This browser is no longer supported.

Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support.

Download Microsoft Edge More info about Internet Explorer and Microsoft Edge

The GPT-3.5-Turbo and GPT-4 models are language models that are optimized for conversational interfaces. The models behave differently than the older GPT-3 models. Previous models were text-in and text-out, meaning they accepted a prompt string and returned a completion to append to the prompt. However, the GPT-3.5-Turbo and GPT-4 models are conversation-in and message-out. The models expect input formatted in a specific chat-like transcript format, and return a completion that represents a model-written message in the chat. While this format was designed specifically for multi-turn conversations, you'll find it can also work well for non-chat scenarios too.

This article walks you through getting started with the GPT-3.5-Turbo and GPT-4 models. It's important to use the techniques described here to get the best results. If you try to interact with the models the same way you did with the older model series, the models will often be verbose and provide less useful responses.

Working with the GPT-3.5-Turbo and GPT-4 models

The following code snippet shows the most basic way to use the GPT-3.5-Turbo and GPT-4 models with the Chat Completion API. If this is your first time using these models programmatically, we recommend starting with our GPT-3.5-Turbo & GPT-4 Quickstart .

In the Azure OpenAI documentation we refer to GPT-3.5-Turbo, and GPT-35-Turbo interchangeably. The official name of the model on OpenAI is gpt-3.5-turbo , but for Azure OpenAI due to Azure specific character constraints the underlying model name is gpt-35-turbo .

client = AzureOpenAI( api_key = os.getenv("AZURE_OPENAI_API_KEY"), api_version = "2024-02-01", azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") response = client.chat.completions.create( model="gpt-35-turbo", # model = "deployment_name". messages=[ {"role": "system", "content": "Assistant is a large language model trained by OpenAI."}, {"role": "user", "content": "Who were the founders of Microsoft?"} #print(response) print(response.model_dump_json(indent=2)) print(response.choices[0].message.content) "id": "chatcmpl-8GHoQAJ3zN2DJYqOFiVysrMQJfe1P", "choices": [ "finish_reason": "stop", "index": 0, "message": { "content": "Microsoft was founded by Bill Gates and Paul Allen. They established the company on April 4, 1975. Bill Gates served as the CEO of Microsoft until 2000 and later as Chairman and Chief Software Architect until his retirement in 2008, while Paul Allen left the company in 1983 but remained on the board of directors until 2000.", "role": "assistant", "function_call": null "content_filter_results": { "hate": { "filtered": false, "severity": "safe" "self_harm": { "filtered": false, "severity": "safe" "sexual": { "filtered": false, "severity": "safe" "violence": { "filtered": false, "severity": "safe" "created": 1698892410, "model": "gpt-35-turbo", "object": "chat.completion", "usage": { "completion_tokens": 73, "prompt_tokens": 29, "total_tokens": 102 "prompt_filter_results": [ "prompt_index": 0, "content_filter_results": { "hate": { "filtered": false, "severity": "safe" "self_harm": { "filtered": false, "severity": "safe" "sexual": { "filtered": false, "severity": "safe" "violence": { "filtered": false, "severity": "safe" Microsoft was founded by Bill Gates and Paul Allen. They established the company on April 4, 1975. Bill Gates served as the CEO of Microsoft until 2000 and later as Chairman and Chief Software Architect until his retirement in 2008, while Paul Allen left the company in 1983 but remained on the board of directors until 2000.

The OpenAI Python library version 0.28.1 is deprecated. We recommend using 1.x . Consult our migration guide for information on moving from 0.28.1 to 1.x .

