ChatGPT Models Explained

Key takeaways

ChatGPT models are versions of the same underlying large language model technology, each optimized for trade-offs between speed, capability, and costs.

• In 2022, ChatGPT-3.5 became the first model to be widely released to the public [1]. 

• ChatGPT models differ in how well they can reason, follow instructions, and interpret inputs, making each better suited for specific types of tasks.

• You can access tailored ChatGPT models at different capacities through either Free, Plus, Pro, Business, or Enterprise-level memberships.

Learn more about what ChatGPT models are and how they function, how different versions compare, and how to choose the right one for your use case. To understand the technology in more detail, consider the IBM AI Foundations for Everyone Specialization. In as little as four weeks, you can develop a deep understanding of key concepts in artificial intelligence (AI) and explore emerging concepts in generative AI. Plus, by the end, you will have earned a career certificate from IBM to showcase on your professional profile.

What are ChatGPT models? 

ChatGPT models (GPT stands for Generative Pre-trained Transformer) are large language models, meaning they train on large amounts of text to predict and generate useful responses to user inputs. After this initial training, ChatGPT models undergo additional training to refine their output using reinforcement learning from human feedback (RLHF). This teaches them to produce responses that are helpful and contextually appropriate rather than just statistically accurate. 

To use ChatGPT, you first input text, often in the form of questions or instructions. Based on this input, the ChatGPT model responds conversationally by generating an answer, asking follow-up questions, challenging incorrect assumptions, or rejecting inappropriate requests. Because ChatGPT models are designed to mimic human interaction, they’re also capable of admitting mistakes and responding to your feedback to improve responses over time.

While all ChatGPT models operate on these fundamentals, they differ in their model sizes, training data, resource demands, and fine-tuning processes. For example, some earlier models focus primarily on text, while later models can interpret a wider range of input types, such as images or speech. As early models have been updated and expanded over time, modern GPT algorithms have become more capable of complex, multistep reasoning. Additionally, specific versions have been tailored to tackle different tasks. As more models emerge, different designs prioritize core functions such as speed, latency, input modality capabilities, instruction-following, and reasoning depth. As a user, exploring how different models function and how they may suit different tasks can help you take advantage of model differences and find the one best suited for your task.

What are the different types of ChatGPT models?

You can choose between several major model families, each with different purposes and availability. Deciding on the right one depends on your intended use case and whether you’d like to opt for a paid plan or prefer to stay with a free version. While not a comprehensive list, below are some of the most common models released over several generations by OpenAI.

GPT-3.5 and GPT-3.5 Turbo

GPT-3.5 was an older model built for general-purpose text and code generation. It was released to the public in 2022 [1]. It’s now considered a legacy model and is generally less efficient and capable than newer models. 

GPT-3.5 was able to complete text-based interactions of up to 3,000 words, solve basic problems, and communicate in multiple languages. GPT-3.5 Turbo was an extension of GPT-3.5, which developers optimized using the Chat Completions API to enhance communications. However, this early version lacked the ability to complete complex reasoning tasks or process images, which led to its replacement by newer GPT-4 models. 

GPT-4 and GPT-4 Turbo

GPT-4 extended GPT-3.5 capabilities by expanding content retention to 25,000 words, adding the ability to handle complex tasks, and incorporating image processing. This version increased parameters from 175 billion to one trillion, reflected in advanced processing abilities that led to demonstrated improvements across legal, technical, and creative tests. For example, this extended processing ability allowed GPT-4 models to pass the bar exam (with a score among top performers), while GPT-3.5 models performed near the bottom of the spectrum on the same exam.

As part of the GPT-4 series, OpenAI professionals introduced GPT-4 Turbo as a more efficient GPT-4 option, designed to be cost-effective while remaining suitable for tasks such as content generation, programming, image analysis, and code development. Rather than being a new generation of model, GPT-4 Turbo was an intermediate update to GPT-4 that included extended training data, an updated context window (i.e., capable of handling more complex instructions), and faster processing speeds.

GPT-4o and GPT-4o mini

GPT-4o (“omni”) extended GPT-4 models by adding the ability to process audio and video, including enhanced processing speed for multimedia. This design balanced performance with speed ahead of the release of GPT-4.l models.

As a smaller version of GPT-4o, the GPT-4o mini was a faster and more affordable option. Its creators intended it to focus on tasks and high-volume use, which made it great for simple content drafts, customer interactions, and quick reasoning tasks. Compared to other small models, GPT-4o mini outperformed in reasoning tasks, coding proficiency, and multimodal reasoning. 

ChatGPT models’ differences: Was GPT-4 Turbo or GPT-4o better?

For most users, GPT-4o offered faster, multimodal processing compared to GPT-4 Turbo without compromising performance. This means that GPT-4o provided similar quality with a faster response time. GPT-4o was especially effective at audio processing, retaining information, and improving auditory replies in scenarios where GPT-4 and GPT-3.5 versions showed limitations. Researchers developed the GPT-4o model to more naturally mimic human interactions compared to the GPT-4 Turbo model, with more flexibility in input and output types. 

GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano

GPT-4.1 models advanced upon GPT-4o models, with larger context windows and improved long-context comprehension. GPT-4.1 models specifically excelled at coding, web development tasks, and following precise instructions. 

To provide quick and affordable options for general-purpose modeling, OpenAI researchers created the GPT-4.1 mini and GPT-4.1 nano models. Both the GPT-4.1 mini and GPT-4.1 nano excelled in coding and instruction-following with lower computational intensity, offering fast and cost-effective responses to queries while maintaining high performance.

