GPT : Generative Pre-trained Transformer | Expand |
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- With broad general knowledge and domain expertise, GPT-4 can follow complex instructions in natural language and solve difficult problems with with greater accuracy.
is more creative and collaborative than ever before. It can generate, edit, and iterate with users on creative and technical writing tasks, such as composing songs, writing screenplays, or learning a user’s writing style. - Following the research path from GPT, GPT-2, and GPT-3, the deep learning approach leverages more data and more computation to create increasingly sophisticated and capable language models
6 months were spent making GPT-4 safer and more aligned. GPT-4 is 82% less likely to respond to requests for disallowed content and 40% more likely to produce factual responses than GPT-3.5 on our internal evaluations. Price list for GPT-4 (Multiple models, each with different capabilities and price points. Prices are per 1,000 tokens. You can think of tokens as pieces of words, where 1,000 tokens is about 750 words.) | Model | Prompt | Completion | | 8K context | $0.03 / 1K tokens | $0.06 / 1K tokens | | 32K context | $0.06 / 1K tokens | $0.12 / 1K tokens |
GPT-4 models | LATEST MODEL | DESCRIPTION | MAX TOKENS | TRAINING DATA |
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| gpt-4 | More capable than any GPT-3.5 model, able to do more complex tasks, and optimized for chat. Will be updated with our latest model iteration. | 8,192 tokens | Up to Sep 2021 | | gpt-4-0314 | Snapshot of gpt-4 from March 14th 2023. Unlike gpt-4, this model will not receive updates, and will only be supported for a three month period ending on June 14th 2023. | 8,192 tokens | Up to Sep 2021 | | gpt-4-32k | Same capabilities as the base gpt-4 mode but with 4x the context length. Will be updated with our latest model iteration. | 32,768 tokens | Up to Sep 2021 | | gpt-4-32k-0314 | Snapshot of gpt-4-32 from March 14th 2023. Unlike gpt-4-32k, this model will not receive updates, and will only be supported for a three month period ending on June 14th 2023. | 32,768 tokens | Up to Sep 2021 |
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 much more capable than any of our previous models. - Limitation:
GPT-4 is currently in a limited beta and only accessible to those who have been granted access. In order to use this API we need to join the waitlist to get access when capacity is available. |
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| GPT-3.5 models can understand and generate natural language or code. Our most capable and cost effective model in the GPT-3.5 family is gpt-3.5-turbo which has been optimized for chat but works well for traditional completions tasks as well. | LATEST MODEL | DESCRIPTION | MAX TOKENS | TRAINING DATA |
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| gpt-3.5-turbo | Most capable GPT-3.5 model and optimized for chat at 1/10th the cost of text-davinci-003. Will be updated with our latest model iteration. | 4,096 tokens | Up to Sep 2021 | | gpt-3.5-turbo-0301 | Snapshot of gpt-3.5-turbo from March 1st 2023. Unlike gpt-3.5-turbo, this model will not receive updates, and will only be supported for a three month period ending on June 1st 2023. | 4,096 tokens | Up to Sep 2021 | | text-davinci-003 | - Can do any language task with better quality, longer output, and consistent instruction-following than the curie, babbage, or ada models. Also supports inserting completions within text.
- Text-davinci-003 has the following improvements:
- It produces higher-quality writing. This will help your applications deliver clearer, more engaging, and more compelling content.
- It can handle more complex instructions, meaning you can get even more creative with how you make use of its capabilities now.
- It’s better at longer-form content generation, allowing you to take on tasks that would have previously been too difficult to achieve.
Because of Davinci's new features, it will need more computing power, which means that each API call will cost more than with older models. The API can be accessed for $0.0200 per 1000 tokens. Prices are per 1,000 tokens. You can think of tokens as pieces of words, where 1,000 tokens are about 750 words.
| 4,097 tokens | Up to Jun 2021 | | text-davinci-002 | Similar capabilities to text-davinci-003 but trained with supervised fine-tuning instead of reinforcement learning | 4,097 tokens | Up to Jun 2021 | | code-davinci-002 | Optimized for code-completion tasks Now deprecated | 8,001 tokens | Up to Jun 2021 |
GPT-3.5 series is a series of models that was trained on a blend of text and code from before Q4 2021. The following models are in the GPT-3.5 series: code-davinci-002 is a base model, so good for pure code-completion taskstext-davinci-002 is an InstructGPT model based on code-davinci-002text-davinci-003 is an improvement on text-davinci-002gpt-3.5-turbo-0301 is an improvement on text-davinci-003, optimized for chat
Recommendation to use gpt-3.5-turbo over the other GPT-3.5 models because of its lower cost. Experimenting with gpt-3.5-turbo is a great way to find out what the API is capable of doing. After you have an idea of what you want to accomplish, you can stay with gpt-3.5-turbo or another model and try to optimize around its capabilities. Note: OpenAI models are non-deterministic, meaning that identical inputs can yield different outputs. Setting temperature to 0 will make the outputs mostly deterministic, but a small amount of variability may remain. 
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| title | text-davinci-002 vs text-davinci-003 |
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| - Enhanced Capabilities of davinci-text-003
The main difference between davinci-text-002 and davinci-text-003 is that the latter has been trained on a larger dataset, allowing it to generate more accurate results. Additionally, text-davinci-003 can better understand natural language instructions and produce more detailed responses. It also has improved capabilities for summarizing long documents and generating coherent paragraphs from multiple sources. Furthermore, OpenAI claims that text-davinci-003 can generate longer content with greater clarity, and engagement.
- Strengths and Weaknesses of Each Model
The output from davinci-text-002 was a series of short, concise sentences that were easy to read and understand. On the other hand, davinci-text-003 produced longer, more complex sentences with more intricate language. Both outputs had their own unique style and could be used for different purposes depending on the context. It is clear that both algorithms have their strengths and weaknesses when it comes to text generation. Note: ‘003 takes longer than the previous version to process prompts. |
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| - GPT-3 models can understand and generate natural language.
- These models were superceded by the more powerful GPT-3.5 generation models.
- However, the original GPT-3 base models (
davinci, curie, ada, and babbage) are current the only models that are available to fine-tune. - Fine-tuning mean to build and train our own data from one of GPT-3 Models
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Comparative Table:   
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