OpenAI Ultrafast: The Investigation from Hours to Minutes—A Revolution or a Narrative?

What Happened

Ultrafast is a low-latency processing tier of Codex, not an independent product. In a preview on August 13 this year, OpenAI revealed that under Ultrafast mode, GPT-5.6 Sol could generate up to 14 times faster than standard processing, at approximately 750 tokens per second. In an article on October 26, the company added that GPT-6 Astra Ultrafast could generate tokens 8 times faster than Astra Standard.

In this article, OpenAI cited two internal use cases. One was how security teams could compress the time required to align structured and unstructured data into a decision context from 1 to 2 hours to 10 to 15 minutes. The other was that ordinary engineers reported that tasks that previously took long to get answers could now be resolved within 30 seconds.

It’s important to note that these are all internal statements by OpenAI and have not been verified by independent third parties. The claims of 14 times and 8 times refer to the model’s token generation speed, not a real reduction in accident investigation time by 14 or 8 times.

Official Statement Doesn’t Match

The article describes Ultrafast as the default option for accident investigation, but OpenAI also emphasizes that “engineers remain responsible for judgment and deployment.” This statement is crucial because it acknowledges that AI is only a supplementary tool, with ultimate decision-making power still in humans.

However, there is a discrepancy in messaging: the promotional headline says “from hours to minutes,” suggesting that accidents are automatically resolved; but the actual description in the article is “investigation time shortened.” Investigation and resolution are two different things. Describing a reduction in investigation time as an improvement in accident handling efficiency is a typical marketing rhetoric.

Additionally, the article mentions that Ultrafast is currently only available to Pro 500 users and qualifying Enterprise/Edu customers, with Enterprise also requiring workspace permissions. This means that most ordinary users cannot use it, and the so-called “from hours to minutes” experience is currently limited to OpenAI’s internal staff and a few paid enterprises.

What’s Been Discussed in the Comments

Discussions on Hacker News and related technical communities focused on two perspectives. One view is that low latency can indeed change engineers’ workflows, especially in accident scenarios where quick decisions are needed, as waiting itself is a cost. Another view pointed out that OpenAI only published internal data and confused “token generation speed” with “accident investigation time.” Some joked that if the model could answer faster, problem diagnosis time would naturally shorten, but this doesn’t mean the model solved more difficult problems.

Some engineers mentioned that real-time-consuming accident investigations often involve not just “asking AI questions,” but also reconstructing scenarios, collecting logs, and coordinating personnel. If AI merely speeds up a step that currently takes only 10% of the time, then the overall benefit is far less than what the headline suggests.

Three Things Ordinary Users Can Do

First, separate “fast” from “correct.” A fast response does not mean reliable conclusions; human judgment remains irreplaceable in accident investigations.

Second, if your team is already using Codex, you can start testing Ultrafast on small-scale, low-risk tasks, such as log summaries or code refactoring, rather than letting it dominate accident investigation.

Third, pay attention to costs. The prices of the Pro 500 and enterprise plans are not cheap. When evaluating benefits, you need to calculate the true total cost of ownership, not just how many times response speed has increased.

Conclusion

Ultrafast’s low-latency capability can indeed change the way engineering teams work, but this article by OpenAI is more like a PR package for internal results. The title exaggerates “缩短调查时间” to “revolution in accident handling,” while the key constraints are hidden in the body of the text. For ordinary users, it’s not yet time to blindly follow suit; for industry observers, it’s more important to track verification data from independent third parties. We’ve only seen OpenAI’s side of the story.