Use the GPT Realtime API via WebSockets

Learn how to use the GPT Realtime API for speech and audio via WebSockets.

Use the GPT Realtime API via WebSockets

This article refers to the Microsoft Foundry (new) portal.

Azure OpenAI GPT Realtime API for speech and audio is part of the GPT-4o model family that supports low-latency, “speech in, speech out” conversational interactions.

You can use the Realtime API via WebRTC, SIP, or WebSocket to send audio input to the model and receive audio responses in real time.

Follow the instructions in this article to get started with the Realtime API via WebSockets. Use the Realtime API via WebSockets in server-to-server scenarios where low latency isn’t a requirement.

In most cases, use the Realtime API via WebRTC for real-time audio streaming in client-side applications such as a web application or mobile app. WebRTC is designed for low-latency, real-time audio streaming and is the best choice for most scenarios.

Use the following table to help you choose the right protocol for your scenario:

Protocol	Best for	Latency	Complexity
WebRTC	Client-side apps (web, mobile)	Lowest (~50-100ms)	Higher
WebSocket	Server-to-server, batch processing	Moderate (~100-300ms)	Lower
SIP	Telephony integration	Varies	Highest

Prerequisites

Before you can use GPT real-time audio, you need:

• An Azure subscription. Create one for free.
• A Microsoft Foundry resource. Create the resource in one of the supported regions. For setup steps, see Create a Microsoft Foundry resource.
• A deployment of the gpt-4o-realtime-preview, gpt-4o-mini-realtime-preview, gpt-realtime, gpt-realtime-mini, or gpt-realtime-mini-2025-12-15 model in a supported region as described in the supported models section in this article.
  • In the Foundry portal, load your project. Select Build in the upper-right menu, then select the Models tab on the left pane, and select Deploy a base model. Search for the model you want, and select Deploy on the model page.
• Required libraries:
  • Python: pip install websockets azure-identity
  • JavaScript/Node.js: npm install ws @azure/identity

Supported models

The GPT real-time models are available for global deployments in the East US 2 and Sweden Central regions.

• gpt-4o-mini-realtime-preview (2024-12-17)
• gpt-4o-realtime-preview (2024-12-17)
• gpt-realtime (2025-08-28)
• gpt-realtime-mini (2025-10-06)
• gpt-realtime-mini-2025-12-15 (2025-12-15)

For more information about supported models, see the models and versions documentation.

Connection and authentication

The Realtime API (via /realtime) is built on the WebSockets API to facilitate fully asynchronous streaming communication between the end user and model.

The Realtime API is accessed via a secure WebSocket connection to the /realtime endpoint of your Azure OpenAI resource.

You can construct a full request URI by concatenating:

• The secure WebSocket (wss://) protocol.
• Your Azure OpenAI resource endpoint hostname, for example, my-aoai-resource.openai.azure.com
• The openai/realtime API path.
• A deployment query string parameter with the name of your gpt-4o-realtime-preview, gpt-4o-mini-realtime-preview, or gpt-realtime model deployment.
• (Preview version only) An api-version query string parameter for a supported API version such as 2025-04-01-preview.

The following example is a well-constructed /realtime request URI:

GA version

    wss://my-eastus2-openai-resource.openai.azure.com/openai/v1/realtime?model=gpt-realtime-deployment-name

The GA API uses model= as the query parameter name, while the preview API uses deployment=. Both refer to your deployed model name.

To authenticate:

• Microsoft Entra (recommended): Use token-based authentication with the /realtime API for an Azure OpenAI resource with managed identity enabled. Apply a retrieved authentication token using a Bearer token with the Authorization header.
• API key: An api-key can be provided in one of two ways:
  • Using an api-key connection header on the pre-handshake connection. This option isn’t available in a browser environment.
  • Using an api-key query string parameter on the request URI. Query string parameters are encrypted when using HTTPS/WSS.

Realtime API via WebSockets architecture

Once the WebSocket connection session to /realtime is established and authenticated, the functional interaction takes place via events for sending and receiving WebSocket messages. These events each take the form of a JSON object.

Events can be sent and received in parallel and applications should generally handle them both concurrently and asynchronously.

• A client-side caller establishes a connection to /realtime, which starts a new session.
• A session automatically creates a default conversation. Multiple concurrent conversations aren’t supported.
• The conversation accumulates input signals until a response is started, either via a direct event by the caller or automatically by voice activity detection (VAD).
• Each response consists of one or more items, which can encapsulate messages, function calls, and other information.
• Each message item has content_part, allowing multiple modalities (text and audio) to be represented across a single item.
• The session manages configuration of caller input handling (for example, user audio) and common output generation handling.
• Each caller-initiated response.create can override some of the output response behavior, if desired.
• Server-created item and the content_part in messages can be populated asynchronously and in parallel. For example, receiving audio, text, and function information concurrently in a round robin fashion.

Try the quickstart

Now that you’ve done the above steps, you can follow the instructions in the Realtime API quickstart to get started with the Realtime API via WebSockets.

Related content

• Try the real-time audio quickstart
• See the Realtime API reference
• Learn more about Azure OpenAI quotas and limits