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In the forthcoming sections, we will provide an extensive overview of various GPT models.

• GPT-4

GPT-4 is a comprehensive multimodal model that accepts text input and produces text output. Future extensions should also enable the processing of image inputs. For many basic tasks, the difference between GPT-4 and GPT-3.5 models is not significant. However, in more complex reasoning situations, GPT-4 is far more powerful than any of the previous models. Similar to its predecessor GPT-3.5-Turbo, GPT-4 is optimized for chat.

• GPT-4: This is the base model of GPT-4 and is more powerful than any GPT-3.5 model. It is optimized for chat and can handle complex tasks. It has been updated with the latest model iterations and can handle up to 8,192 tokens. It was trained with data up to September 2021.
• GPT-4-0314: This variant is a snapshot of GPT-4 as of March 14, 2023. Unlike the base GPT-4, this model will not receive updates and will be discontinued 3 months after a new version is released. It can handle up to 8,192 tokens and was trained with data through September 2021.
• GPT-4-32k: Similar to the base GPT-4, this model is optimized for chat and can handle more extended context. It provides 4x longer context length, allowing it to handle up to 32,768 tokens. Like the base model, it was trained with data up to September 2021.
• GPT-4-32k-0314: This is a snapshot of GPT-4-32k as of March 14, 2023. Similar to GPT-4-0314, this model will not receive updates and will be discontinued 3 months after a new version is released. It can handle up to 32,768 tokens and was trained with data through September 2021.

• GPT-3

GPT-3, or Generative Pre-trained Transformer 3, is a state-of-the-art language model developed by OpenAI. It represents one of the most advanced examples of natural language processing (NLP) technology as of my last knowledge update in September 2021. Here are some key details about GPT-3:

• davinci: This was one of the largest and most powerful variants of GPT-3, with 175 billion parameters. It was capable of handling a wide range of natural language understanding and generation tasks.
• curie: Curie was another model size, smaller than davinci but still highly capable. It had a lower number of parameters, making it more cost-effective for certain applications.
• babbage: Babbage was a smaller model variant of GPT-3, suitable for more budget-conscious applications or those with less demanding language processing requirements.
• gpt-3.5-turbo: GPT-3.5-turbo was a versatile model with 175 billion parameters, designed for a broad range of tasks. It was known for its ability to perform well in various natural language processing tasks.
• Multilingual Models: OpenAI also released GPT-3 models fine-tuned for specific languages or language families, such as Spanish, Chinese, and more. These models were tailored for better performance in their respective languages.
• Fine-Tuned Models: Apart from the base models, developers and organizations had the option to fine-tune GPT-3 models for specific applications. This allowed customization for tasks ranging from content generation to chatbot development.