Postar

OpenAI's early Jalapeño tests point to a potentially meaningful shift in inference economics: 1.5x-1.9x more throughput per watt and 1.7x-3.6x lower end-to-end latency on open models. GPT-5.6 Sol also reportedly used 54% fewer output tokens than a leading rival on coding tasks. The measured judgment is that efficiency gains could improve unit economics, but company-tested benchmarks are not yet proof of lower aggregate costs. With $6.7B in Q2 revenue against a $12.3B operating loss, deployment at scale and workload growth will matter more than headline performance. Not advice, just analysis. #OpenAIInferenceCostTest