OpenAI's o1 Model: A Deep Dive into Its Pros and Cons

In recent years, AI models from major companies have been attracting significant attention and often sparking controversy. One such development is OpenAI’s o1 model, which has already garnered both supporters and critics. In this article, we’ll delve into why o1 is generating mixed opinions and assess whether the model is worth the investment its creators claim.

About the Model

OpenAI’s o1 model has attracted attention for its claimed ability to “think” before providing a final answer. This process is based on the “Chain of Thought” (CoT) method. The AI breaks down complex tasks into steps and analyzes them sequentially. This approach helps the model solve problems more effectively and accurately, creating the impression of a thoughtful approach to answers. Complex tasks are now processed with greater depth, which is a step forward in AI development. However, this technique is far from new.

Transparency Issues

Complaints about OpenAI’s creation stem from a lack of transparency. Users were given only general descriptions of the model—key technical details were not disclosed. This left many wondering about the true innovativeness of o1, with critics claiming the company is creating a sense of uniqueness in its advertising. The technologies embedded in the model have been known for a long time.

The Chain of Thought method was previously used, including in GPT-4. In fact, any user with access to the OpenAI API or other major models can create their own version of CoT by configuring reasoning chains in code. This raises questions about the model's claimed revolutionary nature: it essentially applies existing techniques without any significant innovations.

User Experience

User examples confirm this point of view. In attempts to question the model about its operation, they received limited answers and even threats of a ban. Even those who didn’t try to “get under the skin” of the AI encountered similar issues.

Advantages of o1

Despite the criticism, o1 does have its advantages. Whether the underlying technology is unique or not, it allows AI to generate more than just final conclusions. The neural network thinks step-by-step about each stage of the solution, enabling it to better handle more complex problems. For example, in mathematics or programming, the model gradually unfolds each stage of the calculation.

Imagine a task—solving an equation—where it is necessary not only to get the result but also to calculate all intermediate values. The o1 model, when using Chain of Thought, offers step-by-step:

1. Discussion of variables.
2. Formation of equations.
3. Step-by-step solution.
4. Verification at each stage.

As a result, users can verify the correctness of the calculations by checking the data of each step.

Learning and Adaptability

Unlike simpler models that use only pre-trained data, o1 actively learns using complex reinforcement techniques. This makes it more adaptable to new tasks and improves the quality of intermediate conclusions. However, even this technology has limitations: too long chains can ultimately lead to distortions, causing the model to lose the thread of reasoning. Fortunately, such problems occur less frequently than in simpler AI systems.

Alternatives to o1

With GPT and Python, users can create their own alternative to the “unique model.” The first step is to set up the reasoning chain on which o1 is based. GPT already supports this functionality; users just need to train the model to ask itself intermediate questions or steps to solve the task.

This sounds complicated, but it’s quite realistic, and there are no limitations on the number of requests. Many users complain that o1 is limited to 30–50 prompts per week.

For those seeking simplicity without coding, existing AI with extensive functionality is a viable option. For instance, Jadve AI allows users to create reports, analyze data, or automate marketing campaigns without programming. Prompts for various tasks are already loaded into the neural network, covering everything from SEO to psychology.

User Feedback on o1

The model is currently available only to Plus and Team owners, who have tested it on various tasks. A notable test was the classic “strawberry” challenge, which checks how well the model handles basic text recognition tasks—specifically, counting the number of “r” letters in the word “strawberry.”

Performance Issues

While the GPT-4o model struggles with this task due to its token-based processing, the o1-preview model succeeds. It utilizes a multi-processor analysis system, which operates like a “GPT manager.” Different modules within the system handle tasks sequentially, ensuring accurate results.

However, not all reviews of the model are enthusiastic. Users working on creative tasks have expressed dissatisfaction, as the o1 model does not support image generation and struggles with creative writing tasks. Many wonder if it's worth investing in a platform with limitations when other neural networks offer more versatile tools.

Speed Concerns

Another disappointment is the speed of operation. The o1-preview model’s step-by-step processing can make it slow, with some users reporting wait times of up to a minute for responses, which is inconvenient when deadlines are tight.

Pros and Cons of o1

Advantages

• Accuracy in solving complex tasks: Excels at programming and scientific research through a step-by-step process.
• Step-by-step analysis: Processes large amounts of information effectively, making it useful for complex calculations.

Disadvantages

• Slow performance: Takes a long time to process solutions.
• Limited creative capabilities: Unsuitable for generating text, ideas, or images.
• Limited functionality: Lacks features present in other neural networks, such as memorization and web browsing.

Comparison of o1 with Other Models

o1 vs GPT-4

GPT-4 is more versatile and faster for simple tasks. It supports text and image generation, while the o1 model focuses on accurate logical tasks.

o1 vs GPT-4o

While GPT-4o demonstrates speed and versatility, it can struggle with complex logical tasks. In contrast, o1 excels in analyzing complex problems but does so at a slower pace.

o1 vs Jadve AI

Jadve AI focuses on simplicity and multifunctionality, offering ready-made solutions without the need for deep technical knowledge. This makes it ideal for marketers and content managers who need fast, versatile tools.

o1 vs Other LLMs (e.g., Claude or LLaMA)

Many modern language models strike a balance between speed and accuracy. Claude offers improved context understanding, while LLaMA is faster but less accurate in specialized tasks compared to o1.

Conclusion

The o1 model is a significant achievement in AI, with its approach to analysis and information processing making it a powerful tool. However, its slow operation and limited functionality restrict its overall usefulness. As it stands, o1 is not a universal solution for all tasks, and until OpenAI refines the technology, users may find more accessible platforms to be better suited for their needs.