What’s the difference between GPT-3 5, 4, 4 Turbo, 4o? OpenAI LLMs explained.

At the time of this writing, you can only access GPT-4 and Turbo with the paid subscription. As GPT-4 rumors fly around NeurIPS 2022 this week in New Orleans (including whispers that details about GPT-4 will be revealed there), OpenAI has managed to make plenty of news in the meantime.

GPT-4 Turbo

The performance of “4o” is identical to “Turbo” for text and code reasoning, which translates quicker and at a cheaper cost in the API. In addition, there’s the less talked about GPT-3.5 Turbo, which introduced various improvements over the original release. This version of the language model was released in preparation for the GPT-4 release, and it allowed developers to customize the model for different use cases and run these models at scale. This language mode includes improvements in natural language understanding and generation. Compared to GPT -3, version 3.5 offers better coherence, relevance, and contextual understanding and can more accurately handle more complex instructions than its predecessor. In the case of GPT-4o, we also have to consider the ability to process video to generate responses almost as quickly as a human can process similar information.

OpenAI CEO Sam Altman calls language interfaces a “big deal”

This version has the ability to process both text and images, opening up a world of possibilities for understanding and generating content involving visuals. That said, ChatGPT is an early demo, and in its blog post OpenAI detailed its “limitations,” including the fact that sometimes answers are plausible-sounding but incorrect or nonsensical. It’s worth pointing out that the ability to process images refers to using images as input, since OpenAI also includes the DALL-E model to create AI images from text prompts. Compared to GPT-3.5, it can better understand the context, perform complex reasoning tasks, provide more accurate responses, and generate more human-like text. Currently, OpenAI offers four GPT versions for developers as well as for consumers using ChatGPT, including GPT-3.5, 4, 4 Turbo, and 4o, and here’s an overview of all of them to understand their differences.

OpenAI CEO Sam Altman calls language interfaces a “big deal”

OpenAI recently launched its newest large language model, GPT-4o, but with so many versions now available, it’s getting confusing to distinguish between them as they all understand and generate text responses. However, the differences lie in the accuracy, speed, cost, and specific features each model offers. In this guide, I’ll give you an overview of the differences between OpenAI’s large language models so you can have a basic understanding of the available versions. OpenAI refers to this version as the most human-like experience, since it can process audio almost as fast as the human response time during a conversation.

OpenAI debuts ChatGPT and GPT-3.5 series as GPT-4 rumors fly

It’s important to note while OpenAI has offered other versions of its models, I’m focusing this guide on the flagship models currently available to more users, which include GPT-3.5 and higher releases up to 4o. OpenAI is making GPT-4o the default language model for ChatGPT, but it has limitations in the number of prompts you can input per day. GPT-4o is the latest version of the language model from OpenAI, which became available in May 2024. It’s important to note that this is GPT-4″ o,” not GPT-4 “0” or “4.0.” It’s an “o” for “omni.”

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• One of the limitations of the model is that it consumes more computing resources, which translates into higher costs for the company to maintain.
• This language mode includes improvements in natural language understanding and generation.
• In this guide, I’ll give you an overview of the differences between OpenAI’s large language models so you can have a basic understanding of the available versions.
• GPT-4o is the latest version of the language model from OpenAI, which became available in May 2024.

Although this version is similar to GPT-4 and provides a balance in performance and cost, some trade-offs could make this model less accurate in certain tasks. Some of the improvements include faster response times, making it suitable for applications requiring quick interactions, and using fewer resources, making it more accessible for various applications. This release was a revision of the original GPT-4 that provides similar performance, but it’s not as computationally intensive as the original release, meaning it lowers the operational cost. One of the limitations of the model is that it consumes more computing resources, which translates into higher costs for the company to maintain.