OpenAI has introduced a new Ultrafast mode on Tuesday as a preview. The new tier, powered by 750 output tokens per second, is aimed at reducing the time its GPT-5.6 Sol requires to complete tasks. The ChatGPT maker claims that the Ultrafast mode can run GPT-5.6 Sol significantly faster than standard processing.OpenAI confirmed that it is working with select customers to evaluate the performance of the new tier.
GPT-5.6 Sol Gets Ultrafast Mode
Through a blog post, OpenAI has announced an early preview of Ultrafast. The AI company states that this new API service tier can run GPT-5.6 Sol up to 14 times faster than standard processing. The GPT‑5.6 Sol on Ultrafast mode is powered by Cerebras and is claimed to generate up to 750 output tokens per second.
The company notes that this would allow businesses to build more responsive products, make faster decisions, and bring powerful AI directly into their demanding workflows.
“GPT‑5.6 Sol on Ultrafast mode is available in a limited preview today to a select group of customers”, said OpenAI. It confirmed the plans to expand access as capacity increases.
OpenAI has highlighted potential use cases of the Ultrafast, such as real-time incident response, analysing market signals, assessing transactions, customer support, live research and experimentation. The company confirmed that it is working with an initial group of customers to “understand where this speed makes the biggest difference, and how those learnings can inform our products over time”.
The company is testing GPT‑5.6 Sol in Ultrafast mode across companies in coding, commerce, financial research, customer support and other interactive applications. The company says the early trials are helping it understand where an order-of-magnitude change in speed creates value in workflows. “We will use these findings to guide deployment as capacity grows”, it added.
OpenAI developers are testing the GPT‑5.6 Sol in Ultrafast mode internally. It confirmed that the team is using Ultrafast for Incident response, and the new mode is claimed to reduce the delay between observing a signal, testing a hypothesis, and choosing the next action. The internal testing also involves the adoption of Ultrafast for research involving search knowledge sources, query data, and gathering, organising, and summarising information across connected tools.
