OpenAI’s Realtime API Left Beta — the Production Voice Agent Math

OpenAI’s Realtime API went to general availability carrying four connection paths, one new speech-to-speech model.

And a deprecation clock that most coverage mentioned in passing. The model is the fun part of the story. The interface underneath it is the part that costs money.

The API is “officially out of beta and ready for your production voice agents,” per OpenAI’s announcement. gpt-realtime is the first general-availability realtime model, handling audio and text over WebRTC, WebSocket, or SIP.

A developer guide at open.cx puts latency under 300ms and indicative usage around $0.06 per minute of audio in, $0.24 per minute of audio out.

For a small shop, three things in this release deserve real attention: the beta-to-GA interface migration, the single-connection architecture. And what caching does to the bill.

The GA Interface Migration Nobody Budgeted

The buried line sits in the developer notes: “We updated the Realtime API shape with the GA launch, meaning there’s a beta interface and a GA interface.” OpenAI recommends moving to the GA interface because the beta interface will eventually be deprecated.

No date given. I read the announcement twice expecting one and it simply is not there. And that vagueness is doing quiet work on their side of the table. “Eventually,” from a vendor whose realtime stack just went GA, tends to arrive the quarter you are busiest.

So the model launch is the demo and the interface change is the chore.

Anyone who wired a client project to beta endpoints now owns a migration task with no due date attached. A Reddit thread on r/OpenAI also flagged that gpt-realtime replaces gpt-4o-realtime as the primary realtime model. Two moving targets inside one release.

The model itself is a genuine upgrade rather than a rebadge.

OpenAI says gpt-realtime was trained with customers to excel at real-world tasks like support, personal assistance. And education, and that it is better at following instructions, calling tools, producing natural and expressive speech, understanding cues like laughs, and switching languages. New voices and API capabilities shipped alongside it. Read the list the way a caller would. Instructions and tool calling are the boring backbone, while laughs and language switching are what make someone forget they are talking to software.

OpenAI Realtime API Pricing: The Real Math

Number that decides whether a client’s phone bot survives month three.

Indicative figures from the open.cx guide put audio in at roughly $0.06 per minute and audio out at roughly $0.24 per minute, which puts a full-duplex conversation at about $0.30 a minute. That works out to roughly $18 per hour of connected talk time, before caching. An after-hours support line running a few hours a night can now be forecast on a napkin, which is more than you can say for most AI line items.

The newer generation changes the shape of the bill. A Fora Soft production guide reports that as of August 2026 the model line includes gpt-realtime-2 and gpt-realtime-2.1, with gpt-realtime-2 described as a reasoning-grade model launched in May 2026, carrying a 128,000-token context window and an adjustable reasoning-effort control. That guide prices audio input at $32 per 1M tokens, audio output at $64 per 1M tokens. And cached input at $0.40 per 1M, framed as an 80x drop on cached input.

Those are single-source figures, so treat them as directional until your own invoice arrives.

My first read of the cache number was wrong, for what it is worth.

I assumed cached input would land somewhere near $3 per 1M and drafted the advice around that.

At $0.40 per 1M versus $32 uncached, your system prompt, your scripted intake questions. And your product FAQ all belong in cache.

Static context is nearly free now and live audio is the premium lane.

So design the agent so the expensive tokens are the caller’s actual words rather than your boilerplate.

Realtime API Connection Paths: WebRTC vs WebSocket vs SIP

The old way to build a voice agent was a chain: speech-to-text, then an LLM, then text-to-speech, glued together with your own latency budget. That chain is freight getting unloaded and reloaded at every depot along the route.

The realtime session is one truck going door to door.

As one tutorial puts it, “The OpenAI Realtime API collapses this into a single WebSocket or WebRTC connection.” The model handles speech recognition, reasoning. And speech generation in a single forward pass, per the open.cx guide.

That collapse is why the sub-300ms number is believable. Two years of watching voice demos have taught me that latency specs are table stakes. And what callers actually notice is interruption handling. OpenAI’s voice agents guide describes a RealtimeSession that owns audio turns, tools, interruptions. And handoffs inside the session, which means the hard conversational problems stop being your glue code’s problem. That is the part worth paying for.

Four ways in, per the same tutorial: browser via WebRTC, browser via WebSockets, server via WebSockets.

And the public phone network via SIP. SIP is the sleeper, since your callers are on phone lines whether you like it or not. In JavaScript, OpenAI calls RealtimeAgent plus RealtimeSession “the fastest path to a browser-based voice assistant,” connecting over WebRTC in the browser or WebSocket on the server.

Stranger paths exist too.

An AWS integration article describes a Python aiortc setup streaming a remote participant’s audio straight into gpt-realtime, with generated audio streamed back onto a live Amazon IVS stage. I have not run that one myself. And I would want a full day of load testing before anything like it went near a client.

Worth noting that OpenAI’s docs still describe two architectures, speech-to-speech sessions or chained voice pipelines. The guide’s own advice is to “use the live audio API path when the interaction should feel conversational and immediate.” A chained pipeline is not dead. It is simply the wrong tool when the conversation needs to feel alive.

Scoping a Production Voice Agent: Four Calls Before Code

If a voice project landed on my desk tomorrow, these are the four calls I would make before writing a line of code.

1. Pick the connection path from the four available and stop debating it: browser WebRTC for web widgets, server WebSockets for backend control, SIP the moment a real phone number is involved.
2. Build inside a RealtimeSession and let it own audio turns, tools, interruptions. And handoffs, since reimplementing interruption logic is where voice projects go to die.
3. Front-load cacheable context and keep the reasoning-effort control on a dial you can turn down for simple calls.
4. Migrate anything still on the beta interface now, while “eventually deprecated” has not yet turned into “deprecated in 30 days.”

The honest close on GA: shipping a production-ready realtime model does not make voice agents easy, it makes them billable and supportable. The risk moved from “will the demo work” to “will the interface under it still exist in a year.” That is the kind of risk a small shop can actually manage. Audit which interface your voice stack targets this week, run one hour of test calls against the per-minute math above. And you will know more than most people still writing think-pieces about it. If you want a second pair of eyes on the scope before committing, that is exactly the work my shop does.

OpenAI Realtime API FAQ

What does the OpenAI Realtime API cost per minute? Indicative figures from the open.cx guide put audio in around $0.06 per minute and audio out around $0.24 per minute. So a full-duplex conversation runs about $0.30 a minute, roughly $18 per hour before caching. The gpt-realtime-2 line, per the Fora Soft guide current as of August 2026, prices audio input at $32 per 1M tokens, audio output at $64 per 1M tokens. And cached input at $0.40 per 1M, an 80x drop on cached input. Single-source figures, so verify against your own invoice.

When does the beta interface get deprecated?

No date given.

The developer notes say the beta interface will eventually be deprecated and recommend migrating to the GA interface now rather than waiting for a deadline to appear.

WebRTC or WebSocket for a voice agent?

WebRTC in the browser, WebSocket on the server.

OpenAI’s own guidance calls RealtimeAgent plus RealtimeSession “the fastest path to a browser-based voice assistant.” Pick by where your client code lives, then stop relitigating it.

What about callers on actual phone lines?

That is SIP, the fourth connection path. Your callers are on the public phone network whether you like it or not, which makes it the sleeper option in this release.