Welcome back! In our last session, you got your hands on a Large Language Model (LLM) for the first time, sending prompts and observing how it generates responses token by token. This gave you a feel for the basic mechanics of interacting with a single model.
Today, we'll explore a crucial concept for any AI product builder: not all models are created equal. You'll learn how to compare the responses from different LLMs—like OpenAI's GPT-4o, Anthropic's Claude 3.5 Sonnet, and Meta's Llama—when given the exact same prompt. Understanding their unique strengths and weaknesses is the first step toward choosing the right "brain" for the AI products you'll build.
Why Do Models Behave Differently?
You might wonder why models from different companies give such varied answers. It's because they are built using different "recipes." These recipes include the vast amounts of text and code they were trained on, their underlying architecture, and the specific fine-tuning methods used to shape their behavior.
Think of it like different DJs developing their unique styles. Hernán Cattaneo's progressive house sets are built from a different library and mixing philosophy than Carl Cox's high-energy techno sets. Both are masters, but you'd choose them for different moments on the dance floor. The same is true for LLMs.
This image provides a high-level look at the development pipelines for three major model families.
Don't worry about understanding every detail. The key takeaway is that each company follows a distinct path. For example, notice Anthropic's "Constitutional AI" step. This is a unique technique where the model is aligned with a set of principles (a "constitution") to make it safer and more helpful, which often results in a more cautious and explanatory tone in Claude's responses. These different training philosophies are what give each model its distinct "personality."
Observing the Differences: A Head-to-Head Comparison
Before you run your own tests, let's watch a quick comparison to see what kinds of differences to look for. The following video tests GPT-4o against Claude 3.5 Sonnet on several practical tasks.
GPT-4o VS Claude 3.5 Sonnet - Which AI is #1?
This video from Skill Leap AI provides a great practical showdown between two of the most popular models. Watching these clips will help you spot the nuances in each model's output.
Watch these two short segments: Code Generation: Pay attention to how Claude uses a special "artifacts" feature to create a live, interactive dashboard directly in the chat, while GPT-4o takes a different, less direct approach. This shows how some models have unique, task-specific capabilities. Content Creation: Here, both models are asked to turn a YouTube script into a social media post. Notice the significant difference in quality and tone. One response is ready to use, while the other is generic and unappealing. This highlights how models can vary in their creative and marketing abilities.
As you saw, the differences aren't just subtle; they can dramatically affect the utility of the output for a specific task.
Your Turn: Experiment in a Multi-Model Playground
The most effective way to understand these differences is to experience them yourself. For this, we'll use a tool called a "multi-model playground" that lets you send one prompt to several LLMs at once.
We will use OpenRouter, which is like a universal remote for hundreds of LLMs. It lets you easily compare models side-by-side. The video below gives a quick tour.
How to use Openrouter (Access Every LLM At Once)
This video from TheAIGRID quickly demonstrates how to get started with OpenRouter and use its core comparison feature.
First, watch the setup to see how to create an account and add credits. OpenRouter is extremely cheap; adding just $1 will be more than enough for all the experiments in this course. It's a "pay-as-you-go" system, not a subscription. Next, watch the comparison. This is the main event. It shows how to add multiple models to a chat and get simultaneous responses to your prompt.
Activity: Run Your Own Comparison
Now it's time for you to be the judge.
- Go to OpenRouter.ai, create an account, and add $1 of credits.
- Navigate to the Chat interface.
- Click "Add model" and select three popular choices. I recommend:
OpenAI: GPT-4o(the latest from OpenAI)Anthropic: Claude 3.5 Sonnet(the latest from Anthropic)Meta: Llama 3 70B Instruct(a powerful open-source model)
- Now, send the following prompts to all three models and compare their responses.
Prompt 1 (Business/Creative):
I'm writing a proposal for my event planning company to organize a tech product launch party. The client is a startup launching a new AI app. Suggest 3 unique, tech-themed entertainment ideas for the event. For each idea, provide a brief description and explain why it would appeal to a tech-savvy audience.
Prompt 2 (Technical/DJ Interest):
I use a Pioneer DJ controller. I want to create a JavaScript snippet that takes a list of tracks, each with a BPM value, and sorts them from slowest to fastest to help me plan my set. The input should be an array of objects, and the output should be the sorted array. Please provide the code and a brief explanation.
As you examine the outputs, look for:
- Format: Does one use bullet points? Code blocks? Does one have better structure?
- Tone: Is the language professional, creative, casual, or cautious?
- Content Quality: Are the event ideas genuinely creative? Is the JavaScript code correct and well-explained?
- Verbosity: Is the answer concise and to the point, or does it add extra, conversational text?
(Note: If you prefer not to create an account, you can use a free tool like thisorthis.ai for a similar, though more limited, experience.)
Conclusion
Great work today. You've moved beyond interacting with a single AI and have started thinking like an AI product builder—evaluating different components to find the best fit for the task at hand.
Key Takeaways:
- Different LLMs (GPT, Claude, Llama, etc.) respond differently to the same prompt due to their unique training data, architectures, and alignment methods.
- Some models excel at specific tasks like coding, while others might be better for creative writing or formal analysis.
- Multi-model playgrounds like OpenRouter are invaluable tools for efficiently testing and comparing LLMs to find the best one for your use case.
In our next lesson, we will investigate another critical difference between models: their context window. You'll test how much information a model can "remember" in a single conversation and see what happens when you push it to its limits.