---
title: "Replacing OpenAI ChatCompletion with Completion()"
description: "* [Supported OpenAI LLMs](https://docs.litellm.ai/docs/providers/openai) * [Supported Azure OpenAI LLMs](https://docs.litellm.ai/docs/providers/azure)"
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/azure-openai
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:08:09.213Z
license: CC-BY-4.0
attribution: "Replacing OpenAI ChatCompletion with Completion() — Claudary (https://claudary.paisolsolutions.com/skills/azure-openai)"
---

# Replacing OpenAI ChatCompletion with Completion()
* [Supported OpenAI LLMs](https://docs.litellm.ai/docs/providers/openai) * [Supported Azure OpenAI LLMs](https://docs.litellm.ai/docs/providers/azure)

## Overview

# Replacing OpenAI ChatCompletion with Completion()

* [Supported OpenAI LLMs](https://docs.litellm.ai/docs/providers/openai)
* [Supported Azure OpenAI LLMs](https://docs.litellm.ai/docs/providers/azure)

<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/LiteLLM_Azure_and_OpenAI_example.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

## Completion() - Quick Start
```python
import os 
from litellm import completion

# openai configs
os.environ["OPENAI_API_KEY"] = ""

# azure openai configs
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = "https://openai-gpt-4-test-v-1.openai.azure.com/"
os.environ["AZURE_API_VERSION"] = "2023-05-15"



# openai call
response = completion(
    model = "gpt-3.5-turbo", 
    messages = [{ "content": "Hello, how are you?","role": "user"}]
)
print("Openai Response\\n")
print(response)

# azure call
response = completion(
    model = "azure/<your-azure-deployment>",
    messages = [{ "content": "Hello, how are you?","role": "user"}]
)
print("Azure Response\\n")
print(response)
```

## Completion() with Streaming
```python
import os 
from litellm import completion

# openai configs
os.environ["OPENAI_API_KEY"] = ""

# azure openai configs
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = "https://openai-gpt-4-test-v-1.openai.azure.com/"
os.environ["AZURE_API_VERSION"] = "2023-05-15"



# openai call
response = completion(
    model = "gpt-3.5-turbo", 
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    stream=True
)
print("OpenAI Streaming response")
for chunk in response:
  print(chunk)

# azure call
response = completion(
    model = "azure/<your-azure-deployment>",
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    stream=True
)
print("Azure Streaming response")
for chunk in response:
  print(chunk)

```

## Completion() with Streaming + Async
```python
import os 
from litellm import acompletion

# openai configs
os.environ["OPENAI_API_KEY"] = ""

# azure openai configs
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = "https://openai-gpt-4-test-v-1.openai.azure.com/"
os.environ["AZURE_API_VERSION"] = "2023-05-15"



# openai call
response = acompletion(
    model = "gpt-3.5-turbo", 
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    stream=True
)

# azure call
response = acompletion(
    model = "azure/<your-azure-deployment>",
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    stream=True
)

```

## Completion() multi-threaded

```python
import os
import threading
from litellm import completion

# Function to make a completion call
def make_completion(model, messages):
    response = completion(
        model=model,
        messages=messages,
        stream=True
    )

    print(f"Response for {model}: {response}")

# Set your API keys
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
os.environ["AZURE_API_KEY"] = "YOUR_AZURE_API_KEY"

# Define the messages for the completions
messages = [{"content": "Hello, how are you?", "role": "user"}]

# Create threads for making the completions
thread1 = threading.Thread(target=make_completion, args=("gpt-3.5-turbo", messages))
thread2 = threading.Thread(target=make_completion, args=("azure/your-azure-deployment", messages))

# Start both threads
thread1.start()
thread2.start()

# Wait for both threads to finish
thread1.join()
thread2.join()

print("Both completions are done.")
```

---

Source: [Claudary](https://claudary.paisolsolutions.com/skills/azure-openai) · https://claudary.paisolsolutions.com