OpenAI just pushed two updates to its Realtime API that matter for anyone building voice agents: a new model and a meaningful latency improvement that applies to everything in the lineup.
A smarter mini, at the same price
GPT-Realtime mini is capable of responding to audio and text inputs in realtime over WebRTC, WebSocket, or SIP connections. The original mini was fast and cheap, but it lacked two things that production voice agents almost always need: the ability to reason through a problem before speaking, and reliable tool calling. gpt-realtime-2.1-mini closes that gap.
The new model brings reasoning and tool use to the mini tier, matching the capabilities that GPT-Realtime-2, the most capable realtime voice model, already had , supporting speech-to-speech interactions with configurable reasoning effort, stronger instruction following, and more reliable tool use for complex voice-agent workflows. The key difference: you now get those features at the mini price point.
Pricing stays identical to the original gpt-realtime-mini. Text tokens are priced at $0.60 per 1M input and $2.40 per 1M output. That is a fraction of the flagship gpt-realtime-2 cost, which runs $4.00 per 1M input and $24.00 per 1M output for text.
Why reasoning in a voice model is harder than it sounds
GPT-Realtime-2 was a generational upgrade to OpenAI's speech-to-speech model, bringing internal reasoning to real-time voice. Where previous models responded immediately, it can work through a problem before speaking , making it well suited for voice applications that need to handle complex, multi-step queries entirely in the audio layer without routing to a separate text pipeline.
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