGPT iteration. OpenAI o1, however, is a more specialized model, designed for complex reasoning and problem – solving in areas like math and coding. This comparison aims to highlight their unique strengths and optimal use cases.
Overview of All the OpenAI o1 Models
OpenAI offers different variants of the o1 model. The o1 – preview points to the most recent snapshot of the o1 model, such as o1 – preview – 2024 – 09 – 12, with a context window of 128,000 tokens and a max output of 32,768 tokens, trained on data up to Oct 2023. The o1 – mini also has its latest snapshot, o1 – mini – 2024 – 09 – 12, with a context window of 128,000 tokens and a max output of 65,536 tokens, trained on data up to the same time.
Model Capabilities of o1 and GPT 4o
OpenAI o1
OpenAI’s o1 model has shown remarkable performance. It has ranked highly in competitive programming challenges and math olympiad qualifiers. Trained using a large – scale reinforcement learning algorithm with a “chain of thought” process, it enhances reasoning abilities, allowing for data – efficient learning. For example, it can break down complex tasks, learn from mistakes, and try alternative approaches.
GPT – 4o
GPT – 4o is a multimodal powerhouse. It can handle text, speech, and video inputs seamlessly, making it a strong choice for applications like voice assistants, chatbots, and content creation tools. It has improved upon previous models in terms of latency and integration of different input types in a single neural network.
Performance Comparisons
Multilingual Capabilities
Evaluations using the MMLU test set translated into 14 languages by human translators show that o1 – preview has significantly higher multilingual capabilities than GPT – 4o, especially in languages like Arabic, Bengali, and Chinese. o1 – mini also outperforms GPT – 4o – mini in multilingual tasks.
Human Exams and ML Benchmarks
o1 has outperformed GPT – 4o on most reasoning – intensive tasks in human exams and machine learning benchmarks. For instance, in the AIME 2024, o1 achieved much higher accuracy than GPT – 4o, and in the GPQA Diamond benchmark, it even surpassed human experts with PhDs in specific problem – solving scenarios.
Jailbreak Evaluations
The o1 models (o1 – preview and o1 – mini) have shown significant improvement in robustness against “jailbreaks” compared to GPT – 4o, especially in the StrongReject evaluation, which uses advanced jailbreak techniques.
Handling Agentic Tasks
In handling agentic tasks, GPT – 4o outperformed in some tasks like purchasing GPU via Ranger and sampling tasks, while the o1 models showed potential in passing primary tasks under certain conditions but still had difficulties with complex, multi – step tasks.
Hallucinations Evaluations
o1 – preview exhibits fewer hallucinations compared to GPT – 4o, and o1 – mini hallucinates less frequently than GPT – 4o – mini across some datasets. However, there are concerns that in practice, they may hallucinate more frequently in some domains, and more research is needed.
Quality vs. Speed vs. Cost
Regarding quality, o1 – preview and o1 – mini top the charts. In terms of speed, o1 – mini is relatively fast, while o1 – preview is slower. Cost – wise, o1 – preview is expensive at 26.3 USD per million tokens, and o1 – mini is more affordable at 5 USD. GPT – 4o is optimized for quicker response times and lower costs for general tasks.
Human Preferences
Human trainers preferred o1 – preview in tasks requiring strong reasoning, such as data analysis and programming, but in natural language – centered tasks like personal writing, the preference for o1 – preview was not as strong.
Different Tasks Comparison
In tasks like decoding ciphered text, OpenAI o1 provided a more effective solution. In health science diagnosis, both models had plausible but different diagnoses, with OpenAI o1’s suggestion being considered more likely in one case. For reasoning questions, OpenAI o1 mini solved the problem correctly while GPT – 4o failed. In coding tasks, OpenAI o1 accurately implemented the specified color palette while GPT – 4o did not.
API and Usage Details
The new models are currently available only to tier 5 API users with a minimum spend. They lack support for some features like system prompts and streaming. They introduce “reasoning tokens” which are hidden from users but count as output tokens and are billed accordingly.
Limitations of OpenAI o1
OpenAI o1 has limitations such as limited non – STEM knowledge, lack of multimodal capabilities (only text prompts), slower response times, high cost, early – stage flaws like possible errors or hallucinations, rate limits, and it is not a replacement for GPT – 4o in all use cases.
The Final Verdict: GPT – 4o vs OpenAI o1
Both GPT – 4o and OpenAI o1 are significant advancements in AI. GPT – 4o is better for general – purpose tasks, quick responses, and multimodal interactions. OpenAI o1 shines in complex reasoning, mathematical, and scientific tasks. The choice between them depends on specific task requirements.
