OpenAI's o1 Models: Do They Deliver on Their Promise?

What if an AI could take a moment to think before providing answers? OpenAI's latest o1 models, codenamed “Strawberry,” aim to do just that, but their performance raises questions about practicality and effectiveness. This article addresses the potential of these models, their pricing challenges, and the real-world applications they might serve.

Key Points of Discussion

• The unique reasoning capabilities of OpenAI o1
• The cost implications of utilizing these models
• Real-life examples illustrating o1's strengths and weaknesses

By exploring these elements, readers will gain insights into whether OpenAI o1 is truly a game-changer or simply another AI offering in a crowded market.

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Exploring OpenAI o1's Unique Features

OpenAI's latest model, o1, represents a shift in how users can interact with AI, allowing the model to “think” before delivering responses. This model, affectionately dubbed “Strawberry” within OpenAI, brings both excitement and skepticism to users. However, how does it really perform?

A Look at Multistep Reasoning

• Enhanced Problem Solving: OpenAI o1 is designed to deconstruct larger issues into manageable sub-steps, effectively providing a more thorough analysis.
• Existing Ideas: While this approach to reasoning isn’t brand new, advancements in technology have made it more implementable and practical.
• Industry Response: Experts like Kian Katanforoosh from Workera express enthusiasm about the potential for AI to perform systematic, backward reasoning, effectively helping users navigate complex topics.

Cost Considerations

OpenAI o1 comes with a heavier price tag than its predecessor, GPT-4o, which raises concerns about cost-effectiveness. The pricing model includes:

• Input and Output Tokens: Like previous models, you pay for the tokens processed.
• Reasoning Tokens: o1 employs an additional hidden process that requires substantial computational power, leading to the accumulation of what are termed “reasoning tokens.” This complexity means users must be deliberate to avoid excessive charges for simpler inquiries.

For example, simply asking where Nevada’s capital is may not justify the costs associated with using o1.

Practical Applications

The ability of o1 to “walk backwards from big ideas” proves beneficial in various scenarios. One practical example explored involved organizing a Thanksgiving dinner:

1. A user requested assistance in determining if two ovens would suffice for a gathering of 11 people.
2. After 12 seconds of processing, o1 generated a comprehensive response exceeding 750 words.
3. The model suggested that, with careful planning, two ovens would be adequate while considering related factors like costs and family time.

Compared to GPT-4o, which required numerous follow-up questions to provide similar advice, o1 delivered insights more effectively and succinctly.

Another scenario involved planning a hectic workday, with travel and multiple meetings. While o1 provided a well-thought-out plan, some users might find the level of detail overwhelming.

Managing Expectations

Initial excitement surrounding the release of Strawberry may have set expectations too high. The development of reasoning models in OpenAI has been a topic of discussion since late 2023, leading many to speculate about the potential for Artificial General Intelligence (AGI).

However, CEO Sam Altman clarified that o1 does not equate to AGI, indicating a desire for users to keep their aspirations in check. Altman acknowledged that while o1 has its merits, it has limitations that become apparent over time:

“o1 is still flawed, still limited, and it still seems more impressive on first use than it does after you spend more time with it.”

In light of this, the AI community is adjusting its expectations regarding the model's capabilities.

“The hype sort of grew out of OpenAI’s control,” noted Rohan Pandey of ReWorkd.

Many industry experts, including Mike Conover from Brightwave, share the view that while o1 may resolve certain complex tasks better than GPT-4, it does not represent a significant leap forward in AI technology:

“Everybody is waiting for a step function change for capabilities, and it is unclear that this represents that. I think it’s that simple.”

Latest Statistics and Figures:

• Usage Limits: Users of ChatGPT Plus and Team have access to 30 messages a week with OpenAI o1-preview and 50 messages a week with OpenAI o1-mini.
• Cost: The o1-preview model costs $15 for every 1 million input tokens and $60 for each million output tokens, while GPT-4o costs $5 per million input tokens and $15 per million output tokens.
• Performance Metrics: o1-preview achieved 83% accuracy in a Mathematics Olympiad test, compared to GPT-4o's 13%.
• Context Window: Both o1-preview and o1-mini have a 128k context window, with output limits of 32k and 64k respectively.

Historical Data for Comparison:

• Previous Model Performance: GPT-4o scored 22 on a safety test, while o1-preview scored 84, indicating significant improvement in safety adherence.

