Realtime Voice AI APIs Compared: OpenAI, Gemini, Vapi, Retell, Twilio

Realtime voice AI APIs are no longer one category. In 2026, the right choice depends on whether you are building an in-app voice assistant, a phone agent, a branded voice experience, or a modular speech pipeline.

The short version: use OpenAI Realtime or Gemini Live when the product is a live app or browser assistant; use Vapi or Retell when the product is primarily a phone agent; use Twilio ConversationRelay when Twilio owns the call path; use Deepgram Voice Agent when speech infrastructure and pipeline control matter; and use ElevenLabs Conversational AI when voice quality and agent packaging matter more than owning every layer.

Quick recommendations

Use case	Best fit	Why
Browser or mobile voice assistant	OpenAI Realtime	Strong realtime model API, tool use, and WebRTC/WebSocket patterns for product-owned apps
Multimodal live audio/video experiments	Gemini Live API	Official docs position Live API around low-latency audio/video sessions, WebSockets, and integrations such as LiveKit/Pipecat
Production phone agent with fastest launch	Retell or Vapi	Managed agent, telephony, testing, monitoring, and call workflow surfaces reduce launch glue
Existing Twilio voice stack	Twilio ConversationRelay	Keeps phone numbers, TwiML, recording, routing, and contact-center operations in Twilio while your app supplies the AI backend
Modular speech pipeline	Deepgram Voice Agent	Deepgram exposes a speech-to-text, LLM, and text-to-speech pipeline over a single WebSocket with lower-level control
Premium branded voice agent	ElevenLabs Conversational AI	Voice quality, agent configuration, telephony integrations, and monitoring are packaged together

What counts as a realtime voice AI API?

A realtime voice AI API handles a low-latency conversation where a user can speak, interrupt, wait briefly, hear a response, and continue the task without feeling like they are waiting for a batch job.

That can be native speech-to-speech. It can also be a coordinated pipeline:

Microphone / phone audio
  → streaming speech recognition or native audio model
  → model reasoning and tool calls
  → generated speech
  → browser, mobile app, SIP, or PSTN caller

The API decision usually turns on seven differences:

Transport: WebRTC, WebSocket, SIP, PSTN, or a managed web-call widget.
Pipeline: native realtime model API versus speech-to-text plus LLM plus text-to-speech.
Turn-taking: interruption handling, barge-in, silence detection, and partial-response behavior.
Tools and actions: function calling, webhook actions, workflow handoffs, and backend authorization.
Telephony: phone numbers, outbound dialing, transfers, call recording, voicemail, and regional routing.
Observability: transcripts, event logs, call traces, evaluation runs, latency reporting, and cost by session.
Governance: data retention, redaction, consent prompts, audit logs, and controls for sensitive calls.

Provider comparison table

Provider	Category	Best for	Watch out for
OpenAI Realtime	Realtime multimodal model API	In-app voice assistants, copilots, coaching products, and voice UX with tool calls	Your product owns session creation, audio UX, logging, consent, and production guardrails
Gemini Live API	Realtime multimodal model API	Audio/video experiments and Google ecosystem teams that want Live API patterns	Production telephony still needs a call layer or partner integration
Deepgram Voice Agent	Speech infrastructure and voice-agent pipeline	Teams that want speech-to-text, LLM, and text-to-speech orchestration over one WebSocket	More workflow design remains with your application team
ElevenLabs Conversational AI	Voice quality plus agent platform	Brand-sensitive voices, conversational agents, SIP/Twilio-style deployment, and monitoring	Less portable than assembling independent STT, model, and TTS vendors
Twilio ConversationRelay	Telephony bridge for conversational AI	Twilio-centric phone agents, contact centers, transfers, and programmable voice workflows	It is the call transport layer, not a full model provider by itself
Vapi	Managed voice-agent platform	API-first voice agents, web calls, phone calls, custom transcribers, custom TTS, custom LLMs, tools, and SIP integration	You still need to choose and operate underlying model, voice, and telephony choices carefully
Retell	Managed voice-agent platform	Reliable AI phone agents with inbound/outbound calls, telephony provider integrations, testing, monitoring, and A/B workflows	Platform fit and data/export controls should be tested before deep rollout

OpenAI Realtime API

OpenAI Realtime is the default shortlist candidate when the product is an app-owned voice experience: browser assistants, internal copilots, coaching products, language practice, interactive demos, and support copilots where the user is already inside your product.

Choose it when you want one realtime model session to handle audio, text events, tool use, and conversation state. It is especially strong when your product already owns the UI, identity, permissions, and backend tools. In that setup, OpenAI is the low-latency conversation layer and your application still controls account access, logging, redaction, and analytics.

Do not treat it as a drop-in call-center platform. You still need to build or integrate:

ephemeral session creation and API-key isolation;
WebRTC or WebSocket connection handling;
transcript and audio-retention policy;
tool authorization and failure handling;
product-specific latency and cost monitoring;
handoff paths when the agent cannot finish the task.