import os
import openai
openai.api_type = "azure"
openai.api_version = "2024-02-01" 
openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT")  # Your Azure OpenAI resource's endpoint value.
openai.api_key = os.getenv("AZURE_OPENAI_API_KEY")
response = openai.ChatCompletion.create(
    engine="gpt-35-turbo", # The deployment name you chose when you deployed the GPT-3.5-Turbo or GPT-4 model.
    messages=[
        {"role": "system", "content": "Assistant is a large language model trained by OpenAI."},
        {"role": "user", "content": "Who were the founders of Microsoft?"}
print(response)
# To print only the response content text:
# print(response['choices'][0]['message']['content'])
Output
JSON formatting added artificially for ease of reading.
  "choices": [
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": "The founders of Microsoft are Bill Gates and Paul Allen. They co-founded the company in 1975.",
        "role": "assistant"
  "created": 1679014551,
  "id": "chatcmpl-6usfn2yyjkbmESe3G4jaQR6bsScO1",
  "model": "gpt-3.5-turbo-0301",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 86,
    "prompt_tokens": 37,
    "total_tokens": 123
The following parameters aren't available with the new GPT-35-Turbo and GPT-4 models: logprobs, best_of, and echo. If you set any of these parameters, you'll get an error.
Every response includes a finish_reason. The possible values for finish_reason are:
stop: API returned complete model output.
length: Incomplete model output due to max_tokens parameter or token limit.
content_filter: Omitted content due to a flag from our content filters.
null: API response still in progress or incomplete.
Consider setting max_tokens to a slightly higher value than normal such as 300 or 500. This ensures that the model doesn't stop generating text before it reaches the end of the message.
Model versioning
gpt-35-turbo is equivalent to the gpt-3.5-turbo model from OpenAI.
Unlike previous GPT-3 and GPT-3.5 models, the gpt-35-turbo model as well as the gpt-4 and gpt-4-32k models will continue to be updated. When creating a deployment of these models, you'll also need to specify a model version.
You can find the model retirement dates for these models on our models page.
Working with the Chat Completion API
OpenAI trained the GPT-35-Turbo and GPT-4 models to accept input formatted as a conversation. The messages parameter takes an array of message objects with a conversation organized by role. When using the Python API a list of dictionaries is used.
The format of a basic Chat Completion is as follows:
{"role": "system", "content": "Provide some context and/or instructions to the model"},
{"role": "user", "content": "The users messages goes here"}
A conversation with one example answer followed by a question would look like:
{"role": "system", "content": "Provide some context and/or instructions to the model."},
{"role": "user", "content": "Example question goes here."},
{"role": "assistant", "content": "Example answer goes here."},
{"role": "user", "content": "First question/message for the model to actually respond to."}
System role
The system role also known as the system message is included at the beginning of the array. This message provides the initial instructions to the model. You can provide various information in the system role including:
A brief description of the assistant
Personality traits of the assistant
Instructions or rules you would like the assistant to follow
Data or information needed for the model, such as relevant questions from an FAQ
You can customize the system role for your use case or just include basic instructions. The system role/message is optional, but it's recommended to at least include a basic one to get the best results.
Messages
After the system role, you can include a series of messages between the user and the assistant.
 {"role": "user", "content": "What is thermodynamics?"}
To trigger a response from the model, you should end with a user message indicating that it's the assistant's turn to respond. You can also include a series of example messages between the user and the assistant as a way to do few shot learning.
Message prompt examples
The following section shows examples of different styles of prompts that you could use with the GPT-35-Turbo and GPT-4 models. These examples are just a starting point, and you can experiment with different prompts to customize the behavior for your own use cases.
Basic example
If you want the GPT-35-Turbo model to behave similarly to chat.openai.com, you can use a basic system message like "Assistant is a large language model trained by OpenAI."
{"role": "system", "content": "Assistant is a large language model trained by OpenAI."},
{"role": "user", "content": "Who were the founders of Microsoft?"}
Example with instructions
For some scenarios, you might want to give additional instructions to the model to define guardrails for what the model is able to do.
{"role": "system", "content": "Assistant is an intelligent chatbot designed to help users answer their tax related questions.
Instructions: 
- Only answer questions related to taxes. 
