Previewing Ultrafast mode: GPT-5.6 Sol at up to 14X the speed

Today, we’re sharing an early look at Ultrafast, a new service tier that runs GPT‑5.6 Sol up to 14× faster than Standard processing, launching first in the OpenAI API. Powered by Cerebras, Ultrafast generates up to 750 output tokens per second, bringing our most intelligent model at the time of this announcement to products and workflows where every second matters.

With GPT‑5.6 , we’re pushing the frontier on what our models can do and making them more efficient across every layer of our stack. Those improvements have made advanced intelligence more affordable and more useful to more people . Until now, getting real-time speed typically meant choosing a smaller or more specialized model. Ultrafast points to progress in a new direction: more useful work per second.

When speed no longer requires giving up intelligence, AI can move into the most time-sensitive parts of a business and new kinds of work become possible. We’ve already seen some encouraging scenarios for Ultrafast:

For research, our team uses Ultrafast to rapidly search knowledge sources, query data, and quickly gather, organize, and summarize information across connected tools. A common workflow in research is for our team members to launch a batch of experiments over night, and review the results in the morning. With Ultrafast, we see this loop tightening to support multiple iterations during the workday instead.

Customer support and voice: Resolve complex customer issues in real time without interrupting the conversation, even when finding the answer requires multiple steps or systems.

We’ve been testing GPT‑5.6 Sol on Ultrafast mode with an initial group of companies across coding, commerce, financial research, support, and other interactive applications. Starting with business workflows lets us study these conditions in real production environments. Their early work is helping us understand where an order-of-magnitude change in speed creates the most value and how products change when the model can keep pace with the person using it. We will use these findings to guide deployment as capacity grows.

Incident response and reliability: When a critical system fails, analyze application logs, recent code changes, and engineer reports to identify the likely cause and help prepare a fix while the outage is still unfolding.

Financial research and security: Analyze market signals, assess transactions, and identify suspicious activity while conditions are still changing.

Commerce: Answer product questions, check inventory, personalize recommendations, and resolve checkout issues while the shopper is still deciding, before hesitation becomes an abandoned cart.