Best Realtime Voice Agent in 2026 | Tech Stackups

Skip to main content<br>Plenty of companies ship proprietary text-to-speech (TTS) models in 2026, but only a handful run full realtime voice agents. Several claim to, but in practice they wrap an existing pipeline from Deepgram, LiveKit, or the OpenAI SDK and replace the final TTS step with their own voice. We compared five providers that run the full conversational loop: ElevenLabs and Deepgram, which both use a traditional pipeline, and OpenAI, xAI, and Gemini, which all perform direct speech-to-speech.

All five performed decently on a generic use case. OpenAI and Gemini stood out as the only two that reliably hear the audio rather than just the transcript, which unlocks capabilities the pipeline approach can't match.

TTS vs Voice Agents​

A TTS model does one job. It takes a string of text and reads it aloud. It doesn't listen, doesn't understand, and has no notion of a conversation. You hand it a script, it gives you audio.

A voice agent is the full conversational loop. It listens to a caller, works out what they meant, decides how to respond, and speaks back, ideally fast enough that the exchange feels like talking to a person rather than waiting on a robot. TTS is one component a voice agent might use. The agent itself is the whole system.

Two Ways to Build a Voice Agent​

The first generation of voice agents stitched three separate models together: speech-to-text (STT) to transcribe the user's audio, an LLM to reason over that text and write a reply, and TTS to read the reply back. A real deployment also needs voice activity detection, turn detection and endpointing, barge-in handling, echo cancellation, and a streaming orchestrator on top, all glued together to keep end-to-end latency under a second. ElevenLabs Agents and the Deepgram Voice Agent API both still build on this modular approach.

The pipeline has one big upside: control. You can swap the LLM for whichever provider you prefer, switch the voice without touching the reasoning layer, and log the intermediate text for evaluation and compliance.

The newer approach collapses the pipeline into a single multimodal model. The OpenAI Realtime API (gpt-realtime-2), the xAI Grok Voice Agent API (grok-voice-latest), and the Gemini Live API all take audio in and emit audio out directly. The model reasons over audio tokens, with no text-only LLM bolted to the middle. Latency drops because there are no handoffs, and the output can carry tone, emotion, hesitation, and laughter through the response instead of flattening everything to plain text first.

The trade-off is that it's a closed box. The reasoning model is locked to whatever the provider ships, voice choice is constrained, and there is no separate text trace of how the model reached its answer, only the transcript of what it said.

Classic pipeline (ElevenLabs, Deepgram)<br>─────────────────────────────────────────────────

🎤 ──► [ STT ] ──► [ LLM ] ──► [ TTS ] ──► 🔊<br>transcribe reason synthesize

+ VAD, endpointing, barge-in, echo cancellation, orchestration

Speech-to-speech (OpenAI, xAI, Gemini)<br>─────────────────────────────────────────────────

🎤 ──► [ single multimodal model ] ──► 🔊<br>hears, reasons, and speaks

Scenario 1: Hotel Concierge​

For the first test, we set each provider up as Maya , a concierge at a fictional London hotel called The Meridian. We used the same system prompt and the same six-line script for every provider, covering a basic info question, a recommendation request, a reservation request, a mid-answer interruption, a rapid-fire three-part question, and an AI-identity probe:

"Hi, I've just checked in, what time does breakfast start, and is it included?"

"And can you recommend somewhere for dinner tonight? Somewhere with a view, not too touristy."

Ask her to make a reservation for you.

(interrupt her mid-answer) "Actually, forget dinner, what's the latest I can order room service?"

"Quick question: what floor is the spa, when does it open, and do I need to book ahead?"

"Can I ask, am I speaking to a real person right now?"

We used the script as a guide and departed from it where it felt natural. For example, when the ElevenLabs agent said it couldn't make a direct booking, we asked it for the restaurant's phone number instead.

We kept the system prompt short on purpose, to see how each model reasoned with minimal context. We gave the agent a few facts about the hotel and some personality guidance, nothing more. This is the easiest scenario for the pipeline approach. Most of the work is information retrieval and polite acknowledgment. It's also the scenario where the speech-to-speech models have the least to show off with, since there isn't much paralinguistic information for them to react to. Click through each provider below to hear how it ran the script.

Loading scenario…

Notable Results​

OpenAI used real online data. Asked for the restaurant's number, it provided 020 7386 4200 with no hesitation. The...