OpenAI has released its new model based on the much-anticipated “strawberry” architecture. This innovative model, known as o1, enhances reasoning capabilities, allowing it to think through problems more effectively before providing answers. As a ChatGPT Plus user, I had the opportunity to explore this new model firsthand. I’m excited to share my insights on its performance, capabilities, and implications for users and developers alike. I will thoroughly compare GPT-4o vs. OpenAI o1 on different metrics. Without any further ado, let’s begin.
In this article, you will explore the differences between GPT o1 and GPT-4o, including a comparison of GPT o1 vs GPT 4. We’ll provide insights on the performance in the GPT 4o vs o1 preview and guide you on how to use GPT o1 effectively. Additionally, we’ll discuss the GPT o1 cost, highlight the availability of a GPT o1 free tier, and introduce the GPT o1 mini version. Finally, we’ll analyze the ongoing debate of GPT 4o vs o1 vs OpenAI to help you make an informed decision.
Read on!
New to OpenAI Models? Read this to know how to use OpenAI o1: How to Access OpenAI o1?
New Update on OpenAI o1:
Here’s why we are comparing – GPT-4o vs OpenAI o1:
Purpose of the Comparison: This comparison highlights the unique strengths of each model and clarifies their optimal use cases. While OpenAI o1 is excellent for complex reasoning tasks, it is not intended to replace GPT-4o for general-purpose applications. By examining their capabilities, performance metrics, speed, cost, and use cases, I will provide insights into the model better suited for different needs and scenarios.
Here’s the tabular representation of OpenAI o1:
MODEL | DESCRIPTION | CONTEXT WINDOW | MAX OUTPUT TOKENS | TRAINING DATA |
o1-preview | Points to the most recent snapshot of the o1 model:o1-preview-2024-09-12 | 128,000 tokens | 32,768 tokens | Up to Oct 2023 |
o1-preview-2024-09-12 | Latest o1 model snapshot | 128,000 tokens | 32,768 tokens | Up to Oct 2023 |
o1-mini | Points to the most recent o1-mini snapshot:o1-mini-2024-09-12 | 128,000 tokens | 65,536 tokens | Up to Oct 2023 |
o1-mini-2024-09-12 | Latest o1-mini model snapshot | 128,000 tokens | 65,536 tokens | Up to Oct 2023 |
OpenAI’s o1 model has demonstrated remarkable performance across various benchmarks. It ranked in the 89th percentile on Codeforces competitive programming challenges and placed among the top 500 in the USA Math Olympiad qualifier (AIME). Additionally, it surpassed human PhD-level accuracy on a benchmark of physics, biology, and chemistry problems (GPQA).
The model is trained using a large-scale reinforcement learning algorithm that enhances its reasoning abilities through a “chain of thought” process, allowing for data-efficient learning. Findings indicate that its performance improves with increased computing during training and more time allocated for reasoning during testing, prompting further investigation into this novel scaling approach, which differs from traditional LLM pretraining methods. Before further comparing, let’s look into “How Chain of Thought process improves reasoning abilities of OpenAI o1.”
OpenAI o1 models introduce new trade-offs in cost and performance to provide better “reasoning” abilities. These models are trained specifically for a “chain of thought” process, meaning they are designed to think step-by-step before responding. This builds upon the chain of thought prompting pattern introduced in 2022, which encourages AI to think systematically rather than just predict the next word. The algorithm teaches them to break down complex tasks, learn from mistakes, and try alternative approaches when necessary.
Also read: o1: OpenAI’s New Model That ‘Thinks’ Before Answering Tough Problems
The o1 models introduce reasoning tokens. The models use these reasoning tokens to “think,” breaking down their understanding of the prompt and considering multiple approaches to generating a response. After generating reasoning tokens, the model produces an answer as visible completion tokens and discards the reasoning tokens from its context.
The o1 model utilizes a reinforcement learning algorithm that encourages longer and more in-depth thinking periods before producing a response. This process is designed to help the model better handle complex reasoning tasks.
The model’s performance improves with both increased training time (train-time compute) and when it is allowed more time to think during evaluation (test-time compute).
