We ran 600 controlled voice calls across Anthropic Claude Sonnet 4.6, OpenAI GPT-5, Google Gemini 2.5 Pro, and Groq-served Llama 3.3 70B in a production SMB receptionist context. We scored on latency, filler words, interruption handling, accent comprehension, and on-call conversion. The honest, defensible verdict — including which LLM the TrainYourAgent stack actually picks up first, and the…
If you read any AI vendor blog, every LLM is somehow the best at every task. We run AI receptionists in production for service businesses, and we have informed opinions on which models actually pick up the phone well — and which fall over the second a real customer interrupts. We ran 600 controlled voice calls — 150 per model — against four models that matter in mid-2026: Anthropic Claude Sonnet 4.6 (the Claude family's voice-optimized variant) OpenAI GPT-5 (the flagship reasoning model, low-latency variant) Google Gemini 2.5 Pro (the multimodal model) Groq-served Llama 3.3 70B (the fastest-served open-weights option) We scored each on five operationally-meaningful dimensions: latency, filler / hallucination rate, interruption handling, accent comprehension, and on-call conversion. The full methodology, the per-model scorecard, and our actual production routing logic are below. Spoiler: there is no single winner across all five dimensions.
Spoiler: there is no single winner across all five dimensions. The best production voice agent in 2026 routes between models based on call state, not "use the smartest one for everything." The TrainYourAgent stack uses Anthropic as primary, Groq as fast-fallback, OpenAI as deep-reasoning escalation, and Gemini as multilingual specialist. Why we made each of those choices is below. Methodology Each model was wired into an identical voice agent stack: STT: Deepgram Nova-3 (held constant across all models) TTS: ElevenLabs Turbo v3 (held constant) Orchestration: Vapi 2.0 (held constant) Knowledge base: identical 12-document RAG over a synthetic 14-truck HVAC contractor's pricing, service area, hours, and policies Each model received 150 inbound calls, with the call distribution held constant: 60 "easy path" calls (book a routine service appointment) 30 "hard path" calls (multi-objection booking, complex pricing question) 30 "interruption" calls (caller interrupts mid-explanation, mid-confirmation) 20 "accent" calls (heavily accented English: Indian, Nigerian, Mexican-Spanish-influenced) 10 "hostile" calls (rude, agitated, trying to break the agent) Calls were placed by a panel of 12 native and non-native English speakers reading from a randomized script with intentional jitter to simulate real-call patter. Time-to-first-token (TTFT) — how long from end of user utterance to first audio response, in milliseconds 2.
We ran 600 controlled voice calls across Anthropic Claude Sonnet 4.6, OpenAI GPT-5, Google Gemini 2.5 Pro, and Groq-served Llama 3.3 70B in a production SMB receptionist context. We scored on latency, filler words, interruption handling, accent comprehension, and on-call conversion. The honest, defensible verdict — including which LLM the TrainYourAgent stack actually picks up first, and the multi-LLM fallback story behind it. It is filed under AI Voice because that is where operators looking for this problem actually start, and it is written from production work rather than from a content calendar.