Building low-latency voice AI applications requires careful model selection and pipeline architecture. Fast mini models like GPT-4o Mini via OpenAI's Real-time API achieve sub-200ms TTFT by using native speech-to-speech pipelines, eliminating separate ASR/TTS services. A complete Node.js implementation is provided using WebSockets, streaming PCM audio, server-side VAD, and function calling. The post also compares GPT-4o Mini against Claude 3.5 Haiku and Gemini 2.0 Flash for voice use cases, covers latency optimization techniques (short system prompts, regional deployment, token caps), and includes a TTFT measurement script for benchmarking your own pipeline.
Table of contents
Table of ContentsThe Latency Problem in Voice AIWhat Makes Real-Time-Optimized Mini Models DifferentLatency Comparison: Real-Time Mini Models Across ProvidersArchitecture Overview: How the Voice AI Pipeline WorksBuilding a Real-Time Voice AssistantReal-World Use CasesLimitations and When to Use a Larger ModelChoosing the Right Model for Voice AIAppendix: TTFT Verification ScriptSort: