Introducing Voice Standard Bench: 50+ models, ranked by performance, speed and cost on real audio

AssemblyAI has launched Voice Standard Bench, an open-source benchmark that evaluates more than 50 LLMs available through its LLM Gateway for voice-related tasks including summarization, information extraction, text correction, and dictation cleanup. Using real speech-to-text transcripts ranging from 30-second voicemails to hour-long meetings, the benchmark compares model quality, latency, cost, and reliability, with responses scored by Claude Opus 5.5 and GPT-5.6 Sol under fixed rubrics. Its findings suggest that many routine voice workloads do not require expensive frontier models, although larger models generally perform better on longer recordings, while smaller open models can offer faster responses for short inputs. The results identify different leading models by task and optimization goal, such as quality, price, or speed, and include reliability measures such as formatting failures, refusals, timeouts, and errors. AssemblyAI advises users to treat the public rankings as a starting point and test shortlisted models with their own transcripts, vocabulary, prompts, and operational constraints through the Gateway, Playground, or the downloadable benchmark framework.

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