Jev is seriously impressive. SERV makes it better. We put Jev through the same benchmark we use to evaluate leading AI models, testing it both standalone and armed with SERV Reasoning. Paired with SERV, Jev moves into the top tier of our benchmark, beating Claude Fable 5 at 30x lower cost, with cost similar advantages over GPT-5.5 (~15–20x), Gemini 3.5 Flash (~15x), and Grok 4.3 (~6–7x). The result is clear: Jev + SERV materially outperformed Jev on its own. Jev is extremely fast, well designed, and great at decision-making. Instead of relying on open-ended responses, it produces structured decisions with probability scores attached. SERV pushes that performance further, adding a stronger reasoning layer to improve the quality of those decisions for high volume agentic work. The SERV Reasoning API is live, so any developer can test and deploy SERV Reasoning today in just a couple minutes. Get access at:
openserv.aiThe agentic economy won’t scale on better models alone. It needs better reasoning infrastructure that are finetuned to the reliability, auditability, and affordability requirements of enterprise agents. SERV is building that layer. After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
00:00