Azure OpenAI models

The service provides access to many different models, grouped by family and capability. A model family typically associates models by their intended task. The following table describes model families currently available in Azure OpenAI. Not all models are available in all regions currently. Please refer to the capability table at the bottom for a full breakdown.

Model family Description
GPT-3 A series of models that can understand and generate natural language.
Codex A series of models that can understand and generate code, including translating natural language to code.
Embeddings A set of models that can understand and use embeddings. An embedding is a special format of data representation that can be easily utilized by machine learning models and algorithms. The embedding is an information dense representation of the semantic meaning of a piece of text. Currently, we offer three families of Embeddings models for different functionalities: similarity, text search, and code search.

Model capabilities

Each model family has a series of models that are further distinguished by capability. These capabilities are typically identified by names, and the alphabetical order of these names generally signifies the relative capability and cost of that model within a given model family. For example, GPT-3 models use names such as Ada, Babbage, Curie, and Davinci to indicate relative capability and cost. Davinci is more capable (at a higher cost) than Curie, which in turn is more capable (at a higher cost) than Babbage, and so on.

Note

Any task that can be performed by a less capable model like Ada can be performed by a more capable model like Curie or Davinci.

Naming convention

Azure OpenAI's model names typically correspond to the following standard naming convention:

{family}-{capability}[-{input-type}]-{identifier}

Element Description
{family} The model family of the model. For example, GPT-3 models uses text, while Codex models use code.
{capability} The relative capability of the model. For example, GPT-3 models include ada, babbage, curie, and davinci.
{input-type} (Embeddings models only) The input type of the embedding supported by the model. For example, text search embedding models support doc and query.
{identifier} The version identifier of the model.

For example, our most powerful GPT-3 model is called text-davinci-003, while our most powerful Codex model is called code-davinci-002.

Older versions of the GPT-3 models are available, named ada, babbage, curie, and davinci. These older models do not follow the standard naming conventions, and they are primarily intended for fine tuning. For more information, see Learn how to customize a model for your application.

Finding what models are available

You can easily see the models you have available for both inference and fine-tuning in your resource by using the Models API.

Finding the right model

We recommend starting with the most capable model in a model family because it's the best way to understand what the service is capable of. After you have an idea of what you want to accomplish, you can either stay with that model or move to a model with lower capability and cost, optimizing around that model's capabilities.

GPT-3 models

The GPT-3 models can understand and generate natural language. The service offers four model capabilities, each with different levels of power and speed suitable for different tasks. Davinci is the most capable model, while Ada is the fastest. The following list represents the latest versions of GPT-3 models, ordered by increasing capability.

  • text-ada-001
  • text-babbage-001
  • text-curie-001
  • text-davinci-003

While Davinci is the most capable, the other models provide significant speed advantages. Our recommendation is for users to start with Davinci while experimenting, because it will produce the best results and validate the value our service can provide. Once you have a prototype working, you can then optimize your model choice with the best latency/performance balance for your application.

Davinci

Davinci is the most capable model and can perform any task the other models can perform, often with less instruction. For applications requiring deep understanding of the content, like summarization for a specific audience and creative content generation, Davinci produces the best results. The increased capabilities provided by Davinci require more compute resources, so Davinci costs more and isn't as fast as other models.

Another area where Davinci excels is in understanding the intent of text. Davinci is excellent at solving many kinds of logic problems and explaining the motives of characters. Davinci has been able to solve some of the most challenging AI problems involving cause and effect.

Use for: Complex intent, cause and effect, summarization for audience

Curie

Curie is powerful, yet fast. While Davinci is stronger when it comes to analyzing complicated text, Curie is capable for many nuanced tasks like sentiment classification and summarization. Curie is also good at answering questions and performing Q&A and as a general service chatbot.

Use for: Language translation, complex classification, text sentiment, summarization

Babbage

Babbage can perform straightforward tasks like simple classification. It’s also capable when it comes to semantic search, ranking how well documents match up with search queries.

Use for: Moderate classification, semantic search classification

Ada

Ada is usually the fastest model and can perform tasks like parsing text, address correction and certain kinds of classification tasks that don’t require too much nuance. Ada’s performance can often be improved by providing more context.

Use for: Parsing text, simple classification, address correction, keywords

Codex models

The Codex models are descendants of our base GPT-3 models that can understand and generate code. Their training data contains both natural language and billions of lines of public code from GitHub.

They’re most capable in Python and proficient in over a dozen languages, including C#, JavaScript, Go, Perl, PHP, Ruby, Swift, TypeScript, SQL, and even Shell. The following list represents the latest versions of Codex models, ordered by increasing capability.

