gpt-realtime-2
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About this model
Gpt-realtime-2 is a next‑generation, low‑latency streaming model designed for real‑time speech‑to‑speech and conversational AI scenarios. It processes audio input continuously and generates responses during live interactions, enabling applications such as voice assistants and interactive agents. As part of the latest set of voice models, it builds on existing realtime capabilities to support more advanced conversational experiences within live audio pipelines that combine speech recognition, reasoning, and response generation in a unified flow.
Supported region: Sweden Central, East US 2, Central US, France Central, South India, Canada Central
Key model capabilities
Key Features:
- Speech‑to‑speech (S2S) interaction
Accepts audio input and produces audio output, enabling full conversational voice flows. - Built‑in reasoning capabilities
Incorporates internal reasoning during each turn, improving instruction following and response quality without exposing reasoning traces. - Reasoning‑first interaction model
Designed with reasoning‑centric turn behavior, allowing the model to think before producing final responses. - Configurable reasoning effort
Supports a reasoning.effort parameter with values:
minimal | low | medium | high, enabling control over reasoning depth and latency tradeoffs. - Structured response phases
Outputs responses in phases:
commentary → preamble (thinking / filler / tool signals)
final_answer → final response after reasoning - Low‑latency streaming for real‑time use
Optimized for interactive voice applications where responses must be generated quickly during live conversations. - Long conversational context support
Supports extended context windows (up to ~128k) to maintain richer multi‑turn conversations. - Improved instruction following vs earlier models
Designed to provide stronger adherence to system prompts and user instructions compared to earlier realtime models. - Successor to gpt‑realtime‑1.5
Represents a reasoning-enabled upgrade to the previous realtime S2S model stack.