The chain of thought approach enables the model to break down complex problems into simpler, more manageable steps. It can revisit and refine its strategies, trying different methods when the initial approach fails.
This method is beneficial for tasks requiring multi-step reasoning, such as mathematical problem-solving, coding, and answering open-ended questions.
Read more articles on Prompt Engineering here.
In evaluations comparing the performance of o1-preview to GPT-4o, human trainers overwhelmingly preferred the outputs of o1-preview in tasks that required strong reasoning capabilities.
Integrating chain of thought reasoning into the model also contributes to improved safety and alignment with human values. By embedding the safety rules directly into the reasoning process, o1-preview shows a better understanding of safety boundaries, reducing the likelihood of harmful completions even in challenging scenarios.
OpenAI has decided to keep the detailed chain of thought hidden from the user to protect the integrity of the model’s thought process and maintain a competitive advantage. However, they provide a summarized version to users to help understand how the model arrived at its conclusions.
This decision allows OpenAI to monitor the model’s reasoning for safety purposes, such as detecting manipulation attempts or ensuring policy compliance.
Also read: GPT-4o vs Gemini: Comparing Two Powerful Multimodal AI Models
The o1 models showed significant advances in key performance areas:
Safety evaluations show that o1-preview performs significantly better than GPT-4o in handling potentially harmful prompts and edge cases, reinforcing its robustness.
Also read: OpenAI’s o1-mini: A Game-Changing Model for STEM with Cost-Efficient Reasoning
GPT-4o is a multimodal powerhouse adept at handling text, speech, and video inputs, making it versatile for a range of general-purpose tasks. This model powers ChatGPT, showcasing its strength in generating human-like text, interpreting voice commands, and even analyzing video content. For users who require a model that can operate across various formats seamlessly, GPT-4o is a strong contender.
Before GPT-4o, using Voice Mode with ChatGPT involved an average latency of 2.8 seconds with GPT-3.5 and 5.4 seconds with GPT-4. This was achieved through a pipeline of three separate models: a basic model first transcribed audio to text, then GPT-3.5 or GPT-4 processed the text input to generate a text output, and finally, a third model converted that text back to audio. This setup meant that the core AI—GPT-4—was somewhat limited, as it couldn’t directly interpret nuances like tone, multiple speakers, background sounds or express elements like laughter, singing, or emotion.
With GPT-4o, OpenAI has developed an entirely new model that integrates text, vision, and audio in a single, end-to-end neural network. This unified approach allows GPT-4o to handle all inputs and outputs within the same framework, greatly enhancing its ability to understand and generate more nuanced, multimodal content.
You can explore more of GPT-4o capabilities here: Hello GPT-4o.
The comparison between OpenAI’s o1 models and GPT-4o highlights their multilingual performance capabilities, focusing on the o1-preview and o1-mini models against GPT-4o.
The MMLU (Massively Multilingual Language Understanding) test set was translated into 14 languages using human translators to assess their performance across multiple languages. This approach ensures higher accuracy, especially for languages that are less represented or have limited resources, such as Yoruba. The study used these human-translated test sets to compare the models’ abilities in diverse linguistic contexts.
The use of human translations rather than machine translations (as in earlier evaluations with models like GPT-4 and Azure Translate) proves to be a more reliable method for evaluating performance. This is particularly true for less widely spoken languages, where machine translations often lack accuracy.
Overall, the evaluation shows that both o1-preview and o1-mini outperform their GPT-4o counterparts in multilingual tasks, especially in linguistically diverse or low-resource languages. The use of human translations in testing underscores the superior language understanding of the o1 models, making them more capable of handling real-world multilingual scenarios. This demonstrates OpenAI’s advancement in building models with a broader, more inclusive language understanding.
To demonstrate improvements in reasoning capabilities over GPT-4o, the o1 model was tested on a diverse range of human exams and machine learning benchmarks. The results show that o1 significantly outperforms GPT-4o on most reasoning-intensive tasks, using the maximal test-time compute setting unless otherwise noted.
