OpenAI now have a large number of models and it can get confusing as to what the difference is and what the best use case is for each one. In this post I’ll go through these and try to cut through some of the confusion!
Latest Popular OpenAI Models
GPT-4.1
This is the latest top performing model, which was launched in April 2025. It has a very large context – meaning it could process entire books or all the code for app. This means it’s great for large data analysis and coding co-pilots to help with software development.
o4-mini
This model is a more affordable reasoning model, which balances capability and efficiency. Due to its lower cost, it’s good to use for price sensitive applications.
o3
This is a reasoning model that spends additional time reflecting. It’s the most powerful reasoning model available and it’s great for deep research tasks.
GPT-4o
This is an ‘omni’ model, which can respond to text, audio and images. This can be used for vision enabled chat, voice conversations and interactive agents.
GPT-4o mini
This fast and affordable model. It is included in the free version of ChatGPT. This is a light weight version of GPT-4o and it suitable for everyday chat and other more cost sensitive applications.
How to Access Popular OpenAI Models
| Model | ChatGPT Free | ChatGPT Plus/Pro/Team | API |
|---|---|---|---|
| GPT 4.1 | No | No | Yes |
| o4-mini | No *NOTE 1 | Yes *NOTE2 | Yes |
| o3 | No | Yes *NOTE2 | Yes |
| GPT 4o | Yes *NOTE 1 | Yes *NOTE2 | Yes |
| GPT 4o-mini | Yes | Yes | Yes |
*NOTE 1 = Limited use
*NOTE 2 = On Plus and team plan this has ‘standard’ usage. On Pro it has unlimited, but usage must be reasonable.
Key AI Model Terminology
Tokens
A token is the basic unit of text that AI models process. It might be a single character (“a”, “,”), part of a word (“ing” in “thinking”), or a short whole word (“dof”, “AI”). There are 3 types:
- Input Tokens – When you send a prompt, for example “Explain gravity” – the model first breaks that prompt into input tokens. Everything you send (the question, system instructions, prior conversation) counts toward your input token total.
- Cached Input Tokens – To save time and compute, models often cache (remember) the tokenised form of earlier parts of the conversation or document. Instead of re‑tokenising or re‑processing the same text each time, the model re‑uses these cached representations. Caching speeds up responses and helps keep multi‑turn chats efficient.
- Output Tokens – After reading the input tokens, the model generates new tokens as its response. Each word, punctuation mark, or sub‑word piece it writes is an output token. If you ask for a 100‑word summary, you might consume roughly 100 output tokens (plus a little overhead).
Context
Context refers to all the tokens the model has “in mind” when generating its next response. This includes your prompt plus any prior conversation. A larger context window (measured in tokens) means the model can remember more of what you’ve said earlier, which helps it keep track of long documents or multi‑turn dialogues.
Modality
Modality is a type of data: text, image, audio, or others. Text‑only models handle the text modality. Image‑generation models handle the image modality. Some advanced models (e.g. ‘omni’ models) can work across multiple modalities – e.g. understanding an image and then writing about it.
OpenAI API Pricing
OpenAI API pricing for the popular models as of April 2025.
| Model | Context | Pricing – Input | Pricing – Cached Input | Pricing – Output |
|---|---|---|---|---|
| GPT 4.1 | 1,047,576 | $2.00 | $0.50 | $8.00 |
| o4-mini | 200,000 | $1.10 | $0.28 | $4.40 |
| o3 | 200,000 | $10.00 | $2.50 | $40.00 |
| GPT 4o | 128,000 | $2.50 | $1.25 | $10.00 |
| GPT 4o-mini | 128,000 | $0.15 | $0.08 | $0.60 |
So o3 is the most expensive model by a significant margin.
Main OpenAI Model Types
The 4 main models OpenAI offer are reasoning models, flagship chat models, cost-optimised models and realtime models.
Reasoning Models
These are specialised for logic, math, code understanding, and step‑by‑step problem solving. They tend to produce more accurate, meticulously reasoned outputs – ideal for technical tasks, exams, or debugging code.
Flagship Chat Models
These are the go‑to, general‑purpose conversational AIs. They balance creativity, knowledge, and safety, and are tuned to follow instructions, maintain context, and produce human‑like dialogue.
Cost-optimised Models
Designed to be more affordable for high‑volume use, they trade a bit of raw capability for much lower per‑token cost. Great for applications that need to process a lot of text but don’t require the absolute best performance (e.g. logging analysis, bulk summarisation).
Realtime Models
These prioritise speed, delivering responses with minimal delay. They’re optimised so that interactive applications – chat widgets, live assistants – feel instantaneous, even on slower devices or networks.
Other OpenAI Model Types
OpenAI also has a number of more specialised models.
Image Generation Models
Given a text prompt, these generate original images. You might use them to create illustrations, concept art, or design mockups on the fly.
Text-To-Speech Models
They convert written text into spoken audio. You can power voice assistants, read‑aloud features, or dynamic audio narratives with them.
Transcription Models
These turn spoken audio into written text. Useful for captions, meeting transcripts, or voice‑driven interfaces.
Tool-Specific Models
These models are fine‑tuned to work with particular built in tools.
Embeddings Models
They map text (or other data) into high‑dimensional vectors. These vectors capture semantic meaning and enable tasks like similarity search, clustering, or recommendation.
Moderation Models
Built to detect and filter unwanted content – hate speech, explicit material, harassment. You run your user‑generated text through them to flag or block policy‑violating messages before they reach your app.
Choosing the Model
When deciding which OpenAI model to use, match your task’s needs against each model’s strengths:
- Accuracy vs. Cost
- For critical tasks needing high accuracy or complex reasoning (legal analysis, code debugging, exam questions), choose a Reasoning Model or Flagship Chat Model.
- For bulk processing where perfect accuracy isn’t essential (data tagging, mass summarisation), a Cost‑optimised Model can save you money.
- Speed Requirements
- If your application demands instant responses (live chat, interactive games), pick a Realtime Model.
- For non‑urgent batch jobs, throughput‑oriented models work fine.
- Modality Needs
- If you need images, audio, or transcription, select the corresponding specialised model (Image Generation, Text‑to‑Speech, or Transcription).
- For multimodal tasks (e.g. analysing an image then describing it) use a model that supports multiple modalities.
By balancing these factors – accuracy, cost, speed, modality – you can pick the model that best fits your project’s goals.
