Voice AI Models in 2026: Comparing the LLMs Powering Voice Agents
Blog post from Coval
Voice AI models, encompassing components like speech-to-text (STT), large language models (LLM), and text-to-speech (TTS), are pivotal in creating effective voice agents, with significant considerations around latency, naturalness, and reliability. In 2026, the choice between cascaded pipelines and speech-to-speech models is critical; cascaded pipelines offer observability and separate evaluation of each component, while speech-to-speech models provide lower latency and more natural interaction by processing audio directly. The LLM layer varies significantly across deployments, with options like OpenAI's GPT-4o and GPT-Realtime-2, Anthropic's Claude, and Google's Gemini each offering distinct advantages in terms of speed, cost, and capability. Model selection should be driven by empirical testing on representative datasets to account for unique constraints like latency, cost, and multilingual capabilities, with a strong emphasis on real-world performance rather than benchmark scores. Teams are encouraged to build robust evaluation infrastructures to regularly test and optimize model choices, ensuring that the models meet the specific needs of their voice AI applications.
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