kwindla on X: "I got a bunch of questions about the cost of the Realtime API yesterday after posting this. tldr: OpenAI has followed their usual (and much appreciated) path of cutting the pricing of the Realtime API with every release. Cost is now about $0.04/minute of speech-to-speech time, factori
Quick Answer
OpenAI has reduced the cost of its Realtime API to approximately $0.04 per minute of speech-to-speech time, factoring in token caching.
Quick Take
Users typically incur minimal charges during non-speech periods, making it cost-effective for applications like voice programming, which may only require about $0.20 per hour.
Key Points
- Realtime API costs $0.04 per minute of speech-to-speech time.
- Non-talking time is generally not charged due to voice activity detection.
- Voice programming may only cost about $0.20 per hour.
- OpenAI continues to cut pricing with each release, benefiting users.
- Voice dictation has become a crucial benchmark for programming efficiency.
📖 Reader Mode
~1 min readI got a bunch of questions about the cost of the Realtime API yesterday after posting this. tldr: OpenAI has followed their usual (and much appreciated) path of cutting the pricing of the Realtime API with every release. Cost is now about $0.04/minute of speech-to-speech time, factoring in the implicit token caching. But note: you generally do not get charged for non-talking time because the OpenAI voice activity detection filters out non-speech input. So for a use case like voice programming, you're probably only talking 5% of the time and dictation assistant output is extremely brief, so it's maybe $0.20/hour to stay connected to the Realtime API all the time while you're programming. (ymmv) Cost calculation spreadsheets below ...
— Originally published at x.com
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from WebSearch (Tavily)
See more →全球AI芯片峰会,9月上海见!
The 2026 Global AI Chip Summit will take place in Shanghai on September 22-23, focusing on the evolving AI chip landscape, including the shift from training to inference, the rise of diverse chip technologies, and the restructuring of industry competition. Notable speakers include experts from leading universities and companies, discussing advancements in AI chip architecture and applications.
