Models

The OpenAI API is powered by a diverse set of models with different capabilities and price points. You can also make customizations to our models for your specific use case with fine-tuning.

Visit our model index for researchers to learn more about which models have been featured in our research papers and the differences between model series like InstructGPT and GPT-3.5.

With the release of gpt-3.5-turbo, some of our models are now being continually updated. gpt-3.5-turbo, gpt-4, and gpt-4-32k point to the latest model version. You can verify this by looking at the response object after sending a ChatCompletion request. The response will include the specific model version used (e.g. gpt-3.5-turbo-0613).

We also offer static model versions that developers can continue using for at least three months after an updated model has been introduced. With the new cadence of model updates, we are also giving people the ability to contribute evals to help us improve the model for different use cases. If you are interested, check out the OpenAI Evals repository.

The following models are the temporary snapshots, we will announce their deprecation dates once updated versions are available. If you want to use the latest model version, use the standard model names like gpt-4 or gpt-3.5-turbo.

Learn more about model deprecation on our deprecation page.

GPT-4 is a large multimodal model (accepting text inputs and emitting text outputs today, with image inputs coming in the future) that can solve difficult problems with greater accuracy than any of our previous models, thanks to its broader general knowledge and advanced reasoning capabilities. Like gpt-3.5-turbo, GPT-4 is optimized for chat but works well for traditional completions tasks using the Chat completions API. Learn how to use GPT-4 in our GPT guide.

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.

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 using the Chat completions API but works well for traditional completions tasks as well.

We recommend using gpt-3.5-turbo over the other GPT-3.5 models because of its lower cost and improved performance.

GPT base models can understand and generate natural language or code but are not trained with instruction following. These models are made to be replacements for our original GPT-3 base models and use the legacy Completions API. Most customers should use GPT-3.5 or GPT-4.

DALL·E is a AI system that can create realistic images and art from a description in natural language. We currently support the ability, given a prompt, to create a new image with a certain size, edit an existing image, or create variations of a user provided image.

The current DALL·E model available through our API is the 2nd iteration of DALL·E with more realistic, accurate, and 4x greater resolution images than the original model. You can try it through the our Labs interface or via the API.

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. The Whisper v2-large model is currently available through our API with the whisper-1 model name.

Currently, there is no difference between the open source version of Whisper and the version available through our API. However, through our API, we offer an optimized inference process which makes running Whisper through our API much faster than doing it through other means. For more technical details on Whisper, you can read the paper.

Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Our second generation embedding model, text-embedding-ada-002 is a designed to replace the previous 16 first-generation embedding models at a fraction of the cost. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks. You can read more about our latest embedding model in the announcement blog post.

The Moderation models are designed to check whether content complies with OpenAI's usage policies. The models provide classification capabilities that look for content in the following categories: hate, hate/threatening, self-harm, sexual, sexual/minors, violence, and violence/graphic. You can find out more in our moderation guide.

Moderation models take in an arbitrary sized input that is automatically broken up to fix the models specific context window.

GPT-3 models can understand and generate natural language. These models were superseded 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.

Your data is your data.

As of March 1, 2023, data sent to the OpenAI API will not be used to train or improve OpenAI models (unless you explicitly opt in). One advantage to opting in is that the models may get better at your use case over time.

To help identify abuse, API data may be retained for up to 30 days, after which it will be deleted (unless otherwise required by law). For trusted customers with sensitive applications, zero data retention may be available. With zero data retention, request and response bodies are not persisted to any logging mechanism and exist only in memory in order to serve the request.

Note that this data policy does not apply to OpenAI's non-API consumer services like ChatGPT or DALL·E Labs.

For details, see our API data usage policies. To learn more about zero retention, get in touch with our sales team.