gpt-realtime-1.5 by OpenAI
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Voice workflows just got stronger with gpt-realtime-1.5 in the Realtime API. The model offers more reliable instruction following, tool calling, and multilingual accuracy.
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gpt-realtime-1.5 is OpenAI’s latest voice model for the Realtime API, designed to power live, low-latency voice interactions. It builds on the foundation of realtime voice sessions by delivering more reliable instruction following, improved tool calling, and stronger multilingual accuracy. The model is optimized for applications that require a persistent connection where audio streams in and responses stream out in near real-time.
gpt-realtime-1.5 improves how the model adheres to system prompts and user instructions during live sessions. This means fewer off-track responses and more consistent behavior when handling complex voice workflows.
The model can invoke tools during an active voice session without breaking the conversational flow. This enables voice agents to fetch data, update records, or trigger external actions while the user is still speaking.
Language handling is more precise across supported languages, making the model a stronger choice for translation sessions and multilingual voice agents. The improvement reduces misinterpretations in live speech-to-speech workflows.
gpt-realtime-1.5 makes voice agents more dependable by tightening instruction adherence and tool execution in live audio sessions.
The model’s edge lies in how it balances responsiveness with reliability. Earlier realtime models could drift from instructions or struggle with tool calls mid-conversation. gpt-realtime-1.5 addresses these pain points directly, so developers can build voice agents that feel more predictable and capable without sacrificing low latency.
You are building a voice agent that needs to follow complex instructions, call tools during a conversation, or handle multiple languages accurately. It is also a strong fit if you are already using the Realtime API and want to upgrade from an earlier model for better consistency in production. If your use case is purely file-based transcription or generated speech without live sessions, the request-based audio APIs remain the better choice.
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