Speech-to-text used to force a choice: accurate but slow (batch transcription), or fast but sloppy (streaming). In 2026 that tradeoff narrowed sharply. The leading models are both accurate and fast enough for live conversation.
The two numbers that matter
Two metrics define a streaming STT model. Word error rate (WER) measures accuracy — the best general models now report figures in the mid-single-digit percentages. Latency measures how quickly the first partial transcript appears and how soon it's finalized — the strong real-time models return first partials around 150ms and finalize well under 300ms. A model needs both to power a responsive voice agent.
Batch accuracy is table stakes. The hard part is keeping it while transcribing as the person is still speaking.
Streaming changes everything
The key architectural shift is transcribing incrementally — emitting a running guess and refining it as more audio arrives — rather than waiting for the speaker to stop. That's what lets the downstream model start planning its response before the sentence even ends, shaving precious latency off the full loop.
Choosing one
The practical criteria: WER on audio like yours (accents, jargon, noise all matter and vary by model), streaming latency, language coverage, and cost per hour. Benchmarks give a starting point, but real-world audio is messy — the honest move is to run your own recordings through the top candidates and compare. The best model on a clean benchmark isn't always the best on your call-center audio.
