Field user providing audio input
AI Models & Assistants / Knowledge library
GPT-Realtime
Model capable of realtime text and audio inputs and outputs
Why learn GPT-Realtime?
Offers the documented OpenAI model capabilities through hosted.
The problem it solves
Provides model inference for supported text / audio / image inputs.
Where it came from
GPT-Realtime: Model capable of realtime text and audio inputs and outputs Published aliases: gpt-realtime-2025-08-28. Input: text, audio, image. Output: text, audio. Checked 2026-10-13. Model capabilities are distinct from tools available in a particular agent.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what GPT-Realtime does.
- Identify a use case for GPT-Realtime.
- Describe a limitation of GPT-Realtime.
- Name a compatible tool for GPT-Realtime.
- Compare GPT-Realtime against an alternative.
Documentation and sources
- Primary source / model card (developers.openai.com)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
Realtime Multimodal Technical Support for Field Technicians
Industrial field service companies managing complex hardware repairs.
The business problem
Technicians often struggle with hands-free documentation access, leading to delays when they must manually search manuals while handling tools or equipment in low-visibility environments.
How this topic solves it
The GPT-Realtime model enables a hands-free, voice-activated interface that processes audio input directly from the field, allowing technicians to ask questions and receive immediate, synthesized audio instructions without interrupting their physical workflow.
- Establish a persistent WebSocket connection to the gpt-realtime endpoint.
- Configure the session to accept audio input streams from the technician's headset.
- Define function calling tools that allow the model to query internal repair databases.
- Stream the model's audio output back to the technician's device for immediate playback.
- Implement prompt caching for frequently accessed equipment manuals to optimize context usage.
This diagram illustrates the bidirectional flow of data between the technician and the model.
Persistent communication channel
Multimodal processing engine
Tool-accessible repair manuals
- Technician → Streams audio input → WebSocket
- WebSocket → Transmits data → GPT-Realtime
- GPT-Realtime → Executes function call → Database
- GPT-Realtime → Streams audio response → WebSocket
Implementation example
This code demonstrates the WebSocket communication pattern required to stream audio to the model and handle function calls for retrieving technical data.
// Establish connection to the realtime endpoint
const ws = new WebSocket('wss://api.openai.com/v1/realtime');
// Send audio chunk as part of the session
ws.send(JSON.stringify({
type: 'input_audio_buffer.append',
audio: base64EncodedAudio // Audio from technician's microphone
}));
// Handle function call for database lookup
if (event.type === 'function_call') {
const result = await queryRepairDatabase(event.args);
// Send tool output back to model
ws.send(JSON.stringify({ type: 'conversation.item.create', item: result }));
}Expected business outcome
Technicians experience improved operational efficiency and reduced cognitive load by interacting with technical documentation through natural, conversational voice commands.
Limitations and checks
The model's reliance on continuous streaming connections means that intermittent network connectivity in remote industrial sites can cause latency or session drops; developers should monitor WebSocket heartbeat signals to detect and trigger automatic reconnection logic.