GPT-5 series

The GPT-5 series is the newest generation of OpenAI models, designed to go beyond GPT-4 capabilities to more effectively handle complex, multistep work, including longer reasoning chains, end-to-end task execution, and changes in context. As an improvement on GPT-4 models, GPT-5 has fewer hallucinations, responds within safety constraints, and works more efficiently across writing, coding, and health care domains. 

The GPT-5 series has several models, each optimized for different styles of tasks. GPT-5.4 is the latest version of GPT-5 (as of March 2026) and is currently the best model for advanced reasoning and logic, designed to generate complex code, build agentic tools, and work through problems with humanlike reasoning. Newer models, such as GPT-5.4 Pro, excel in research and innovative use cases, though they require additional time and resources compared to some legacy models. For more affordable reasoning and logic tasks, the GPT-5 mini and GPT-5 nano models operate similarly to prior generation mini and nano models, offering fast outputs that balance performance with resource constraints. 

While 5.4 models tend to work with most types of tasks, you can also find specific versions in the GPT-5 series, such as GPT-5.4 Codex for agentic coding or GPT-5.4 Thinking for long-context reasoning. As AI continues to develop, new models are likely to become available on the OpenAI GPT platform.

Which ChatGPT model should you choose?

Choosing the right GPT model relies on an understanding of the task types each model excels at, as well as your priorities when it comes to speed, performance, and resource demand. For many users, newer GPT-5 models offer deeper reasoning capabilities and more advanced functionality that stand out against legacy models. Review the GPT-5.4 models that you might opt for based on their everyday use cases:

• GPT-5.4: Excels at advanced reasoning, logic, and agentic tasks. Great for everyday tasks, long-context understanding, and code reviews.

• GPT-5.4 Thinking or Pro: These excel at deep research and advanced research questions, especially in professional settings. Early tests show thinking and reasoning similar to human performance. 

• GPT-5.4-Codex: Excels at advanced, multilayered coding and software engineering with agentic capabilities. Models have cybersecurity functionality.

• GPT-5 mini: Offers affordable reasoning and logic while maintaining high performance. This model is best for well-defined tasks and prompts.

While newer GPT-5 models typically provide the most advanced functionality, knowing what earlier models were optimized for can help you combine available models or interpret older comparisons and advice. However, it’s worth noting that many of these versions are either restricted to higher-tier memberships or will likely become unavailable in the future as OpenAI focuses on GPT-5 versions:

• GPT-4.5: Excels at improving writing, programming, and solving practical problems.

• GPT-4o: Excels at reasoning across multimedia inputs with a natural conversation flow.

• GPT-4.1: Excels at instruction-following, programming, and overall intelligence.

• GPT-4.1 mini: Excels at GPT-4.1 tasks with lower cost requirements. 

What’s the difference between free and paid ChatGPT plans?

As a free user, you can utilize the newer GPT-5.3 model a limited number of times within a five-hour window, while paid tiers have access to higher-use volume and additional models. “Plus” users, who pay a subscription fee of $20 per month, have expanded access to models like GPT-5.4 Thinking, video generation models, custom GPTs, agent mode, and the Codex agent, among other benefits. “Pro” users, who pay a subscription fee of $200 per month, can access capabilities like GPT-5.4 Pro, unlimited messages and uploads, extended video generation, the expanded Codex agent, and previews of new-generation features [2].

For Business and Enterprise-grade access, users have additional options to integrate with apps like Slack and Google Drive, expanded context windows, GPT-5.4 Thinking and Pro models, and advanced data privacy to support work with proprietary information.

How to improve your results with any ChatGPT model

To improve your results with ChatGPT models, it’s important to craft effective prompts. This is known as “prompt engineering,” a process of crafting specific input prompts that guide the GPT model to generate an effective and tailored response.

While learning to write effective prompts takes time and practice, some principles you can follow to improve your inputs include the following:

• Ensure your prompts are clear and provide appropriate context: This helps to communicate to the model what you are asking for.

• Define the desired output: If you’re looking for a specific response type, clearly communicate with the model the type of format and structure you’d like for your output. For example, you may want text outputs that are more formal, friendly, professional, or humorous. Or you may be looking for specific structures like bullet points, outlines, or narrative forms. You can guide the model to tailor the output to what you’d like. 

• Supply examples: If you have example code, document outlines, text, or data formats, you can more specifically guide the model to mimic the desired format. In some cases, this may be clearer than written instructions.

• Refine iteratively: After you receive your initial response, think carefully about what you’d like improved, and where the model may have misinterpreted your request. This can help you refine your request to generate a more aligned output.

What is the future of ChatGPT models?

While nobody knows exactly what the future of ChatGPT will look like, early GPT-5 models give an indication of what the next generation might hold. Based on GPT-5.4 priorities, you can likely expect to see continuing improvements in long-context understanding, better handling of multistep tasks, and more specialized models for specific tasks like coding, research, and creative writing. 

Another shift you’re likely to see is toward agentic behavior, with models that can plan and execute tasks independently, manage tools, and make decisions on their own. While GPT-5 takes steps in this direction, it’s still in early stages, and future models are likely to improve on this capability. 

As GPT models continue to evolve, one thing is likely to stay consistent with the previous years. Researchers and developers have the goal of maximizing AI’s benefit to the world, with an eye on applications that can help society and the technology grow together. 

Read more: Generative AI vs. Agentic AI: What Is the Difference? 

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