Azure OpenAI exposes babbage through model ID babbage. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-09-29. Next refresh: weekly.
Quick Start
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Code Examples
pip install openaiAZURE_OPENAI_API_KEYbabbagebabbage is your Azure deployment name, not the underlying model name. Deployment names are set when you deploy a model in Azure AI Foundry / Azure OpenAI Studio.
import os
from openai import AzureOpenAI
client = AzureOpenAI(
azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"], # e.g. https://{resource}.openai.azure.com
api_key=os.environ["AZURE_OPENAI_API_KEY"],
api_version="2024-02-01"
)
response = client.chat.completions.create(
model="babbage", # your deployment name
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Pricing on Azure OpenAI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.40 |
| Output tokens | $0.40 |
Capabilities
No model capability flags are currently sourced.
About babbage
The Babbage model is a large language AI model within the GPT-3 family, recognized for its speed and cost-effectiveness 569. Although it is not as capable as the Davinci model, it outperforms the Ada model in various capabilities 129. Ideal for simpler classification tasks and semantic searches, Babbage efficiently ranks document relevance to search queries 129. With an estimated 1.3 billion parameters 9, it requires less computational power compared to other models like Davinci, which has 175 billion parameters 9.