OpenAI’s o1-preview vs o1-mini: A Step Forward to AGI

Introduction

On September 12th, OpenAI released an update titled “Learning to Reason with LLMs.” They introduced the o1 model, which is trained using reinforcement learning to tackle complex reasoning tasks. What sets this model apart is its ability to think before it answers. It generates a lengthy internal chain of thought before responding, allowing for more nuanced and sophisticated reasoning. The release of a new series of OpenAI models clearly shows that we can move forward one step at a time towards Artificial General Intelligence (AGI). The most awaited time when AI can potentially match the reasoning capabilities of humans is here!

With OpenAI’s new model, o1-preview and o1-mini, the benchmark for efficiency and performance in AI language models has been set. These models are expected to push the boundaries in terms of speed, lightweight deployment, reasoning abilities, and resource optimization, making them more accessible for a wide range of applications. If you haven’t used them yet, don’t fret; we will compare both o1-preview and o1-mini models to provide you with the best option.

Checkout the comparison of OpenAI o1 models and GPT 4o.

Overview

• OpenAI’s o1 model uses reinforcement learning to tackle complex reasoning tasks by generating a detailed internal thought process before responding.
• The o1-preview model excels in deep reasoning and broad-world knowledge, while the o1-mini model focuses on speed and STEM-related tasks.
• o1-mini is faster and more cost-efficient, making it ideal for coding and STEM-heavy tasks with lower computational demands.
• o1-preview is suited for tasks requiring nuanced reasoning and non-STEM knowledge, offering a more well-rounded performance.
• The comparison between o1-preview and o1-mini helps users choose between accuracy and speed based on their specific needs.

o1-preview vs o1-mini: The Purpose of Comparison

Comparing o1-preview and o1-mini aims to understand key differences in capabilities, performance, and use cases between these two models. 

• Comparing these helps determine the trade-offs between size, speed, and accuracy. Users may want to know which model suits specific applications based on the balance between resource consumption and performance.
• To understand which model excels in tasks requiring high accuracy and which is better for faster, possibly real-time applications.
• To evaluate whether certain tasks, like natural language understanding, problem-solving, or multi-step reasoning, are better handled by one model.
• This comparison helps developers and organizations choose the right model for their specific needs, such as whether they need raw power or a model that can function in limited computational environments.
• To assess how each model contributes to the broader goal of AGI development. For example, does one model demonstrate more sophisticated emergent behaviors indicative of AGI, while the other focuses on efficiency improvements?

Also read: o1: OpenAI’s New Model That ‘Thinks’ Before Answering Tough Problems

OpenAI’s o1-preview and o1-mini: An Overview

Note: Recently, OpenAI has increased the rate limits for o1-mini for Plus and Team users by 7x – from 50 messages per week to 50 messages per day. For o1-preview, the rate limit is increased from 30 to 50 weekly messages. I hope there will be more customization in the usage.

The o1 series models appear to be a range of AI models optimized for different use cases, with the following key distinctions between the two specific variants you mentioned:

o1-Preview

• Most capable model in the o1 series: This variant is likely designed to handle complex tasks that require deep reasoning and advanced understanding. It may excel in areas like natural language understanding, problem-solving, and offering more nuanced responses, making it suitable for scenarios where depth and accuracy are prioritized over speed or efficiency.
• Enhanced reasoning abilities: This suggests that the model can perform tasks involving logical deduction, pattern recognition, and possibly even inference-based decision-making better than other models in the o1 series. It could be well-suited for applications in research, advanced data analysis, or tasks that require sophisticated language comprehension, such as answering complex queries or generating detailed content.

o1-Mini

• Faster and more cost-efficient: This version is optimized for speed and lower computational resource usage. It likely trades off some advanced reasoning capabilities in exchange for better performance in situations where quick responses are more important than depth. This makes it a more economical option when large-scale usage is necessary, such as when handling many requests in parallel or for simpler tasks that don’t require heavy computation.
• Ideal for coding tasks: The o1-Mini appears to be tailored specifically for coding-related tasks, such as code generation, bug fixing, or basic scripting. Its efficiency and speed make it a good fit for rapid iteration, where users can generate or debug code quickly without needing to wait for complex reasoning processes.
• Lower resource consumption: This means the model uses less memory and processing power, which can help reduce operational costs, especially in large-scale deployments where multiple instances of the model may be running concurrently.

Also read: How to Build Games with OpenAI o1?

o1-preview vs o1-mini: Reasoning and Intelligence of Both the Models

Mathematics

• o1-mini: Scored 70.0% on the AIME (American Invitational Mathematics Examination), which is quite competitive and places it among the top 500 U.S. high school students. Its strength lies in reasoning-heavy tasks like math.
• o1-preview: Scored 44.6% on AIME, significantly lower than o1-mini. While it has reasoning capabilities, o1-preview doesn’t perform as well in specialized math reasoning.

Winner: o1-mini. Its focus on STEM reasoning leads to better performance in math.

