This post is a simple and helpful breakdown of three key AI model types: OpenAI’s o1 model series, GPT-4o, and Small language models (SLMs). It explains how they serve different purposes, from powering copilots that assist us in decision-making to helping AI agents complete tasks autonomously.
I’ll explain when and why you should use each model and how they can drive value for your organization. Let’s go.
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As using AI actively at work is still new to most, I want to quickly explain the difference between copilots and agents before diving into the model comparison:
Copilots: AI assistants that help users work by providing suggestions, completing tasks, or enhancing productivity.
Agents: Autonomous AI systems that can perform tasks or jobs on behalf of a user without direct intervention
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To simplify your decision-making process, I've created a concise comparison table that highlights the key characteristics of the three AI models: GPT-4o, o1-preview, and Small Language Models (SLMs). The table breaks down each model's features, operational mechanisms, and ideal applications in business contexts. In simple terms, you can use AI to assist you in your work or use AI as autonomous agents that complete tasks for you.
By examining these side by side, you can quickly identify which model best aligns with your organization's goals—be they speed and creativity, accuracy, or data privacy.
| Category | GPT-4o | o1-preview | Small Language Model |
|---|---|---|---|
| When to use it | When speed and creativity are important | When answer correctness is crucial | When data privacy and personal intelligence are important |
| Operational mechanism | Autoregressive, single-path generation | Chain-of-Thought reasoning (CoT); evaluates multiple paths before answering | Lightweight processing optimized for efficiency |
| Response Speed | Fast responses through immediate generation | Slower due to multi-path evaluation | Fast, optimized for low-resource environments |
| Resource and cost efficiency | Cheaper and faster due to efficiency improvements | More tokens; higher cost and memory demand due to CoT | Optimized for low inference cost and battery usage |
| Model architecture | Autoregressive transformer model | Utilizes CoT reasoning to enhance correctness | Emphasizes security and efficiency in training and inference |
| Scalability and performance | Efficient across scales due to optimized architecture | Performance improves with more computational resources | Designed for efficient inference on low-resource devices |
| Paradigm shift | Advances in efficiency and creativity in language models | Introduces multi-path reasoning, transforming problem-solving | Makes AI accessible on personal devices with privacy |
| Application in Copilots | Enhances user productivity with quick, creative assistance | Provides highly accurate support through thorough reasoning | Offers private, on-device assistance respecting user data |
| Application in Agents | Ideal for fast, real-time autonomous workflows | Suitable for tasks requiring accurate decision-making, despite slower responses | Optimal for privacy-focused tasks on personal devices |
After reading through the comparisons, it might be helpful to elaborate a bit more on the application of Copilots and Agents:
Applications in Copilot
Applications in Agents
Understanding any given technology to a certain level is a prerequisite for overcoming the main barriers organizations typically face when adopting AI.
The GPT model series, e.g., GPT-4o**,** stands out for its speed and creativity, making it ideal for applications that demand quick, innovative responses. The o1 model series emphasizes accuracy through its Chain-of-Thought reasoning, which is suitable for tasks where precision is non-negotiable. Small Language Models (SLMs) prioritize data privacy and efficiency, offering solutions tailored for environments where resource constraints and security are paramount.