AI as a System (Part 2): What AI Models Actually Are
The Engines: Why GPT, Claude, and Others Feel Different
This is Part 2 of the AI as a System series.
See the full series here.
In the last post, we established that AI is a stack made up of models, tools, and agents. This post focuses on the foundation of that stack: models.
What a Model Actually Is
A model is not an app, an interface, or something you interact with directly.
A model is a system trained to predict the next piece of information based on context.
For language models, that means taking input text, predicting the next word or phrase, and repeating that process many times to produce a complete response. At scale, this mechanism powers everything from chatbots to code generation.
Large Language Models (LLMs) vs Machine Learning (ML)
Machine learning is a broad field focused on building systems that learn patterns from data to make predictions without being explicitly programmed. These systems are used across a wide range of tasks, including fraud detection, forecasting, and recommendation systems.
Large language models are a specialized subset of machine learning. They are trained on large amounts of text data and designed specifically to understand and generate language by predicting the next token. This makes them well-suited for tasks like writing, summarization, and conversation.
AI is Advanced Pattern Prediction
AI models do not understand meaning in the way humans do. They do not have intent or awareness. They identify and reproduce patterns.
Because these models are trained on massive datasets, the patterns they learn can resemble reasoning, creativity, or judgment. The behavior can feel intelligent, even though the underlying mechanism is statistical.
Why Models Feel So Different From Each Other
Although models operate on similar principles, they can feel very different in practice.
Training data plays a major role. The data a model is trained on influences its tone, knowledge, and strengths. Training approach also matters. Techniques like reinforcement learning, human feedback, and fine-tuning shape how the model behaves. Context window is another key factor.
A context window is the maximum amount of text, measured in tokens, that a model can process and reference at one time.
A larger context window allows the model to consider more information at once, which improves its ability to reason across longer inputs. When the limit is exceeded, earlier parts of the conversation may be lost. This is why long threads can sometimes lead to the model forgetting earlier details.
The Major Models (What You’re Actually Using)
Different models are optimized for different strengths.
GPT (OpenAI)
- Strong general-purpose performance with consistent reasoning and structure
- Large ecosystem and broad support across tools
Claude (Anthropic)
- Particularly strong in coding and long-context tasks
- Handles large inputs well
Gemini (Google)
- Strong multimodal capabilities
- Tight integration with Google services
Grok (xAI)
- Designed with real-time data access
- More conversational tone
The Model Is Not the Tool
When comparing AI tools, it is easy to mix up layers. The tool is the interface you interact with, while the model is the underlying engine.
The same model can feel very different depending on how it is wrapped, what context it receives, and what actions it is allowed to take.
Why Model Choice Matters
Model choice directly affects output quality.
Stronger models tend to require fewer corrections, handle edge cases more effectively, and produce more structured responses. Weaker models often need more guidance and are more likely to miss important details or generate incorrect information.
The Beginning of Multi-Model Thinking
Instead of relying on a single model for every task, it is often more effective to choose models based on what you are trying to accomplish.
Model routing is the practice of selecting the most appropriate AI model for each task instead of using a single model for everything.
For example, smaller and faster models are often a better fit for simple or high-volume tasks. More capable models are better suited for complex reasoning. Some models are stronger for coding, while others are better for real-time information.
This approach reflects a shift toward treating models as components within a system rather than as a single solution.
What’s Next
Now that the role of models is clear, the next step is to look at how the interface layer shapes the experience. This is where the same model can feel completely different depending on the tool you are using.