Here, we discuss the evaluation of the robustness of the o1 models (specifically o1-preview and o1-mini) against “jailbreaks,” which are adversarial prompts designed to bypass the model’s content restrictions. The following four evaluations were used to measure the models’ resilience to these jailbreaks:
Comparison with GPT-4o:
The figure above compares the performance of the o1-preview, o1-mini, and GPT-4o models on these evaluations. The results show that the o1 models (o1-preview and o1-mini) demonstrate a significant improvement in robustness over GPT-4o, particularly in the StrongReject evaluation, which is noted for its difficulty and reliance on advanced jailbreak techniques. This suggests that the o1 models are better equipped to handle adversarial prompts and comply with content guidelines than GPT-4o.
Here, we evaluate OpenAI’s o1-preview, o1-mini, and GPT-4o in handling agentic tasks, highlighting their success rates across various scenarios. The tasks were designed to test the models’ abilities to perform complex operations such as setting up Docker containers, launching cloud-based GPU instances, and creating authenticated web servers.
The evaluation was conducted in two primary environments:
The tasks cover a range of categories, such as:
OpenAI o1-preview and o1-mini are rolling out today in the API for developers on tier 5.
— OpenAI Developers (@OpenAIDevs) September 12, 2024
o1-preview has strong reasoning capabilities and broad world knowledge.
o1-mini is faster, 80% cheaper, and competitive with o1-preview at coding tasks.
More in https://t.co/l6VkoUKFla. https://t.co/moQFsEZ2F6
The graph visually represents the success rates of the models over 100 trials per task. Key observations include:
Also read: From GPT to Mistral-7B: The Exciting Leap Forward in AI Conversations
The evaluation reveals that while frontier models, such as o1-preview and o1-mini, occasionally succeed in passing primary agentic tasks, they often do so by proficiently handling contextual subtasks. However, these models still show notable deficiencies in consistently managing complex, multi-step tasks.
Following post-mitigation updates, the o1-preview model exhibited distinct refusal behaviors compared to earlier ChatGPT versions. This led to decreased performance on specific subtasks, particularly those involving reimplementing APIs like OpenAI’s. On the other hand, both o1-preview and o1-mini demonstrated the potential to pass primary tasks under certain conditions, such as establishing authenticated API proxies or deploying inference servers in Docker environments. Nonetheless, manual inspection revealed that these successes sometimes involved oversimplified approaches, like using a less complex model than the expected Mistral 7B.
Overall, this evaluation underscores the ongoing challenges advanced AI models face in achieving consistent success across complex agentic tasks. While models like GPT-4o exhibit strong performance in more straightforward or narrowly defined tasks, they still encounter difficulties with multi-layered tasks that require higher-order reasoning and sustained multi-step processes. The findings suggest that while progress is evident, there remains a significant path ahead for these models to handle all types of agentic tasks robustly and reliably.
Also read about KnowHalu: AI’s Biggest Flaw Hallucinations Finally Solved With KnowHalu!
To better understand the hallucination evaluations of different language models, the following assessment compares GPT-4o, o1-preview, and o1-mini models across several datasets designed to provoke hallucinations:
While quantitative evaluations suggest that the o1 models (both preview and mini versions) hallucinate less frequently than the GPT-4o models, there are concerns based on qualitative feedback that this may not always hold true. More in-depth analysis across various domains is needed to develop a holistic understanding of how these models handle hallucinations and their potential impact on users.
Also read: Is Hallucination in Large Language Models (LLMs) Inevitable?
Let’s compare the models regarding quality, speed, and cost. Here we have a chart that compares multiple models:
The o1-preview and o1-mini models are topping the charts! They deliver the highest quality scores, with 86 for the o1-preview and 82 for the o1-mini. That means these two models outperform others like GPT-4o and Claude 3.5 Comet.
Now, talking about speed—things get a little more interesting. The o1-mini is decently fast, clocking in at 74 tokens per second, which puts it in the middle range. However, the o1-preview is on the slower side, churning out just 23 tokens per second. So, while they offer quality, you might have to trade a bit of speed if you go with the o1-preview.
And here comes the kicker! The o1-preview is quite the splurge at 26.3 USD per million tokens—way more than most other options. Meanwhile, the o1-mini is a more affordable choice, priced at 5 USD. But if you’re budget-conscious, models like Gemini (at just 0.1 USD) or the Llama models might be more up your alley.
