Realtime API

About Realtime API

OpenAI Realtime API is the developer product for building low-latency voice and realtime multimodal experiences on OpenAI models. It is a distinct API product because it has its own interaction model, specialized realtime models and deployment considerations that differ from ordinary request-response text generation. This page focuses on voice agents, streaming interaction, architecture, cost and deployment trade-offs rather than repeating the broader OpenAI API Platform page.

What is the Realtime API for?

The Realtime API is designed for applications where latency and continuous interaction matter, especially conversational voice agents, live assistants and streaming audio workflows. It supports realtime input and output instead of requiring each interaction to be handled as a separate long request.

The current OpenAI model catalogue includes specialized realtime models for speech, translation and transcription. Buyers should select the model based on the interaction pattern, latency target, quality needs and cost rather than treating every realtime model name as a separate product.

How does Realtime differ from ordinary API calls?

Traditional API requests are well suited to tasks where an application sends input, waits for a completed result and then decides what to do next. Realtime applications maintain a more continuous session so audio, events and tool interactions can happen with much lower conversational delay.

That architecture adds complexity. Developers must handle connection state, interruptions, turn detection, audio transport, retries and session-level permissions in addition to normal prompting and tool use.

Where does GPT-Live fit?

OpenAI launched GPT-Live in July 2026 to power a more natural ChatGPT Voice experience and stated that GPT-Live models are planned for API access. OpenAI's current public API model catalogue still centers on the GPT-Realtime family for developer realtime workloads, so Brandligo does not describe GPT-Live API access as generally available unless OpenAI publishes that status.

Teams should use the currently documented realtime models for production and treat announced GPT-Live API access as a separate upcoming line until OpenAI confirms release details.

What should teams consider when estimating cost?

Realtime workloads can be more expensive and less predictable than simple text requests because they may process continuous audio, generate speech and invoke tools during a session. Pricing should be evaluated using the current OpenAI API pricing documentation for the exact realtime model and media type in use.

Teams should test average session length, audio volume, interruptions, tool calls and retry behavior. A per-session cost estimate based on real conversations is more useful than comparing only a text-token headline price.

What are the main deployment risks?

Voice agents can misunderstand users, interrupt at the wrong time or take incorrect actions when tools are enabled. Production systems should separate conversation from high-risk actions and require confirmation where a mistake could affect money, accounts, bookings or other important state.

Latency also depends on networking, audio processing, model choice and application architecture. Teams should test in the regions and devices their users actually have rather than relying on a local development demonstration.

How does Realtime API compare with alternatives?

Alternatives include other model providers with native speech-to-speech APIs, separate speech recognition plus text-to-speech stacks, and specialist contact-center voice platforms. A modular stack can offer more control over individual components, while an integrated realtime model can reduce orchestration complexity.

The right choice depends on latency, language coverage, voice quality, tool use, observability, compliance requirements and total cost at expected call volume.

Who should choose something else?

A standard text API is simpler when an application does not need live conversation. A separate transcription service may be better when the job is only turning audio into text, and a specialist telephony platform may be better when call routing, recording and contact-center operations dominate the project.

Realtime API is most useful when natural, low-latency interaction is central to the product and the engineering team is prepared to manage streaming state and voice-specific failure modes.