Compare / Speech-to-speech

Prism vs. OpenAI GPT-Live-1

Use GPT-Live-1 if you want something to play with. Use Prism if you're putting speech-to-speech in front of real callers and need to control it: fine-tuned on your calls, running in your VPC, and checked word by word before anyone hears it. It starts talking in half the time, too.

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Side by side

Where Prism has the edge

Capability	PrismRime	GPT-Live-1OpenAI
Speed and accuracy
Time to first audioτ-Voice, Artificial Analysis	0.57 sReasoning off	1.24 sReasoning on (Sol, low)
Task completionτ-Voice average	56.6%Without reasoning	59.3%With reasoning on
Telecom task completionτ-Voice telecom domain	82.5%Highest published domain score on the board	45.6%Sol, low
Turn-taking	Takes turns. Callers can cut in at any point and Prism yields.	Full duplex: listens and speaks at the same time.
Your model
Fine-tune the speech-to-speech model	Yes. RL fine-tuning on your production calls, scored against your own rubrics on held-out calls.	No. OpenAI's fine-tuning platform is closed to new users.
How it hears the caller	One model reasons directly over the caller's audio, tone included.	A voice model hears the audio. A separate backend model does reasoning and tool calls from a transcript.
Guardrails
Review before speech	Every response is written as text first. Your policies check each word before it becomes audio.	Transcript checks run while the model speaks. An optional path holds audio until your app approves it.
Required disclosures	Enforced at the text checkpoint, on every turn.	Corrections can interrupt speech after it starts.
Deployment
Where it runs	On-prem, your VPC, or Rime cloud	OpenAI cloud or Azure Foundry
Compliance	HIPAA and SOC 2	Zero data retention available, with limits
Switching from OpenAI	Uses OpenAI Realtime's event names, so existing agent code carries over.	n/a

Prism beat OpenAI on order completion

We put Prism head-to-head against OpenAI to see which model made more accurate orders. The results: We nearly 2x'ed order accuracy for Revmo on real calls.

94%

Orders placed with the caller's consent, Prism fine-tuned on Revmo's calls

57%

Orders placed with the caller's consent, the OpenAI model behind Revmo's agents

~10 hours of training calls · same held-out calls for both models · Oct 2026

Fast enough to feel human, accurate enough to trust

Prism is fast and accurate out of the box. Faster than OpenAI models and at par with accuracy out of the box. Fine-tune it on your own calls and the gap widens.

Each dot is a speech-to-speech model on Artificial Analysis's τ-Voice benchmark. Up is more accurate, left is faster to start speaking, and rings mark models with reasoning turned on. GPT-Live-1 is measured at its Sol, low reasoning setting. Prism gets to the top-left corner without reasoning. Scores as of October 2026.

Three things you keep with Prism

Fine-tuning

A model trained on your callers

Rime fine-tunes Prism on your production calls with reinforcement learning, then scores it against your rubrics on calls it hasn't seen. You ship when it beats your incumbent.

your calls → baseline → fine-tune → re-measure → deploy

Guardrails

Read every word before it's spoken

Prism reasons over audio, then writes its reply as text. Your app checks that text against policies, guardrails, and required disclosures, and only approved text becomes speech.

caller audio → reasoning → text → your policy check → speech

Deployment

Runs where your data lives

Deploy Prism on-prem, in your virtual private cloud (VPC), or in Rime cloud. Caller audio never has to leave infrastructure you control. HIPAA and SOC 2 compliant.

on-prem · VPC · Rime cloud

Questions builders ask

How much faster is Prism than GPT-Live-1?

On Artificial Analysis's τ-Voice benchmark, Prism starts speaking 0.57 seconds after the caller finishes, with reasoning off. GPT-Live-1 (Sol, low) takes 1.24 seconds with reasoning on. That's 0.67 seconds less silence on every turn of every call.

Is GPT-Live-1 more accurate?

On the public average, slightly: 59.3% to Prism's 56.6%, and higher reasoning settings score higher still at a longer wait. Prism gets within three points without reasoning, scores 82.5% to 45.6% on the telecom domain, and pulls ahead once it's fine-tuned on your calls. At Revmo that meant 94% of orders placed with consent, against 57%.

Doesn't a text step make Prism a cascade?

No. A cascade reasons over a transcript and loses the caller's tone. Prism reasons over the audio and emits text so your rules can review every word before it becomes speech. The text is the output checkpoint. The reasoning happens on audio.

How much data does fine-tuning take?

Revmo's model was tuned on about ten hours of production calls, but each customer varies. Rime runs fine-tuning with you: we measure a baseline on your calls, tune, and re-measure on held-out calls against the outcomes you care about.

Can I run Prism on-prem?

Yes. Prism runs on-prem, in your VPC, or in Rime cloud. Rime is HIPAA and SOC 2 compliant, and Prism is telephony-ready.

How hard is it to switch from OpenAI?

Prism uses the same event names as OpenAI's Realtime API, so an agent built on OpenAI's realtime stack moves over without a rewrite.

Is Prism full duplex?

Not yet. Prism takes turns, and callers can interrupt it at any point. GPT-Live-1 is full duplex today. If simultaneous speech matters more on your calls than speed, guardrails, or fine-tuning, tell us and we'll show you where Prism is headed.

Start the conversation

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Sources: τ-Voice scores and time to first audio from Artificial Analysis, Oct 2026; GPT-Live-1 at Sol, low reasoning. GPT-Live-1 architecture, guardrail and deployment details from OpenAI's public documentation. OpenAI fine-tuning availability per OpenAI's API pricing page. Revmo results measured by Rime on held-out Revmo production calls.