Also read: 3 Hands-On Experiments with OpenAI’s o1 You Need to See

STEM Reasoning (Science Benchmarks like GPQA)

• o1-mini: Outperforms GPT-4o in science-focused benchmarks like GPQA and MATH-500. While o1-mini doesn’t have as broad a knowledge base as o1-preview, its specialization in STEM allows it to excel in reasoning-heavy science tasks.
• o1-preview: Performs reasonably well on GPQA, but it lags behind o1-mini due to its more generalized nature. o1-preview doesn’t have the same level of optimization for STEM-specific reasoning tasks.

Winner: o1-mini. Its specialization in STEM reasoning allows it to outperform o1-preview on science benchmarks like GPQA.

Coding (Codeforces and HumanEval Coding Benchmarks)

• o1-mini: Achieves an Elo of 1650 on Codeforces, which places it in the 86th percentile of competitive programmers, just below o1. It performs excellently on the HumanEval coding benchmark and cybersecurity tasks.
• o1-preview: Achieves 1258 Elo on Codeforces, lower than o1-mini, showing weaker performance in programming and coding tasks.

Winner: o1-mini. It has superior coding abilities compared to o1-preview.

Also read: How to Access the OpenAI o1 API?

o1-preview vs o1-mini: Model Speed

• o1-mini: Faster across the board. In many reasoning tasks, o1-mini responds 3-5x faster than GPT-4o and o1-preview. This speed efficiency makes it an excellent choice for real-time applications requiring rapid responses.
• o1-preview: While o1-preview has strong reasoning skills, its speed is slower than o1-mini, which could be a limiting factor in applications needing quick responses.

Winner: o1-mini. Its performance-to-speed ratio is much better, making it highly efficient for fast-paced tasks.

o1-preview vs o1-mini: Human Preference Evaluation

• o1-mini: Preferred by human raters over GPT-4o for reasoning-heavy, open-ended tasks. It demonstrates better performance in domains requiring logical thinking and structured problem-solving.
• o1-preview: Similarly, o1-preview is also preferred to GPT-4o in reasoning-focused domains. However, for more language-focused tasks that require a nuanced understanding of broad-world knowledge, o1-preview is more well-rounded than o1-mini.

Winner: Tied. Both models are preferred over GPT-4o in reasoning-heavy domains, but o1-preview holds an edge in non-STEM language tasks.

Also read: OpenAI’s o1-mini: A Game-Changing Model for STEM with Cost-Efficient Reasoning

o1-preview vs o1-mini: Safety and Alignment

Safety is critical in deploying AI models, and both models have been extensively evaluated to ensure robustness.

• o1-mini: Highly robust in handling challenging harmful prompts, outperforming GPT-4o and showing excellent performance on jailbreak safety (both human-sourced and [email protected] jailbreak eval).
• o1-preview: Performs almost identically to o1-mini on safety metrics, demonstrating excellent robustness against harmful completions and jailbreaks.

Winner: Tied. Both models perform equally well in safety evaluations.

Limitations of o1-preview and o1-mini

Non-STEM Knowledge

• o1-mini: Struggles in non-STEM factual tasks, such as history, biographies, or trivia. Its specialization on STEM reasoning means it lacks broad-world knowledge, leading to poorer performance in these areas.
• o1-preview: Performs better on tasks requiring non-STEM knowledge due to its more balanced training that covers broader world topics and factual recall.

STEM Specialization

• o1-mini: Excels in STEM reasoning tasks, including mathematics, science, and coding. It is highly effective for users seeking expertise in these areas.
• o1-preview: While capable in STEM tasks, o1-preview doesn’t match o1-mini’s efficiency or accuracy in STEM fields.

o1-preview vs o1-mini: Cost Efficiency

• o1-mini: Offers comparable performance to o1 and o1-preview on many reasoning tasks while being significantly more cost-effective. This makes it an attractive option for applications where both performance and budget matter.
• o1-preview: Though more general and well-rounded, o1-preview is less cost-efficient than o1-mini. It requires more resources to operate due to its broader knowledge base and slower performance on certain tasks.

Winner: o1-mini. It’s the more cost-efficient model, providing excellent reasoning abilities at a lower operational cost.

Conclusion

• o1-mini is ideal for users who need a highly efficient, fast model optimized for STEM reasoning, coding, and quick response times, all while being cost-effective.
• o1-preview is better suited for those who require a more balanced model with broader non-STEM knowledge and robust reasoning abilities in a wider range of domains.

The choice between o1-mini and o1-preview largely depends on whether your focus is on specialized STEM tasks or more general, world-knowledge-driven tasks.

The o1-preview model likely serves as a more robust, full-featured option aimed at high-performance tasks. At the same time, the o1-mini focuses on lightweight tasks, catering to use cases where low latency and minimal computational resources are essential, such as mobile devices or edge computing. Together, they mark a significant step forward in the quest for scalable AI solutions, setting a new standard in both accessibility and capability across industries.

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Pankaj9786 19 Sep, 2024

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.