OpenAI provides several pre-trained language models that can be used for different tasks. The available models are:
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GPT (Generative Pre-trained Transformer) models:
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text-davinci-002: The most capable GPT model, with 2.7 billion parameters. -
text-davinci: A smaller version oftext-davinci-002, with 1.4 billion parameters. -
text-curie: A medium-sized GPT model, with 1 billion parameters. -
text-babbage: A smaller GPT model, with 400 million parameters. -
text-ada: A small GPT model, with 1.2 million parameters.
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Codex models:
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davinci-codex: A model based on GPT-3 that can generate code, natural language text, and blend the two together. -
davinci: A smaller version ofdavinci-codex, with fewer parameters.
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Other models:
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davinci-instruct-beta: A model for generating instructional text. -
content-filter-alpha-1: A model for detecting and filtering unsafe or inappropriate content.
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All of these models are designed to be general-purpose, but some are better suited for certain tasks than others. For example, the GPT models are good for generating natural language text, while the Codex models are better suited for generating code and blending natural language with code. The choice of which model to use will depend on the specific task you want to accomplish. For example, if you want to build a chatbot or a language-based assistant, one of the GPT models might be a good choice, while if you want to generate code snippets or automate programming tasks, the Codex models might be a better fit.
OpenAI Language Models and Their Use Cases
First, it's important to identify the specific use case for your app. OpenAI provides several pre-trained language models, each with its own strengths and weaknesses. For example, GPT models are best suited for generating natural language text, while Codex models are better for generating code and blending natural language with code. Understanding the requirements of your app will help you choose the best model for the job.
Once you've identified the most suitable model, it's important to consider the cost. OpenAI provides a free tier for their API, which allows for a limited number of requests per month. However, if you need more than the free tier allows, you will need to purchase API credits.
| Model Name | Cost per Token | Best Use Case | Short Description |
|---|---|---|---|
davinci |
$0.0006 | General | Powerful, versatile model for many use cases. |
curie |
$0.0005 | NLP | Best for natural language processing tasks. |
babbage |
$0.0004 | Translation | Optimized for machine translation. |
ada |
$0.0003 | Language Gen. | Specialized for natural language generation. |
davinci-codex |
$0.002 | Programming | Trained on programming-related content. |
davinci-instruct-beta |
$0.0012 | Instruction | Best for generating instructional content. |
davinci-codex-beta |
$0.001 | Programming | Improved version of davinci-codex. |
davinci-codex-csharp |
$0.0012 | C# | Trained specifically on C# language content. |
davinci-codex-java |
$0.0012 | Java | Trained specifically on Java language content. |
davinci-codex-python |
$0.0012 | Python | Trained specifically on Python content. |
text-davinci-002 |
$0.006 | Advanced Gen. | Most capable natural language generation model. |
The cost of API credits varies depending on the specific model you are using. The GPT models are generally more expensive than the Codex models, with text-davinci-002 being the most expensive. The cost per token for text-davinci-002 is currently 0.0009 per token. The cost for Codex models is generally lower, with
davinci-codex being the most expensive at $0.002 per token.
It's also worth noting that OpenAI periodically adjusts their pricing and may introduce new pricing tiers, so it's important to check their website for the most up-to-date information.
In summary, when choosing the right OpenAI language model for your app, consider the specific use case, as well as the cost associated with the model.