- If you're unsure of an answer, you can say "I don't know" or "I'm not sure" and recommend users go to the IRS website for more information. "},
{"role": "user", "content": "When are my taxes due?"}
Using data for grounding
You can also include relevant data or information in the system message to give the model extra context for the conversation. If you only need to include a small amount of information, you can hard code it in the system message. If you have a large amount of data that the model should be aware of, you can use embeddings or a product like Azure AI Search to retrieve the most relevant information at query time.
{"role": "system", "content": "Assistant is an intelligent chatbot designed to help users answer technical questions about Azure OpenAI Serivce. Only answer questions using the context below and if you're not sure of an answer, you can say 'I don't know'.
Context:
- Azure OpenAI Service provides REST API access to OpenAI's powerful language models including the GPT-3, Codex and Embeddings model series.
- Azure OpenAI Service gives customers advanced language AI with OpenAI GPT-3, Codex, and DALL-E models with the security and enterprise promise of Azure. Azure OpenAI co-develops the APIs with OpenAI, ensuring compatibility and a smooth transition from one to the other.
- At Microsoft, we're committed to the advancement of AI driven by principles that put people first. Microsoft has made significant investments to help guard against abuse and unintended harm, which includes requiring applicants to show well-defined use cases, incorporating Microsoft’s principles for responsible AI use."
{"role": "user", "content": "What is Azure OpenAI Service?"}
Few shot learning with Chat Completion
You can also give few shot examples to the model. The approach for few shot learning has changed slightly because of the new prompt format. You can now include a series of messages between the user and the assistant in the prompt as few shot examples. These examples can be used to seed answers to common questions to prime the model or teach particular behaviors to the model.
This is only one example of how you can use few shot learning with GPT-35-Turbo and GPT-4. You can experiment with different approaches to see what works best for your use case.
{"role": "system", "content": "Assistant is an intelligent chatbot designed to help users answer their tax related questions. "},
{"role": "user", "content": "When do I need to file my taxes by?"},
{"role": "assistant", "content": "In 2023, you will need to file your taxes by April 18th. The date falls after the usual April 15th deadline because April 15th falls on a Saturday in 2023. For more details, see https://www.irs.gov/filing/individuals/when-to-file."},
{"role": "user", "content": "How can I check the status of my tax refund?"},
{"role": "assistant", "content": "You can check the status of your tax refund by visiting https://www.irs.gov/refunds"}
Using Chat Completion for non-chat scenarios
The Chat Completion API is designed to work with multi-turn conversations, but it also works well for non-chat scenarios.
For example, for an entity extraction scenario, you might use the following prompt:
{"role": "system", "content": "You are an assistant designed to extract entities from text. Users will paste in a string of text and you will respond with entities you've extracted from the text as a JSON object. Here's an example of your output format:
   "name": "",
   "company": "",
   "phone_number": ""
{"role": "user", "content": "Hello. My name is Robert Smith. I'm calling from Contoso Insurance, Delaware. My colleague mentioned that you are interested in learning about our comprehensive benefits policy. Could you give me a call back at (555) 346-9322 when you get a chance so we can go over the benefits?"}
Creating a basic conversation loop
The examples so far have shown you the basic mechanics of interacting with the Chat Completion API. This example shows you how to create a conversation loop that performs the following actions:
Continuously takes console input, and properly formats it as part of the messages list as user role content.
Outputs responses that are printed to the console and formatted and added to the messages list as assistant role content.
This means that every time a new question is asked, a running transcript of the conversation so far is sent along with the latest question. Since the model has no memory, you need to send an updated transcript with each new question or the model will lose context of the previous questions and answers.
OpenAI Python 1.x
OpenAI Python 0.28.1
client = AzureOpenAI(
  api_key = os.getenv("AZURE_OPENAI_API_KEY"),  
  api_version = "2024-02-01",
  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")  # Your Azure OpenAI resource's endpoint value.
conversation=[{"role": "system", "content": "You are a helpful assistant."}]
while True:
    user_input = input("Q:")      
    conversation.append({"role": "user", "content": user_input})
    response = client.chat.completions.create(
        model="gpt-35-turbo", # model = "deployment_name".
