If you’ve ever tried figuring out all of OpenAI’s models and APIs but got lost halfway through, you’re not alone. This blog breaks it all down in a simple way.

Language Models

Let’s start with GPT-4.5. It’s the most powerful model in OpenAI’s lineup right now. It takes both, text and images, as input and responds with text. You can use it through chat completions, responses, assistants, or batch endpoints. It supports everything from structured outputs and streaming to function calling, making it the best choice if you want maximum capability.

Then there’s GPT-4o, the flagship model. It’s cheaper than GPT-4.5 but still extremely smart. The “o” stands for “omni,” meaning it can handle text, image, and even audio inputs in some cases. It supports fine-tuning, distillation, predicted outputs, and more. Think of it as the most versatile all-rounder that’s also cost-effective.

If you’re working on something more focused, GPT-4o-mini is the better choice. It’s faster, more affordable, and perfect for lightweight tasks. It still accepts text and image inputs and supports streaming, function calling, and fine-tuning. While it’s slightly less intelligent than the bigger models, it performs well for everyday use cases.

For those who need strong reasoning, there’s o3-mini. This model is optimized for deep thought and decision-making. It only works with text (no images or audio) and is ideal for tasks where logic and structure are more important than multimodal inputs.

There’s also o1, another high-level reasoning model. It works with both text and images and is designed to think before it answers, generating a longer internal thought chain before responding. It’s not as fast as o3-mini, but it’s very detailed and accurate.

Image & Audio Models

DALL-E 3 is OpenAI’s image generation model. You give it a text prompt, and it creates visuals out of that. It’s ideal for design, art, or creative use cases.

Images can also be given as input and an inference can be generated from that image.

messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
                },
            },
        ],
    }],

For audio, GPT-4o Transcribe handles speech-to-text. It works in real-time or as a batch transcription tool, which is perfect for notes, interviews, or accessibility. On the flip side, GPT-4o mini TTS does the opposite. It takes text and turns it into natural-sounding speech, letting you build voice interfaces or generate audio from written content.

| API                  | Supported modalities              | Streaming support          |
|----------------------|-----------------------------------|----------------------------|
| Realtime API         | Audio and text inputs and outputs | Audio streaming in and out |
| Chat Completions API | Audio and text inputs and outputs | Audio streaming out        |
| Transcription API    | Audio inputs                      | Audio streaming out        |
| Speech API           | Text inputs and audio outputs     | Audio streaming out        |

Other Tools & APIs

OpenAI also offers omni-moderation; a moderation model that checks for harmful or unsafe content in both text and images. It’s a backend tool for keeping user content clean and compliant.

If you’re building search, recommendation, or NLP features, the text-embedding-3-small model is needed. It converts text into embeddings (numeric vectors) that help measure similarity between pieces of text. This is the backbone of semantic search and retrieval.

API Core Concepts

The Chat Completions API is the building block for OpenAI’s conversational tools. You send in a list of messages (with roles like system, user, and assistant), and it gives you a smart reply. It works with text, images, and audio inputs and can return either text or audio responses.

"messages": [
      {
        "role": "developer",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Hello!"
      }
    ]

The newer Responses API builds on chat completions but adds memory and state. Unlike the basic API where you have to keep track of past messages manually, Responses remembers and adapts. It can also perform tasks like file search, web browsing, and more.

Then there’s the Assistants API. It lets you create an actual assistant that has access to tools like the code interpreter, file search, and function calling. Assistants can use multiple models at once and store memory across conversations, making them feel a lot more persistent and intelligent.

Key Features

Here are some of the key features that Open AI offers. Structured outputs let you format responses in clean JSON, which is super helpful for programming tasks. Function calling enables the model to trigger predefined functions based on the input. Streaming allows the output to start displaying as soon as it’s generated, which feels faster and smoother. Fine-tuning is a game changer if you have custom data. It lets you teach the model to better suit your domain. Distillation is a clever trick where you use the output of a big model to teach a smaller one, cutting down on cost and response time without sacrificing quality. You can also batch requests to save money and get higher throughput, which is great for processing large datasets or background tasks. Lastly, prompt generation inside the Playground helps you create smart inputs and function definitions just by describing your task.

Evaluations & Retrieval

OpenAI also supports evaluations to help you measure how well your models are performing. This is useful when you’re building production apps and need reliable quality. Retrieval allows the model to look up your data using embeddings by giving AI the ability to “read” the knowledge base.

That concludes the overview and a rough guide on OpenAI’s products. Hopefully this helped you understand and select which API works best for your use case