Announcing agentic performance benchmarking for Speech to Speech models on Artificial Analysis. We use τ-Voice to measure tool calling and customer interaction voice agent capabilities in realistic customer service scenarios

Even the strongest Speech to Speech （S2S） models today resolve only about half of realistic customer service scenarios end-to-end - a meaningful gap relative to frontier text-based agents on the same tasks. Voice channels introduce significant complexity： challenging accents， background noise， and packet loss， all while requiring fast responses， consistency across long multi-turn conversations， and reliable tool use. Performance also varies considerably by audio condition： in clean audio some models perform notably better， but realistic conditions continue to pose a challenge. Conversation duration also varies meaningfully across models， with implications for both customer experience and operational cost.