Recent Trends or Changes:

• Training Method: o1 models use reinforcement learning and a "chain of thought" approach, differing from previous models that replicated patterns in their training datasets.
• Availability: o1 models are currently available to ChatGPT Plus, Team, Enterprise, and Edu users, with plans to extend access to free users.

Relevant Economic Impacts or Financial Data:

• Cost Efficiency: o1-mini is 80% cheaper than o1-preview, making it a cost-effective option for applications requiring reasoning but not broad world knowledge.

Notable Expert Opinions or Predictions:

• CEO Sam Altman: Clarified that o1 does not equate to Artificial General Intelligence (AGI) and acknowledged its limitations.
• Bob McGrew: Expressed hope that o1 will be the beginning of more sensible naming conventions that better communicate their objectives to the public.
• Industry Experts: Views from experts like Mike Conover and Rohan Pandey suggest that while o1 resolves certain complex tasks better, it does not represent a significant leap forward in AI technology.

This comprehensive overview showcases the latest developments and insights regarding OpenAI's o1 models, highlighting their capabilities, cost implications, and expert recommendations.

Frequently Asked Questions

1. What are the unique features of OpenAI o1?

OpenAI o1, affectionately nicknamed “Strawberry”, introduces a significant change in user interaction with AI by allowing the model to “think” before providing responses. Key features of o1 include:

• Multistep Reasoning: It deconstructs complex problems into manageable parts for a more thorough analysis.
• Enhanced Problem-Solving: This model is particularly adept at systematic, backward reasoning.

2. How does OpenAI o1 handle multistep reasoning?

OpenAI o1 is designed to facilitate enhanced problem-solving by breaking larger issues into smaller, more manageable sub-steps. This approach allows for:

• Thorough Analysis: Users can expect deeper insights into the problems presented.
• Practical Applications: It assists users in navigating complex topics.

3. What are the cost implications of using OpenAI o1?

OpenAI o1 is priced higher than its predecessor, GPT-4o, raising cost-effectiveness concerns. The pricing model incorporates:

• Input and Output Tokens: Users pay for the tokens processed as with previous models.
• Reasoning Tokens: O1 requires substantial computational power, resulting in additional charges for reasoning tokens.

4. Can you provide a practical application example for OpenAI o1?

One practical example of o1's capabilities involves organizing a Thanksgiving dinner. The model:

1. Assessed a user's query about the adequacy of two ovens for 11 people.
2. Processed for 12 seconds and delivered a comprehensive response exceeding 750 words.
3. Recommended that two ovens would suffice with appropriate planning, considering costs and family time.

5. How does OpenAI o1 compare to GPT-4o in terms of response quality?

Compared to GPT-4o, OpenAI o1 has demonstrated more effective and succinct responses. While GPT-4o often necessitated follow-up questions to provide similar advice, o1 can:

• Deliver insights more promptly.
• Provide detailed information without requiring multiple exchanges.

6. What are the expectations for OpenAI o1's capabilities?

Initial excitement surrounding the release of o1 may have set expectations too high. Even though many believed it could lead to Artificial General Intelligence (AGI), CEO Sam Altman reassured users that:

• O1 does not represent AGI.
• While it boasts several merits, it still has inherent limitations.

7. How has the AI community responded to OpenAI o1?

The response from the AI community has been a mix of enthusiasm and skepticism. Experts, including Mike Conover from Brightwave, have remarked that while o1 may resolve certain complex tasks better than GPT-4, it does not represent a significant leap forward. Key points include:

• Some experts noted the hype exceeded practical improvements.
• Users should temper their expectations regarding o1's capabilities.

8. Can OpenAI o1 handle simple queries effectively?

OpenAI o1 may not be the best fit for simpler queries due to its operational complexity, which results in additional costs. For instance:

• Asking a basic question, such as the location of Nevada's capital, may not justify using o1 due to the computation involved.
• Users should be deliberate in their inquiries to avoid incurring excessive charges.

9. What is the feedback from industry experts about OpenAI o1?

Feedback from various industry experts has been cautious. Rohan Pandey from ReWorkd pointed out that much of the excitement grew beyond OpenAI’s control. General sentiments include:

• O1 may not represent a transformative advancement in AI.
• Expectations should be moderated based on real-world performance.

10. How does OpenAI plan to address the limitations of o1 in the future?

OpenAI acknowledges the flaws and limitations of o1, with Sam Altman stating that while impressive at first use, its limitations become clearer over time. Future improvements will likely focus on:

• Enhancing reasoning processes to reduce computational costs.
• Improving overall capabilities while managing user expectations.