which are the mpost used models in voice ai especially wit toolm calls?

Thanks for reaching out, luisa. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

I mean is it gpt-4o, 41-mini? which is the fastest but at the same time can handle well tool calls building function on the fly

Thanks — good clarification. Responding to your question about speed vs. tool-calling reliability for voice AI: • Fastest: GPT‑4o mini — lowest latency (~200 ms) and lowest cost; great for quick intent detection and simple responses. • Best for reliable tool/function calls: GPT‑4o — stronger reasoning, more robust at building/executing functions on the fly and avoiding hallucinated tool arguments. • Recommended pattern: Use a hybrid/cascaded approach — run GPT‑4o mini for fast triage/intent classification, then escalate to GPT‑4o to generate and execute tool calls or compose complex function payloads. If you want, tell me your expected request volume and latency target and I’ll suggest a concrete pipeline (where to switch models, caching, and guardrails for tool arguments). Sources: Pricing | LiveKit | Sequential Pipeline Architecture for Voice Agents | LiveKit | Build Your First AI Voice Agent in Python: Complete Tutorial | LiveKit | Pipeline vs. Realtime - Which is the better Voice Agent Architecture? | Liv... | The ReAct Pattern for Voice Agents and How AI Agents Think, Act, and Respon..., with interactive elements

but how about for conversations?

Thanks — good question about conversations. • Best overall for multi‑turn conversations: GPT‑4o — stronger context retention, more coherent and nuanced replies across longer dialogues, and more reliable when conversation logic needs tool calls. • Fastest for short turns: GPT‑4o mini — great for low‑latency, quick replies but weaker on long context and complex follow‑ups. • Practical recommendation: Use a hybrid approach: a. GPT‑4o mini for brief confirmations, quick intent classification, and low‑latency UX touches. b. GPT‑4o for sustained multi‑turn sessions, summarization, or whenever the assistant must build/execute tool calls reliably. c. Switch to the full model when the conversation reaches complexity thresholds (e.g., N turns, large context size, or when a tool call is required). Tell me your typical conversation length, target latency, and budget and I’ll propose specific thresholds and an implementation pattern. , with interactive elements