GPT-Live-1 narrowly tops Grok Voice on speech benchmark, but Gemini leads the table

Exact OpenAI model: GPT‑Live‑1; evaluated configuration: GPT‑Live‑1 (Astra, medium). OpenAI says GPT‑Live handles spoken conversation, listens while speaking, and delegates backend work to OpenAI Responses models or a developer-controlled agent/service. Sessions can use WebRTC, WebSockets, telephony/SIP, or sideband server control. API pricing is duration-based; OpenAI’s launch post states $0.05/minute for the front-end voice layer, with backend model/tool usage billed separately. Getting started with GPT‑Live, OpenAI API launch

GPT‑Live is a two-part system: a native speech front end for conversation control and a backend for reasoning/tool work. That design removes the classic STT→LLM→TTS cascade from the critical interaction loop, but it does not remove application responsibilities: permissions, confirmations, function execution, state saving, and cancellation policy remain developer-owned. Artificial Analysis’ index currently weights Speech Reasoning, Agentic Performance, Arena Preference, and Task Success equally; earlier versions weighted Full Duplex Bench directly. Reproducibility depends on public benchmark harnesses plus opaque proprietary model endpoints. Methodology

• Independent: Artificial Analysis lists GPT‑Live‑1 Astra at 81.5, Grok Voice Think Fast 2.0 at 81.3, and Gemini 3.8 Live Extended Thinking at 82.6. Speech to Speech Models
• Vendor-reported: OpenAI says GPT‑Live‑1 listens and speaks simultaneously and can delegate reasoning/tool calls to backends such as Astra or third-party systems. OpenAI API launch
• Vendor/customer-reported: Speak says tutor-specific prompting cut GPT‑Live‑1 interruption rates from 27.6% to 13.6%, and delivered 476/477 authored lesson lines in 27 evaluation sessions. Speak blog
• Commentary/transcript: The video’s core argument is that GPT‑Live‑1 feels more human because of full duplex, but Grok remains faster and Gemini leads overall. 00:24, 03:06, 07:10

This is part of a broader move from turn-based voice bots to continuous-time interaction. Artificial Analysis’ June index formalized speech-to-speech evaluation across reasoning, conversational dynamics, and agentic customer-service tasks. Research prototypes are converging on similar designs: continuous thinking while listening/speaking, asynchronous delegation, and foreground/background loops. Realtime-Venus documents a shared timeline plus dual-loop runtime; “Never Stop Thinking” reports latency cuts from an interrupt-and-resume orchestrator. Artificial Analysis announcement, Realtime‑Venus, Never Stop Thinking

The “OpenAI beat Grok” claim is narrow: GPT‑Live‑1 leads Grok by 0.2 on one composite score, while Grok is faster on time-to-first-audio and stronger on some listed submetrics; Gemini leads the current overall table. Separately, voice naturalness is not emotional understanding: a July 2026 paper found leading realtime systems often acted on words while ignoring distress, fear, or sarcasm in vocal delivery. Artificial Analysis, Real-Time Voice AI Hears but Does Not Listen

Voice-agent procurement should move from “sounds human” demos to scored task workflows: interruption handling, latency, task completion, escalation quality, and cost per live minute. GPT‑Live‑1 is attractive where natural turn-taking and backend flexibility matter; Grok remains relevant where first-audio latency is decisive; Gemini currently tops the composite index. Production teams should prototype against real calls, not scripts, and keep tool execution, approvals, monitoring, and human handoff outside the voice model.