GPT-5 Explained: Build a No-Code AI Agent in Under 10 Minutes

The arrival of GPT-5 marks a significant milestone in artificial intelligence, potentially establishing it as one of the most powerful AI models ever developed. This article offers a comprehensive guide covering two main areas. First, we will explore the high-level aspects of GPT-5—what makes it important, how it stacks up against other leading models, and its core functionalities. Second, we will walk through a practical, step-by-step tutorial on creating a functional AI agent using GPT-5, all without writing a single line of code.

While the tutorial is designed to be accessible for those new to low-code platforms, even seasoned developers will find value in the initial overview. It provides crucial insights into what GPT-5 offers, its cost structure, and its operational mechanics—is it a single thinking model, a collection of smaller models, or something else entirely? Let's dive in.

GPT-5: API vs. Chat Interface

To start, it's essential to understand the distinction between using the GPT-5 API for building applications and interacting with GPT-5 within the ChatGPT interface, as there are some key differences.

The version of GPT-5 integrated into ChatGPT is known for its dynamic nature. It can provide quick responses or more thoughtful, in-depth answers depending on the user's prompt. For instance, you could instruct it to "really think about" a topic like AI agents, and it would take a longer duration to generate a more comprehensive response.

The API, however, offers a different structure. Instead of a single, adaptable model, developers can choose from three distinct sizes: - GPT-5: The large, powerful "thinking" model, ideal for complex and demanding tasks. - GPT-5 Mini: A smaller, cheaper, and faster model. - GPT-5 Nano: The smallest and most cost-effective option for quick, simple tasks.

This architecture is similar to the structure seen with previous models like GPT-4.1, where a powerful primary model is complemented by smaller, more efficient alternatives.

Furthermore, the GPT-5 API introduces several new parameters for developers to fine-tune: - Verbosity: A parameter to control the length of the response, allowing for either concise or detailed answers. - Reasoning Effort: A tunable knob that adjusts how much "thought" the model puts into generating a response, balancing speed and accuracy.

These three elements—the choice of multiple models, verbosity control, and adjustable reasoning effort—represent the primary advantages of using the API over the standard ChatGPT interface.

Breaking Down the Costs

Here’s a look at how GPT-5's pricing compares to other major models in the market. First, let's clarify some terms: - Tokens: Think of tokens as pieces of words. Roughly every word you input or receive is a token. - Input Cost: The price for the tokens you send to the model in your prompt. - Output Cost: The price for the tokens the model generates in its response.

Here are the costs per million tokens for the GPT-5 models:

• GPT-5 (The Big Model):

  • Input Cost: $1.25
  • Output Cost: $10.00

• GPT-5 Mini & Nano:

  • Input Cost: $0.05 (5 cents)
  • Output Cost: $0.40 (40 cents)

These prices are quite reasonable. The main GPT-5 model is cheaper than many competitors; for example, Gemini 2.5 Pro has double the input cost and a 50% higher output cost. It is also priced competitively against Grok 3 and is slightly more affordable than GPT-4o. The Mini and Nano versions are exceptionally cheap, making them excellent choices for production environments where cost-efficiency is crucial.

How Powerful Is GPT-5?

While marketing materials often show new models outperforming their predecessors, the real test is against current competitors like Gemini, Claude, and Grok.

Note: Benchmark numbers should always be taken with a grain of salt, as every company optimizes its models for these specific tests. However, assuming a level playing field, they provide a useful comparison.

GPT-5 consistently leads the pack or is a close runner-up in most benchmarks, particularly excelling in math and coding. One point to note is its context window, which at 256k tokens, is smaller than some competitors like Gemini, which boasts a million-token window. The practical value of an enormous context window is still debated, but it's a factor to consider.

For clarity, the context window is the total number of tokens (both input and output) the model can handle in a single interaction.

Overall, GPT-5 brings more power at a competitive price point, along with increased flexibility through its multiple model sizes and new API parameters.

Building a No-Code AI Agent with GPT-5

Now, let's move on to the practical part: building a no-code AI agent powered by GPT-5 using the n8n platform. This platform allows you to connect different services and APIs visually. You can get started with a free trial on their website to follow along.

Step 1: Create the AI Agent Foundation

In your n8n workflow, add your first step and search for the AI Agent node. This node will serve as the base for our agent.

Step 2: Select the GPT-5 Model

Click the plus (+) button on the model parameter. In the right-hand panel, select the OpenAI Chat Model. From the list of available models, you can choose from GPT-5 (the latest version by date), GPT-5-mini, or GPT-5-nano. For this demonstration, we'll select the main GPT-5 model.

Step 3: Connect Your OpenAI Account

You will need to connect your OpenAI account by providing an API key. 1. Click Create New Credential. 2. Go to the OpenAI website and navigate to the API section (note: the OpenAI API and ChatGPT are separate services with different billing). 3. If you haven't already, set up your billing information by going to Settings > Billing and adding a credit balance. A few dollars is enough to get started. 4. Go to API Keys and click Create new secret key. 5. Name your key, copy it, and keep it secure. 6. Return to n8n and paste the API key into the credential field. Save it.

Step 4: Add Memory to the Agent

To allow the agent to remember past interactions, add a memory component. Click the plus (+) button for Memory and select Simple Memory. The default context window length of five means the agent will remember the last five messages exchanged.

Step 5: Give the Agent Tools

What makes an AI agent truly powerful is its ability to use tools to perform actions. We'll add a tool to our agent. 1. Click the plus (+) button for Tools. 2. n8n has numerous native tools. For this example, we will select Gmail. 3. Connect your Google account by creating a new credential and following the documentation.

Step 6: Configure the Tool for AI Control

In the Gmail tool's settings, you will see fields for To, Subject, and Message. Next to these fields are star icons that say, "Let the model define this parameter." - Click the star icon for each of these fields. This action gives GPT-5 control over these parameters, allowing it to decide the recipient, subject, and body of the email based on your chat prompt.

Step 7: Test the AI Agent

Now you can interact with your agent. Open the chat window and give it a command: Send an email to [email protected] saying what's up. The agent will process this request, understand that it needs to use the Gmail tool, and send the email accordingly. Because we are using the most powerful GPT-5 model, this simple task might take a moment as the model "thinks" through the process. For simpler tasks like this, using the Mini or Nano model would be much faster and more efficient.

This example demonstrates how easily you can set up a sophisticated AI agent with powerful tools like GPT-5 without any coding. The true potential is unlocked when you add more tools, enabling the agent to handle increasingly complex and dynamic tasks on your behalf.