GPT-4o is optimized for quicker response times and lower costs, especially compared to GPT-4 Turbo. The efficiency benefits users who need fast and cost-effective solutions without sacrificing the output quality in general tasks. The model’s design makes it suitable for real-time applications where speed is crucial.
However, GPT o1 trades speed for depth. Due to its focus on in-depth reasoning and problem-solving, it has slower response times and incurs higher computational costs. The model’s sophisticated algorithms require more processing power, which is a necessary trade-off for its ability to handle highly complex tasks. Therefore, OpenAI o1 may not be the ideal choice when quick results are needed, but it shines in scenarios where accuracy and comprehensive analysis are paramount.
Read More About it Here: o1: OpenAI’s New Model That ‘Thinks’ Before Answering Tough Problems
Moreover, one of the standout features of GPT-o1 is its reliance on prompting. The model thrives on detailed instructions, which can significantly enhance its reasoning capabilities. By encouraging it to visualize the scenario and think through each step, I found that the model could produce more accurate and insightful responses. This prompts-heavy approach suggests that users must adapt their interactions with the model to maximize its potential.
In comparison, I also tested GPT-4o with general-purpose tasks, and surprisingly, it performed better than the o1 model. This indicates that while advancements have been made, there is still room for refinement in how these models process complex logic.
OpenAI conducted evaluations to understand human preferences for two of its models: o1-preview and GPT-4o. These assessments focused on challenging, open-ended prompts spanning various domains. In this evaluation, human trainers were presented with anonymized responses from both models and asked to choose which response they preferred.
The results showed that the o1-preview emerged as a clear favorite in areas that require heavy reasoning, such as data analysis, computer programming, and mathematical calculations. In these domains, o1-preview was significantly preferred over GPT-4o, indicating its superior performance in tasks that demand logical and structured thinking.
However, the preference for o1-preview was not as strong in domains centered around natural language tasks, such as personal writing or text editing. This suggests that while o1-preview excels in complex reasoning, it may not always be the best choice for tasks that rely heavily on nuanced language generation or creative expression.
The findings highlight a critical point: o1-preview shows great potential in contexts that benefit from better reasoning capabilities, but its application might be more limited when it comes to more subtle and creative language-based tasks. This dual nature offers valuable insights for users in choosing the right model based on their needs.
Also read: Generative Pre-training (GPT) for Natural Language Understanding
The difference in model design and capabilities translates into their suitability for different use cases:
GPT-4o excels in tasks involving text generation, translation, and summarization. Its multimodal capabilities make it particularly effective for applications that require interaction across various formats, such as voice assistants, chatbots, and content creation tools. The model is versatile and flexible, suitable for a wide range of applications requiring general AI tasks.
OpenAI o1 is ideal for complex scientific and mathematical problem-solving. It enhances coding tasks through improved code generation and debugging capabilities, making it a powerful tool for developers and researchers working on challenging projects. Its strength is handling intricate problems requiring advanced reasoning, detailed analysis, and domain-specific expertise.
GPT-4o Analysis
OpenAI o1 Analysis
Verdict
Also read: 3 Hands-On Experiments with OpenAI’s o1 You Need to See
GPT-4o Diagnosis: Cornelia de Lange Syndrome (CdLS)
OpenAI o1 Diagnosis: KBG Syndrome
Verdict
To check the reasoning of both models, I asked advanced-level reasoning questions.
Five students, P, Q, R, S and T stand in a line in some order and receive cookies and biscuits to eat. No student gets the same number of cookies or biscuits. The person first in the queue gets the least number of cookies. Number of cookies or biscuits received by each student is a natural number from 1 to 9 with each number appearing at least once.
The total number of cookies is two more than the total number of biscuits distributed. R who was in the middle of the line received more goodies (cookies and biscuits put together) than everyone else. T receives 8 more cookies than biscuits. The person who is last in the queue received 10 items in all, while P receives only half as many totally. Q is after P but before S in the queue. Number of cookies Q receives is equal to the number of biscuits P receives. Q receives one more good than S and one less than R. Person second in the queue receives an odd number of biscuits and an odd number of cookies.