  • code-cushman-001
  • code-davinci-002

Davinci

Similar to GPT-3, Davinci is the most capable Codex model and can perform any task the other models can perform, often with less instruction. For applications requiring deep understanding of the content, Davinci produces the best results. These increased capabilities require more compute resources, so Davinci costs more and isn't as fast as other models.

Cushman

Cushman is powerful, yet fast. While Davinci is stronger when it comes to analyzing complicated tasks, Cushman is a capable model for many code generation tasks. Cushman typically runs faster and cheaper than Davinci, as well.

Embeddings models

Currently, we offer three families of Embeddings models for different functionalities:

Each family includes models across a range of capability. The following list indicates the length of the numerical vector returned by the service, based on model capability:

  • Ada: 1024 dimensions
  • Babbage: 2048 dimensions
  • Curie: 4096 dimensions
  • Davinci: 12288 dimensions

Davinci is the most capable, but is slower and more expensive than the other models. Ada is the least capable, but is both faster and cheaper.

Similarity embedding

These models are good at capturing semantic similarity between two or more pieces of text.

Use cases Models
Clustering, regression, anomaly detection, visualization text-similarity-ada-001
text-similarity-babbage-001
text-similarity-curie-001
text-similarity-davinci-001

Text search embedding

These models help measure whether long documents are relevant to a short search query. There are two input types supported by this family: doc, for embedding the documents to be retrieved, and query, for embedding the search query.

Use cases Models
Search, context relevance, information retrieval text-search-ada-doc-001
text-search-ada-query-001
text-search-babbage-doc-001
text-search-babbage-query-001
text-search-curie-doc-001
text-search-curie-query-001
text-search-davinci-doc-001
text-search-davinci-query-001

Code search embedding

Similar to text search embedding models, there are two input types supported by this family: code, for embedding code snippets to be retrieved, and text, for embedding natural language search queries.

Use cases Models
Code search and relevance code-search-ada-code-001
code-search-ada-text-001
code-search-babbage-code-001
code-search-babbage-text-001

When using our Embeddings models, keep in mind their limitations and risks.

Model Summary table and region availability

GPT-3 Models

Model Supports Completions Supports Embeddings Base model Regions Fine-Tuning Regions
Ada Yes No N/A East US, South Central US, West Europe
Text-Ada-001 Yes No East US, South Central US, West Europe N/A
Babbage Yes No N/A East US, South Central US, West Europe
Text-Babbage-001 Yes No East US, South Central US, West Europe N/A
Curie Yes No N/A East US, South Central US, West Europe
Text-curie-001 Yes No East US, South Central US, West Europe N/A
Davinci* Yes No N/A East US, South Central US, West Europe
Text-davinci-001 Yes No South Central US, West Europe N/A
Text-davinci-002 Yes No East US, South Central US, West Europe N/A
Text-davinci-003 Yes No East US N/A
Text-davinci-fine-tune-002* Yes No N/A East US, West Europe

*Models available by request only. Please open a support request.

Codex Models

Model Supports Completions Supports Embeddings Base model Regions Fine-Tuning Regions
Code-Cushman-001* Yes No South Central US, West Europe East US, South Central US, West Europe
Code-Davinci-002 Yes No East US, West Europe N/A
Code-Davinci-Fine-tune-002* Yes No N/A East US, West Europe

*Models available for Fine-tuning by request only. Please open a support request.

Embeddings Models

Model Supports Completions Supports Embeddings Base model Regions Fine-Tuning Regions
text-similarity-ada-001 No Yes East US, South Central US, West Europe N/A
text-similarity-babbage-001 No Yes South Central US, West Europe N/A
text-similarity-curie-001 No Yes East US, South Central US, West Europe N/A
text-similarity-davinci-001 No Yes South Central US, West Europe N/A
text-search-ada-doc-001 No Yes South Central US, West Europe N/A
text-search-ada-query-001 No Yes South Central US, West Europe N/A
text-search-babbage-doc-001 No Yes South Central US, West Europe N/A
text-search-babbage-query-001 No Yes South Central US, West Europe N/A
text-search-curie-doc-001 No Yes South Central US, West Europe N/A
text-search-curie-query-001 No Yes South Central US, West Europe N/A
text-search-davinci-doc-001 No Yes South Central US, West Europe N/A
text-search-davinci-query-001 No Yes South Central US, West Europe N/A
code-search-ada-code-001 No Yes South Central US, West Europe N/A
code-search-ada-text-001 No Yes South Central US, West Europe N/A
code-search-babbage-code-001 No Yes South Central US, West Europe N/A
code-search-babbage-text-001 No Yes South Central US, West Europe N/A

Next steps

Learn more about Azure OpenAI.