If your immediate question is specifically how to build with OpenAI Realtime, start with our OpenAI Realtime API voice applications guide and OpenAI Realtime WebRTC setup guide. This page is the broader provider-selection guide.

Google Gemini Live API

Gemini Live API is the closest broad comparison to OpenAI Realtime when the product needs live multimodal interaction. Google’s Live API documentation covers low-latency audio/video sessions, raw WebSockets, tool use, session management, ephemeral tokens, and third-party realtime integrations such as LiveKit and Pipecat.

Choose Gemini Live when the product benefits from Google ecosystem fit, multimodal experimentation, or audio/video context rather than pure phone-call operations. It is a better fit for interactive products and prototypes than for a team that only needs a managed outbound call agent tomorrow.

Evaluate three things before standardizing:

whether your team prefers Gemini’s session and tool-use model;
whether your app needs video/screen context now or later;
whether your production call path already lives somewhere else, such as Twilio, Vapi, or Retell.

Deepgram Voice Agent

Deepgram Voice Agent is for teams that want speech infrastructure control without wiring every pipeline event by hand. Deepgram’s official docs describe a voice-agent API that combines speech-to-text, LLM integration, and text-to-speech over a single WebSocket connection.

That makes it attractive when transcription quality, streaming behavior, latency instrumentation, and modularity matter. A Deepgram-centered architecture can be easier to reason about than a fully managed voice-agent platform because you can still think in pipeline stages: listening, thinking, speaking, telephony, browser SDK, and reusable configuration.

Choose Deepgram when you want to swap or tune parts of the stack, when transcripts are critical, or when speech infrastructure is already part of your platform. If the team mainly wants a ready-made phone agent with campaign tooling, compare Vapi and Retell first.

ElevenLabs Conversational AI

ElevenLabs started as a voice-quality leader, but its Conversational AI and ElevenAgents documentation now make it a broader voice-agent platform. The docs expose agent behavior, voice and language configuration, knowledge bases, tools, personalization, authentication, deployment options, telephony, WhatsApp, batch calls, monitoring, testing, experiments, versioning, analytics, and real-time monitoring.

Choose ElevenLabs when the voice itself is a product requirement. That includes education, creator tools, language-learning apps, interactive characters, branded assistants, and support flows where tone, pronunciation, language coverage, or voice consistency change the perceived quality of the product.

The tradeoff is control. If your engineering team wants to own each speech and model stage independently, a modular Deepgram/OpenAI/Gemini/TTS stack may be cleaner. If the team wants a polished agent surface with strong voice defaults, ElevenLabs belongs on the shortlist.

For a narrower TTS comparison, see ElevenLabs vs Cartesia vs Deepgram for text-to-speech APIs.

Twilio ConversationRelay

Twilio ConversationRelay matters when the real product is a phone workflow. Twilio is the voice-network layer: phone numbers, programmable voice, TwiML, routing, recording, transfers, compliance surfaces, and contact-center operations.

ConversationRelay connects a Twilio call to a conversational AI application. Twilio’s documentation exposes ConversationRelay attributes, nested language and parameter elements, interruption behavior, reporting input during agent speech, and other call-control details. That is different from choosing an LLM provider. In most architectures, Twilio handles the call path while your backend or agent platform handles the model, tools, and business workflow.

Choose Twilio first when you already operate Twilio voice infrastructure or you need mature phone-network controls. Pair it with OpenAI, Gemini, Deepgram, ElevenLabs, Vapi, Retell, or your own backend depending on who should own the conversation intelligence.

Vapi

Vapi is a managed voice-agent platform for teams that want APIs around phone calls, web calls, assistants, conversation behavior, model configuration, tools, custom voices, custom transcribers, custom TTS, custom LLMs, observability, evals, simulations, phone numbers, SIP integration, and in-call control.

Choose Vapi when you want a developer-facing voice-agent platform but still care about customizing the model, voice, tools, and telephony pieces. It is a good fit for teams that want to launch faster than a fully custom pipeline while retaining more API-level control than a pure no-code call-agent builder.

The evaluation question is not simply “does Vapi support voice agents?” It does. The question is whether your team likes the operating model: assistant configuration, provider selection, tool handling, simulations/evals, and how much of the call lifecycle you want the platform to own.

Retell

Retell is a managed platform for building, testing, deploying, and monitoring AI phone agents. Its documentation emphasizes conversational AI agents that handle phone calls naturally, support inbound and outbound calls, integrate with telephony providers, and provide testing and monitoring workflows.

Choose Retell when the task is explicitly a phone agent: booking, qualification, intake, support triage, call campaigns, or operational workflows where a visual/testable call-agent lifecycle is valuable. Retell can reduce time-to-demo because more of the phone-agent workflow is packaged.

The tradeoff is platform dependency. Before committing, test data exports, call traces, tool integrations, phone-number ownership, custom telephony requirements, and how quickly a non-specialist on your team can debug a failed call.

