OpenAI's first chip wins by the watt — read per accelerator, two of the three tests go the other way

On 25 August 2026 OpenAI published the first measured results of Jalapeno, the custom inference chip it designed with Broadcom: 1.5 to 1.9 times more AI work per watt and 1.7 to 3.6 times lower end-to-end latency than leading commercial systems, rising to 2.1 to 4.1 times higher performance on highly interactive workloads. Every throughput figure in that comparison is divided by power. Read the same published numbers per accelerator instead, and two of the three tests point the other way. The tests were run on InferenceX, a public benchmark from SemiAnalysis, on three open models: GPT-OSS 120B, DeepSeek R1 670B and Kimi K2.5 1T. OpenAI names both the comparison systems and the power ratings it normalised by: Jalapeno is rated at 700 watts per package, the GB200 it was measured against on GPT-OSS at 1,200 watts, and the GB300 used for the two larger models at 1,400 watts. That leaves enough on the table to do the multiplication. On GPT-OSS 120B, Jalapeno's peak 85,448 mixed tokens per second per kilowatt works out to roughly 59,800 per accelerator, against about 53,900 for the GB200 — an 11% lead. On DeepSeek R1 the same arithmetic gives roughly 13,700 for Jalapeno against 16,500 for the GB300, and on Kimi K2.5 about 12,700 against 16,600. Per chip, on the two largest models, NVIDIA's system still serves 17% and 23% more. OpenAI does not hide the choice; it argues for it. "Although performance is sometimes reported per chip, we believe the more useful standard is performance per unit of power," the company writes — a defensible position, because a data centre is limited by the megawatts it can draw far more than by the number of boards it can rack. The company also reports that Jalapeno's measured sustained draw stayed at or below 550 watts while it was normalised at its 700-watt rating, which means the real per-watt advantage is larger than the published one. The two readings answer two different questions: how much a rack delivers for a given power budget, and how much silicon it takes to get there. One set of numbers survives the normalisation untouched. Latency is measured per request, not per kilowatt: 1.03 seconds end to end against 1.80 on GPT-OSS, and 1.56 against 5.31 seconds on Kimi K2.5, with time between tokens down from 5.48 to 1.44 milliseconds. For agents, which chain many calls in sequence and compound every delay, that is the figure that matters, and it owes nothing to how the power was counted. The development story is unusual in its own right. OpenAI says the chip went from initial design to tapeout in nine months, with its own models helping to explore implementations and optimise the arithmetic circuits, and that Codex running GPT-Astra brought three open-weight models that were never in the production plan up to high performance in two months. On selected GPT-OSS attention and mixture-of-experts blocks, the AI-written implementations ran 1.5 to 1.8 times faster than the human-expert versions — for those blocks, the company stresses, not the whole model. Deployment inside OpenAI's own infrastructure is due to begin by the end of 2026, Gen 2 is described as deep in development and Gen 3 as taking shape, and the company repeats that it will keep buying NVIDIA accelerators for training and inference alike.