OpenAI's Ultrafast mode runs GPT-5.6 Sol up to 14x faster using Cerebras wafer-scale hardware, now in limited API preview.

Ultrafast's headline number - up to 14x faster inference, topping out around 750 output tokens per second ^[1]- isn't achieved by shrinking the model. It's the same GPT-5.6 Sol, run on hardware that sidesteps the bottleneck that normally throttles large language models: memory bandwidth. On a standard GPU cluster, weights have to be shuttled between off-chip memory and processing cores for every token generated. Cerebras' Wafer-Scale Engine instead keeps 44 GB of SRAM directly on each wafer-sized chip ^[1], so the model's weights sit next to the compute rather than queuing behind a bus. OpenAI frames the result as a tradeoff-free kind of scaling: '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.' ^[2]

The benchmark numbers back up the framing. On GDP-Val, a suite of economically valuable knowledge-work tasks like legal briefs and financial models, Ultrafast delivered a 5.6x end-to-end speedup with no measured loss in quality ^[1]. On Humanity's Last Exam, a 2,500-question benchmark, Ultrafast finished the full set in 11 hours 11 minutes versus 78 hours 27 minutes for Anthropic's Claude Fable 5 at comparable accuracy - roughly a 7x wall-clock difference ^[1]. That gap matters more than the raw tokens-per-second figure: it's the difference between a benchmark run that finishes overnight and one that finishes before lunch.

Cerebras and OpenAI didn't just publish a speed number in isolation - they published it against Anthropic by name. Citing Artificial Analysis output-speed data, the companies say Ultrafast runs roughly 5x faster than Claude Opus 4.8 in Fast mode and 11x faster than Claude Fable 5 ^[1]. Cerebras CEO Andrew Feldman put it plainly: 'GPT-5.6 Sol on Ultrafast is proof that speed and intelligence are no longer mutually exclusive.' ^[1]Coverage from TechCrunch ^[3]and 9to5Mac ^[4]both led with the 14x figure, a sign that inference speed, not just benchmark accuracy, is becoming a marketable differentiator between frontier labs.

This didn't happen overnight. OpenAI and Cerebras signed a multi-year deal back in January 2026 to deploy up to 750 megawatts of wafer-scale systems into OpenAI's inference stack, phased through 2028 ^[5], ^[6]. Ultrafast is the first visible product of that infrastructure bet, arriving seven months later alongside a formal press release ^[7]. The timing suggests OpenAI viewed raw generation speed as a strategic gap worth a dedicated hardware partnership, rather than something to solve purely in software.

OpenAI's pitch for Ultrafast centers on workflows that break down when a model takes too long to answer: voice applications, customer support, e-commerce, developer agents, financial research, incident response, and cybersecurity threat detection ^[2]. Internally, OpenAI says incident-response investigations that used to take one to two hours now resolve in ten to fifteen minutes on Ultrafast. One researcher described responses returning before he context-switches away from a task ^[1]- a small but telling detail, since context-switching cost, not raw compute, is often the real productivity tax of slow AI tools.

Reception outside the official channels leans toward validating that framing, with a caveat. Early testers describe GPT-5.6 Sol on Ultrafast as a fast, dependable workhorse rather than a leap in raw intelligence, and note it is the first model in the 5.6 line trustworthy enough to run autonomous sub-agent tasks unsupervised. That distinction matters for where the speedup pays off: as more workflows involve parallel sub-agents firing off many small requests rather than one long conversation, the bottleneck shifts from how smart the model is to how fast it can respond - exactly the gap Ultrafast is built to close.

Everything about Ultrafast so far is described as a preview: available first through the OpenAI API to a select group of customers, with no public pricing and wider access planned as capacity grows ^[2]. Businesses that want in have to join a waitlist and describe their workload, latency requirements, and expected usage ^[4]. That gating matters because speed like this is presumably expensive to serve - wafer-scale chips are a scarcer, costlier resource than commodity GPUs - and neither company has said what a 14x speedup will cost end users.

That silence is where public reaction turns skeptical. Absent official pricing, discussion elsewhere has ranged from genuine enthusiasm about the underlying hardware achievement to pointed cost anxiety and even suspicion that standard-mode responses might be paced deliberately slower to make a premium fast tier look more valuable - a claim neither OpenAI nor Cerebras has addressed. Others argue the more interesting bottleneck isn't model speed at all: as parallel sub-agent workflows spread, local infrastructure like compilers and CPU throughput could become the next constraint regardless of how fast the model itself responds. Both critiques point to the same open question the preview leaves unanswered - whether Ultrafast is a genuine step change in how AI gets used, or a capacity-constrained showcase whose real test only comes once pricing and availability go broad.