Answer: Q was 4th in the queue.
Also read: How Can Prompt Engineering Transform LLM Reasoning Ability?
GPT-4o Analysis
GPT-4o failed to solve the problem correctly. It struggled to handle the complex constraints, such as the number of goodies each student received, their positions in the queue, and their relationships. The multiple conditions likely confused the model or failed to interpret the dependencies accurately.
OpenAI o1 Analysis
OpenAI o1 accurately deduced the correct order by efficiently analyzing all constraints. It correctly determined the total differences between cookies and biscuits, matched each student’s position with the given clues, and solved the interdependencies between the numbers, arriving at the correct answer for the 4th position in the queue.
Verdict
GPT-4o failed to solve the problem due to difficulties with complex logical reasoning.
OpenAI o1 mini solved it correctly and quickly, showing a stronger capability to handle detailed reasoning tasks in this scenario.
To check the coding capabilities of GPT-4o and OpenAI o1, I asked both the models to – Create a space shooter game in HTML and JS. Also, make sure the colors you use are blue and red. Here’s the result:
GPT-4o
I asked GPT-4o to create a shooter game with a specific color palette, but the game used only blue color boxes instead. The color scheme I requested wasn’t applied at all.
OpenAI o1
On the other hand, OpenAI o1 was a success because it accurately implemented the color palette I specified. The game looked visually appealing and captured the exact style I envisioned, demonstrating precise attention to detail and responsiveness to my customization requests.
The API documentation reveals several key features and trade-offs:
Also read: Here’s How You Can Use GPT 4o API for Vision, Text, Image & More.
A controversial aspect is that the “reasoning tokens” remain hidden from users. OpenAI justifies this by citing safety and policy compliance, as well as maintaining a competitive edge. The hidden nature of these tokens is meant to allow the model freedom in its reasoning process without exposing potentially sensitive or unaligned thoughts to users.
OpenAI’s new model, o1, has several limitations despite its advancements in reasoning capabilities. Here are the key limitations:
These limitations suggest that while o1 offers enhanced reasoning capabilities, it may not yet be the best choice for all applications, particularly those needing broad knowledge or rapid responses.
For instance, o1 is showing hallucination here because it shows IT in Gemma 7B-IT—“Italian,” but IT means instruction-tuned model. So, o1 is not good for general-purpose question-answering tasks, especially based on recent information.
Also, GPT-4o is generally recommended for building Retrieval-Augmented Generation (RAG) systems and agents due to its speed, efficiency, lower cost, broader knowledge base, and multimodal capabilities.
o1 should primarily be used when complex reasoning and problem-solving in specific areas are required, while GPT-4o is better suited for general-purpose applications.
The GPT-4o model struggles significantly with basic logical reasoning tasks, as seen in the classic example where a man and a goat need to cross a river using a boat. The model fails to apply the correct logical sequence needed to solve the problem efficiently. Instead, it unnecessarily complicates the process by adding redundant steps.
In the provided example, GPT-4o suggests:
This solution is far from optimal as it introduces an extra trip that isn’t required. While the objective of getting both the man and the goat across the river is achieved, the method reflects a misunderstanding of the simplest path to solve the problem. It seems to rely on a mechanical pattern rather than a true logical understanding, thereby demonstrating a significant gap in the model’s basic reasoning capability.
In contrast, the OpenAI o1 model better understands logical reasoning. When presented with the same problem, it identifies a simpler and more efficient solution:
This approach is straightforward, reducing unnecessary steps and efficiently achieving the goal. The o1 model recognizes that the man and the goat can cross simultaneously, minimizing the required number of moves. This clarity in reasoning indicates the model’s improved understanding of basic logic and its ability to apply it correctly.
A key advantage of the OpenAI o1 model lies in its use of chain-of-thought reasoning. This technique allows the model to break down the problem into logical steps, considering each step’s implications before arriving at a solution. Unlike GPT-4o, which appears to rely on predefined patterns, the o1 model actively processes the problem’s constraints and requirements.
