The AI That Thinks Before It Speaks

Getting to know Open AI's two new models

Author

Dan Vega
September 16, 2024 • Estimated Reading Time: 7 minutes

OpenAI has once again pushed the boundaries of artificial intelligence, but not in the way many expected. For months, the AI community has been buzzing with rumors about the potential unveiling of GPT-5, codenamed "Strawberry." The wait is finally over, but with a twist that's caught everyone by surprise.

Instead of releasing GPT-5, OpenAI has introduced an entirely new class of AI models: o1-preview and o1-mini. This bold move represents a significant shift in their approach to advancing AI capabilities. By creating a new series rather than simply incrementing the GPT number, OpenAI signals a fundamental leap in how these models operate and reason.

These new offerings represent significant advancements in AI reasoning capabilities, each tailored for specific use cases. The o1 series introduces integrated chain-of-thought reasoning, a feature that promises to revolutionize how AI models approach complex problems and tasks.

ChatGPT Plus and Team users will be able to access o1 models in ChatGPT today. Both o1-preview and o1-mini can be selected manually in the model picker, and at launch, weekly rate limits will be 30 messages for o1-preview and 50 for o1-mini. They are working to increase those rates and enable ChatGPT to automatically choose the right model for a given prompt.

Chain-of-Thought (CoT) Reasoning

The standout feature of both o1-preview and o1-mini is their built-in chain-of-thought reasoning. While this approach was possible with previous models, it often required explicit instructions and yielded inconsistent results.

If you take a look at Claude’s Prompt Engineering Guide they have a section on “Let Claude think (chain of thought prompting) to increase performance”

When faced with complex tasks like research, analysis, or problem-solving, giving Claude space to think can dramatically improve its performance. This technique, known as chain of thought (CoT) prompting, encourages Claude to break down problems step-by-step, leading to more accurate and nuanced outputs.

They also provide some advanced examples but we can try this out with a very basic prompt. At the end of our prompt we want to instruct the LLM to think step by step before performing whatever action we want it to perform.

Can you write a short story where the first and last word of each sentence is the same word? Please think step by step before writing your story.

Claude Response:

I'll approach this task step by step to create a compelling short story that meets the criteria you've specified.

Step 1: Choose a theme or setting for the story. I'll write about a mysterious forest to create an atmospheric and intriguing narrative.

Step 2: Brainstorm words that can work well as both the first and last word of a sentence. Some options: Shadows, Whispers, Trees, Silence, Time, Secrets, Darkness, Light, Leaves, Wind.

Step 3: Plan the basic structure of the story. I'll aim for 5-6 sentences to keep it short but allow for a complete narrative arc.

Step 4: Craft sentences that make sense both grammatically and narratively while adhering to the constraint.

Now, I'll write the short story:

Shadows crept through the ancient forest, concealing untold shadows. Whispers echoed among the gnarled branches, carrying forgotten whispers. Trees stood sentinel, their roots deeply intertwined with other trees. Silence reigned in the misty glades, broken only by an occasional, unsettling silence. Time seemed to flow differently here, bending and twisting like a river of time. Secrets lurked behind every moss-covered trunk, guarding age-old secrets.

This short story meets the criteria of having the first and last word of each sentence be the same, while still maintaining a coherent narrative about a mysterious forest.

This example demonstrates that current language models can perform complex reasoning tasks, though results may vary and often require some back-and-forth interaction.

With the o1 series, this powerful reasoning method is baked into the core functionality, operating behind the scenes to produce more thoughtful and accurate responses. This advancement marks a significant step forward in AI's ability to mimic human-like thought processes, potentially opening up new realms of application and problem-solving capabilities.

o1-preview: Advanced Reasoning for Complex Tasks

o1-preview represents a significant leap forward in AI reasoning capabilities. This model excels at complex problem-solving tasks, utilizing an integrated chain-of-thought process to approach problems with unprecedented thoroughness and nuance.

While o1-preview offers enhanced problem-solving abilities, users should be aware of certain trade-offs. The model's detailed reasoning process may result in longer response times and potentially higher token usage compared to other AI models. However, this additional processing typically yields more robust and well-considered answers.

It's important to note that o1-preview is not a replacement for GPT-4o, but rather a specialized tool designed for tasks that benefit from in-depth reasoning. Its ability to handle longer, more open-ended tasks with less need for input chunking sets it apart from previous models.

When to Use o1-preview

o1-preview shines in scenarios that require:

  • Detailed analysis of complex problems

  • Multi-step reasoning tasks

  • In-depth exploration of scientific or mathematical concepts

  • Thorough evaluation of hypothetical scenarios

o1-mini: Streamlined for Coding and Faster Processing

o1-mini is a compact powerhouse designed specifically for coding tasks. This model offers a perfect balance of speed and capability, making it an invaluable tool for developers and programmers. While smaller than o1-preview, o1-mini delivers impressive performance, particularly in STEM-related tasks.

One of o1-mini's standout features is its rapid processing speed. It provides quicker response times compared to o1-preview, enabling faster iteration and problem-solving in coding scenarios. This efficiency extends to resource usage as well, with o1-mini typically consuming fewer tokens than its larger counterpart, potentially resulting in cost savings for API users.

It's worth noting that while o1-mini excels in coding and STEM tasks, it has more limited general world knowledge compared to o1-preview. This specialization allows it to shine in its intended domain, offering unparalleled support for coding-related challenges.

When to Use o1-mini

o1-mini is best suited for:

  • Writing and debugging code

  • Solving programming challenges

  • Quickly generating code snippets or examples

  • Iterative development processes where speed is crucial

Comparing o1-preview and o1-mini

Feature

01-preview

01-mini

Primary Focus

General reasoning, scientific and math problems

Coding tasks

Processing

Slower, more thorough

Faster, optimized for coding

Token Usage

Potentially higher

Generally lower

Best Use Cases

Complete problem-solving, scientific analysis

Code generation, debugging, rapid development

Current Limitations

Both models have some limitations to be aware of:

  • Neither supports multimodal inputs (like images or audio).

  • Function calling is not available in this initial release.

Conclusion

OpenAI's o1-preview and o1-mini models represent exciting developments in the field of AI, offering enhanced reasoning capabilities that could revolutionize how we tackle complex problems and approach coding tasks. While each comes with its own trade-offs, the potential benefits in problem-solving, analysis, and code generation make them valuable tools for professionals working in various fields.

As with any new technology, it's important to understand both the capabilities and limitations of these models to make the most of their power. We look forward to seeing how developers, researchers, and problem-solvers will leverage these technologies to push the boundaries of what's possible with AI.

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