        messages=conversation
    conversation.append({"role": "assistant", "content": response.choices[0].message.content})
    print("\n" + response.choices[0].message.content + "\n")
openai.api_type = "azure"
openai.api_version = "2024-02-01" 
openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT")  # Your Azure OpenAI resource's endpoint value.
openai.api_key = os.getenv("AZURE_OPENAI_API_KEY")
conversation=[{"role": "system", "content": "You are a helpful assistant."}]
while True:
    user_input = input()      
    conversation.append({"role": "user", "content": user_input})
    response = openai.ChatCompletion.create(
        engine="gpt-35-turbo", # The deployment name you chose when you deployed the GPT-35-turbo or GPT-4 model.
        messages=conversation
    conversation.append({"role": "assistant", "content": response["choices"][0]["message"]["content"]})
    print("\n" + response['choices'][0]['message']['content'] + "\n")
When you run the code above you'll get a blank console window. Enter your first question in the window and then hit enter. Once the response is returned, you can repeat the process and keep asking questions.
Managing conversations
The previous example will run until you hit the model's token limit. With each question asked, and answer received, the messages list grows in size. The token limit for gpt-35-turbo is 4096 tokens, whereas the token limits for gpt-4 and gpt-4-32k are 8192 and 32768 respectively. These limits include the token count from both the message list sent and the model response. The number of tokens in the messages list combined with the value of the max_tokens parameter must stay under these limits or you'll receive an error.
It's your responsibility to ensure the prompt and completion falls within the token limit. This means that for longer conversations, you need to keep track of the token count and only send the model a prompt that falls within the limit.
We strongly recommend staying within the documented input token limit for all models even if you find you are able to exceed that limit.
The following code sample shows a simple chat loop example with a technique for handling a 4096 token count using OpenAI's tiktoken library.
The code uses tiktoken 0.5.1. If you have an older version run pip install tiktoken --upgrade.
OpenAI Python 1.x
OpenAI Python 0.28.1
client = AzureOpenAI(
  api_key = os.getenv("AZURE_OPENAI_API_KEY"),  
  api_version = "2024-02-01",
  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")  # Your Azure OpenAI resource's endpoint value.
system_message = {"role": "system", "content": "You are a helpful assistant."}
max_response_tokens = 250
token_limit = 4096
conversation = []
conversation.append(system_message)
def num_tokens_from_messages(messages, model="gpt-3.5-turbo-0613"):
    """Return the number of tokens used by a list of messages."""
        encoding = tiktoken.encoding_for_model(model)
    except KeyError:
        print("Warning: model not found. Using cl100k_base encoding.")
        encoding = tiktoken.get_encoding("cl100k_base")
    if model in {
        "gpt-3.5-turbo-0613",
        "gpt-3.5-turbo-16k-0613",
        "gpt-4-0314",
        "gpt-4-32k-0314",
        "gpt-4-0613",
        "gpt-4-32k-0613",
        tokens_per_message = 3
        tokens_per_name = 1
    elif model == "gpt-3.5-turbo-0301":
        tokens_per_message = 4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
        tokens_per_name = -1  # if there's a name, the role is omitted
    elif "gpt-3.5-turbo" in model:
        print("Warning: gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613.")
        return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0613")
    elif "gpt-4" in model:
        print("Warning: gpt-4 may update over time. Returning num tokens assuming gpt-4-0613.")
        return num_tokens_from_messages(messages, model="gpt-4-0613")
    else:
        raise NotImplementedError(
            f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."""
    num_tokens = 0
    for message in messages:
        num_tokens += tokens_per_message
        for key, value in message.items():
            num_tokens += len(encoding.encode(value))
            if key == "name":
                num_tokens += tokens_per_name
    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
    return num_tokens
while True:
    user_input = input("Q:")      
    conversation.append({"role": "user", "content": user_input})
    conv_history_tokens = num_tokens_from_messages(conversation)
    while conv_history_tokens + max_response_tokens >= token_limit:
        del conversation[1] 
        conv_history_tokens = num_tokens_from_messages(conversation)
    response = client.chat.completions.create(
        model="gpt-35-turbo", # model = "deployment_name".
        messages=conversation,
        temperature=0.7,
        max_tokens=max_response_tokens
    conversation.append({"role": "assistant", "content": response.choices[0].message.content})
    print("\n" + response.choices[0].message.content + "\n")
openai.api_type = "azure"
openai.api_version = "2024-02-01" 
openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT")  # Your Azure OpenAI resource's endpoint value.
openai.api_key = os.getenv("AZURE_OPENAI_API_KEY")
system_message = {"role": "system", "content": "You are a helpful assistant."}
max_response_tokens = 250
token_limit = 4096
conversation = []
conversation.append(system_message)
def num_tokens_from_messages(messages, model="gpt-3.5-turbo-0613"):
    """Return the number of tokens used by a list of messages."""
        encoding = tiktoken.encoding_for_model(model)
    except KeyError:
        print("Warning: model not found. Using cl100k_base encoding.")
        encoding = tiktoken.get_encoding("cl100k_base")
    if model in {
        "gpt-3.5-turbo-0613",
        "gpt-3.5-turbo-16k-0613",
        "gpt-4-0314",
        "gpt-4-32k-0314",
        "gpt-4-0613",
        "gpt-4-32k-0613",
        tokens_per_message = 3
        tokens_per_name = 1
    elif model == "gpt-3.5-turbo-0301":
        tokens_per_message = 4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
        tokens_per_name = -1  # if there's a name, the role is omitted
    elif "gpt-3.5-turbo" in model:
        print("Warning: gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613.")
        return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0613")
    elif "gpt-4" in model:
        print("Warning: gpt-4 may update over time. Returning num tokens assuming gpt-4-0613.")
        return num_tokens_from_messages(messages, model="gpt-4-0613")
    else:
        raise NotImplementedError(
            f"""num_tokens_from_messages() is not implemented for model {model}."""
    num_tokens = 0
    for message in messages:
        num_tokens += tokens_per_message
        for key, value in message.items():
            num_tokens += len(encoding.encode(value))
            if key == "name":
                num_tokens += tokens_per_name
    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
    return num_tokens
while True:
    user_input = input("")     
    conversation.append({"role": "user", "content": user_input})
    conv_history_tokens = num_tokens_from_messages(conversation)
    while conv_history_tokens + max_response_tokens >= token_limit:
        del conversation[1] 
        conv_history_tokens = num_tokens_from_messages(conversation)
    response = openai.ChatCompletion.create(
        engine="gpt-35-turbo", # The deployment name you chose when you deployed the GPT-35-Turbo or GPT-4 model.
        messages=conversation,
        temperature=0.7,
        max_tokens=max_response_tokens,
    conversation.append({"role": "assistant", "content": response['choices'][0]['message']['content']})
    print("\n" + response['choices'][0]['message']['content'] + "\n")
In this example, once the token count is reached, the oldest messages in the conversation transcript will be removed. del is used instead of pop() for efficiency, and we start at index 1 so as to always preserve the system message and only remove user/assistant messages. Over time, this method of managing the conversation can cause the conversation quality to degrade as the model will gradually lose context of the earlier portions of the conversation.
An alternative approach is to limit the conversation duration to the max token length or a certain number of turns. Once the max token limit is reached and the model would lose context if you were to allow the conversation to continue, you can prompt the user that they need to begin a new conversation and clear the messages list to start a brand new conversation with the full token limit available.
The token counting portion of the code demonstrated previously is a simplified version of one of OpenAI's cookbook examples.
Troubleshooting
Don't use ChatML syntax with the Chat Completions endpoint
We have found that some customers will try using the legacy ChatML syntax with the chat completion endpoints and newer models. ChatML was a preview capability that only worked with the legacy completions endpoint with the gpt-35-turbo version 0301 model which is slated for retirement. Attempting to use ChatML syntax with newer models and the chat completions endpoint can result errors as well as unexpected model response behavior, and is not recommended.
Error
Cause
Solution
400 - Failed to generate output due to special tokens in the input.
Your prompt contains legacy ChatML tokens not recognized or supported by the model/endpoint.
Ensure that your prompt/messages array does not contain any legacy ChatML tokens. If you are upgrading from a legacy model, please exclude all special tokens before submitting an API request to the model.
Next steps
Learn more about Azure OpenAI.
Get started with the GPT-35-Turbo model with the GPT-35-Turbo quickstart.
For more examples, check out the Azure OpenAI Samples GitHub repository
			Coming soon: Throughout 2024 we will be phasing out GitHub Issues as the feedback mechanism for content and replacing it with a new feedback system. For more information see: https://aka.ms/ContentUserFeedback.		

        
Submit and view feedback for
                    This product