For a narrower managed-platform comparison, see Vapi vs Retell AI voice agent APIs and Bland.ai vs Vapi vs Retell voice agent APIs.

Architecture patterns that change the API choice

Pattern 1: in-app voice assistant

Use this for browser, mobile, desktop, and internal-product assistants where the user is already authenticated inside your product.

Best starting shortlist:

OpenAI Realtime
Gemini Live
Deepgram Voice Agent if speech pipeline control matters

The application owns identity, UI state, product permissions, consent prompts, microphone access, tool authorization, and analytics. The realtime API mainly needs low-latency audio, interruption handling, tool events, and predictable session state.

Pattern 2: phone agent

Use this for inbound support, outbound sales, appointment booking, intake, collections, reminders, and call-center workflows.

Best starting shortlist:

Retell
Vapi
Twilio ConversationRelay plus your model/agent backend
ElevenLabs if voice quality and agent packaging are central

The phone path changes the decision because a production voice agent needs phone numbers, ringing, voicemail, transfers, recording, regional routing, caller-ID behavior, compliance review, transcript retention, and sometimes human escalation. A raw realtime model API does not replace that operating layer.

Pattern 3: branded voice experience

Use this when the voice is the product: education, language learning, creator products, coaching, entertainment, and customer-facing assistants where tone changes conversion.

Best starting shortlist:

ElevenLabs Conversational AI
OpenAI Realtime or Gemini Live for the reasoning layer
A TTS-focused stack if you need custom pipeline control

Evaluate voice quality with real scripts, not demos. Include interruptions, names, jargon, emotional tone, multilingual turns, noisy microphones, and the exact phrases that matter to your brand.

Pattern 4: modular speech infrastructure

Use this when the team needs to control, observe, or swap each stage independently.

Best starting shortlist:

Deepgram Voice Agent
Deepgram or another STT provider plus OpenAI/Gemini/Anthropic plus a TTS provider
Twilio or another CPaaS provider for phone transport

This path has more engineering surface area, but it can be cheaper and more portable at scale. It is also easier to audit because you can inspect transcripts, tool inputs, model outputs, generated speech, and call events as separate stages.

Evaluation checklist before committing

Run a proof of concept before choosing the platform. The test should include real background noise, interruptions, slow user responses, a tool call that succeeds, a tool call that fails, a long pause, a transfer or escalation path, and one case where the user changes their mind mid-sentence.

Measure at least these fields:

Metric	Why it matters
Median turn latency	Captures everyday conversational feel
p95 turn latency	Shows whether edge cases create awkward silence
Barge-in recovery	Tests whether callers can interrupt naturally
Completed-task rate	Separates impressive demos from useful agents
Tool-call success rate	Shows whether the agent can actually do work
Cost per successful conversation	Better than cost per minute because failed calls still cost money
Transcript and trace quality	Determines whether your team can debug production failures

For phone agents, also measure call connection time, dropped-call rate, voicemail handling, transfer success, recording/transcript availability, and phone-number operations. For browser assistants, measure browser compatibility, reconnect behavior, permission prompts, local-device echo/noise, and session analytics.

Pricing checklist

Realtime voice pricing is hard because several meters stack together:

input audio or input tokens;
output audio or output tokens;
speech-to-text;
text-to-speech;
telephony minutes;
phone numbers;
recording and storage;
observability, evaluation, or platform fees;
failed calls, retries, and test traffic;
human review time for sensitive transcripts.

Do not price only one average minute. Model three conversations: a short successful call, a long call with tools, and a failed call that still consumes telephony and model time. That exposes whether a “cheap” platform becomes expensive because of retries, premium voices, number rental, transcript storage, concurrency, or manual review.

Decision framework

Use this ordering when the shortlist feels crowded:

Start with the channel. Browser/app voice pushes you toward OpenAI Realtime or Gemini Live. Phone voice pushes you toward Retell, Vapi, Twilio, or ElevenLabs telephony integrations.
Then decide ownership. If you want the platform to own the call-agent lifecycle, choose a managed platform. If you want swappable components, choose a modular stack.
Then test latency and barge-in. Voice-agent quality is mostly felt in the awkward moments: interruptions, silence, tool latency, and recovery.
Then review data handling. Voice agents produce raw audio, transcripts, phone numbers, tool arguments, and sometimes identity context.
Then price successful outcomes. Cost per minute is less useful than cost per completed, compliant, reviewable conversation.

OpenAI Realtime API voice applications for OpenAI-specific WebRTC, model, voice, and security details.
OpenAI Realtime API WebRTC setup for browser-session implementation details.
Vapi vs Retell AI voice agent APIs for the managed voice-agent platform decision.
Bland.ai vs Vapi vs Retell voice agent APIs for a three-way phone-agent comparison.
Best voice and speech APIs when you need broader speech-to-text and text-to-speech context.

Sources

Official sources checked for this refresh on 2026-05-15:

The API Integration Checklist (Free PDF)