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This benchmark shifts focus from isolated Time to First Token (TTFT) metrics to a holistic analysis of the entire voice stack latency budget, including speech-to-text, LLM inference, and text-to-speech components. By quantifying how milliseconds at each layer contribute to perceived conversational flow, it provides engineers with actionable data for optimizing real-time agent performance beyond simple generation speed.

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Lowest-Latency Inference APIs for Voice and Realtime Agents: A Time to First Token TTFT-First Benchmark

MarkTechPostAugust 30, 20267 min read

Voice agents fail on latency long before they fail on intelligence. Time to first token is the metric most teams use to choose an inference API, and it is the right starting point and the wrong stopping point. This benchmark works through every layer of the voice stack — LLM, speech-to-text, text-to-speech, and speech-to-speech — using figures verified against primary sources on August 30, 2026, with each number labeled as independently measured, vendor-published, or vendor-measured on its own product. The post Lowest-Latency Inference APIs for Voice and Realtime Agents: A Time to First Token TTFT-First Benchmark appeared first on MarkTechPost.

It is time for the first token (TTFT) is the metric teams use to select an inference API for voice. It is also the metric that misleads them. TTFT marks when generation starts; a text-to-speech model cannot speak until a full clause arrives. Between those two points sits the difference between an agent that feels conversational and one that gets interrupted. This piece benchmarks every layer of the voice stack including LLM, speech-to-text, text-to-speech, and speech-to-speech. A voice agent is a latency budget with a language model inside it. Every stage spends milliseconds the user can hear. Time to first token (TTFT) is the interval between sending an inference request and receiving the first token back. IBM's definition frames it as the moment…

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