Last refreshed 2026-05-10. Next refresh: weekly.
Why use GPT-3.5 Turbo on Azure OpenAI?
Azure OpenAI offers GPT-3.5 Turbo with pay-as-you-go pricing at $0.50/1M input tokens. Azure OpenAI Service hosts OpenAI's GPT-4o, GPT-4, GPT-3.5, and embedding models on Microsoft Azure with enterprise SLAs.
Compare GPT-3.5 Turbo across 6 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport AZURE_OPENAI_API_KEY=...import os
from openai import AzureOpenAI
client = AzureOpenAI(
azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"], # e.g. https://{resource}.openai.azure.comgpt-3.5-turboRequest example
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="gpt-3.5-turbo", # your deployment name
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- gpt-3.5-turbo 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.
- The examples expect AZURE_OPENAI_API_KEY; rename it only if your application config maps the new variable.
Compare GPT-3.5 Turbo Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Azure OpenAI | $0.50 | $1.50 |
| OpenAI API | $0.50 | $1.50 |
| Salesforce Einstein Generative AI | — | — |
| OpenRouter | $0.50 | $1.50 |
| Replicate API | $0.50 | $1.50 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.50 |
| Output tokens | $1.50 |
Capabilities
About GPT-3.5 Turbo
GPT-3.5 Turbo is an advanced language model developed by OpenAI, showcasing significant advancements over GPT-3 and GPT-3.5. As the engine behind the popular ChatGPT application, it excels in tasks like text generation, translation, question answering, summarization, and code generation. This model employs Reinforcement Learning from Human Feedback (RLHF) to enhance accuracy and produce policy-optimized responses. Despite its prowess, it has a knowledge cutoff of September 2021 and can demonstrate biases from its training data. Occasionally, it may generate incorrect or nonsensical content, known as "hallucination," and is sensitive to input phrasing variations.