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OpenAI has made its Decisions API generally available to all customers, reconfiguring ChatGPT Luna for lower-latency, lower-cost decision (System One) use cases and adding voice and visual modalities. Initial customer trials (Box, Datadog, Ramp, Speak) show large latency reductions versus LLM calls, while benchmarks place OpenAI’s decision model behind some rivals on certain indices.
TypeSafe AI said it raised $870 million at a $7.5 billion valuation for Jev, a transformer-based model that outputs probabilities rather than text, in a round led by Andreessen Horowitz with Sequoia and DCVC participating. Jev launched on Sept. 15 and the company says a third of Fortune 500 companies are already using it and that it runs faster and uses fewer tokens than LLMs, claims the startup has made about the product.
Amazon Web Services’ Strands Labs released Strands Decider 2B, an open-source, compact decision model inspired by TypeSafe’s Jev that selects between pre-defined options and reports calibrated confidence scores; the model is built on a Qen3.5-2B torso, is small enough to run locally, and was developed by AWS engineer Marc Brooker. The release, which briefly topped Jevbench for its size, arrived the same week OpenAI announced a similar offering and reflects growing interest in lightweight decision models for agentic workflows.
TypeSafe AI released Jev, the first of its "System One" models: a decision-only model that returns typed, probabilistic answers (Choice, Score, Noul) instead of natural language, evaluating multiple typed questions in parallel and emitting probability distributions with confidence values. Jev has a 32,000-token context window, quoted input cost of $0.042 per million tokens (output free), end-to-end latency of 70–500 ms, uses a training method called Reinforcement Learning for Calibrated Decisions, and saw rapid early integrations from Vercel, Netlify and LangChain.
At its Dev Day, OpenAI’s CEO Sam Altman revealed the Decisions API, a limited-preview product that lets the Luna model output probabilities over a predefined set of choices (e.g., classifications or agent behaviors) at high speed and low cost. Media and industry observers note the API resembles TypeSafe AI’s recently released Jev; proponents say such decision-focused models could cheaply monitor or gate agentic actions, though exact capabilities and calibration remain unclear.
Startup TypeSafe AI, co-founded by ex-OpenAI researcher Diogo Almeida, introduced Jev, a model designed to return narrow labels and probabilities (judgments) for developer-defined questions rather than free-form text. TypeSafe says Jev responds in 70–500 ms and is very cheap (listed at $0.042 per million input tokens), but its published benchmarks are limited, comparisons use the company's own workflows, and the model's "no hallucination" claim only applies to output structure, not correctness within allowed choices.
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