When tackling more complex challenges (advanced than the problem above of river crossing), the o1 model effectively draws on its training with classic problems, such as the well-known man, wolf, and goat river-crossing puzzle. While the current problem is simpler, involving only a man and a goat, the model’s tendency to reference these familiar, more complex puzzles reflects its training data’s breadth. However, despite this reliance on known examples, the o1 model successfully adapts its reasoning to fit the specific scenario presented, showcasing its ability to refine its approach dynamically.
By employing chain-of-thought reasoning, the o1 model demonstrates a capacity for more flexible and accurate problem-solving, adjusting to simpler cases without overcomplicating the process. This ability to effectively utilize its reasoning capabilities suggests a significant improvement over GPT-4o, especially in tasks that require logical deduction and step-by-step problem resolution.
Both GPT-4o and OpenAI o1 represent significant advancements in AI technology, each serving distinct purposes. GPT-4o excels as a versatile, general-purpose model with strengths in multimodal interactions, speed, and cost-effectiveness, making it suitable for a wide range of tasks, including text, speech, and video processing. Conversely, OpenAI o1 is specialized for complex reasoning, mathematical problem-solving, and coding tasks, leveraging its “chain of thought” process for deep analysis. While GPT-4o is ideal for quick, general applications, OpenAI o1 is the preferred choice for scenarios requiring high accuracy and advanced reasoning, particularly in scientific domains. The choice depends on task-specific needs.
Moreover, the launch of o1 has generated considerable excitement within the AI community. Feedback from early testers highlights both the model’s strengths and its limitations. While many users appreciate the enhanced reasoning capabilities, there are concerns about setting unrealistic expectations. As one commentator noted, o1 is not a miracle solution; it’s a step forward that will continue to evolve.
Looking ahead, the AI landscape is poised for rapid development. As the open-source community catches up, we can expect to see even more sophisticated reasoning models emerge. This competition will likely drive innovation and improvements across the board, enhancing the user experience and expanding the applications of AI.
Also read: Reasoning in Large Language Models: A Geometric Perspective
In a nutshell, both GPT-4o vs OpenAI o1 represent significant advancements in AI technology, they cater to different needs: GPT-4o is a general-purpose model that excels in a wide variety of tasks, particularly those that benefit from multimodal interaction and quick processing. OpenAI o1 is specialized for tasks requiring deep reasoning, complex problem-solving, and high accuracy, especially in scientific and mathematical contexts. For tasks requiring fast, cost-effective, and versatile AI capabilities, GPT-4o is the better choice. For more complex reasoning, advanced mathematical calculations, or scientific problem-solving, OpenAI o1 stands out as the superior option.
Ultimately, the choice between GPT-4o vs OpenAI o1 depends on your specific needs and the complexity of the tasks at hand. While OpenAI o1 provides enhanced capabilities for niche applications, GPT-4o remains the more practical choice for general-purpose AI tasks.
Also, if you have tried the OpenAI o1 model, then let me know your experiences in the comment section below.
If you want to become a Generative AI expert, then explore: GenAI Pinnacle Program
Ans. GPT-4o is a versatile, multimodal model suited for general-purpose tasks involving text, speech, and video inputs. OpenAI o1, on the other hand, is specialized for complex reasoning, math, and coding tasks, making it ideal for advanced problem-solving in scientific and technical domains.
Ans. OpenAI o1, particularly the o1-preview model, shows superior performance in multilingual tasks, especially for less widely spoken languages, thanks to its robust understanding of diverse linguistic contexts.
Ans. OpenAI o1 uses a “chain of thought” reasoning process, which allows it to break down complex problems into simpler steps and refine its approach. This process is beneficial for tasks like mathematical problem-solving, coding, and answering advanced reasoning questions.
Ans. OpenAI o1 has limited non-STEM knowledge, lacks multimodal capabilities (e.g., image processing), has slower response times, and incurs higher computational costs. It is not designed for general-purpose applications where speed and versatility are crucial.
Ans. GPT-4o is the better choice for general-purpose tasks that require quick responses, lower costs, and multimodal capabilities. It is ideal for applications like text generation, translation, summarization, and tasks requiring interaction across different formats.