Simon Willison writes about Jev, a new category of models from TypeSafe AI called "System One models" or decision models. Unlike standard large language models that output text, Jev accepts unstructured input and returns structured probabilistic decisions such as floating-point numbers for yes/no questions (Noul), choices between options, or numeric scores. These models are designed to be extremely fast and inexpensive, charging only for input tokens while providing free output.
- Jev is optimized for classification tasks like spam detection, ranking, and labeling.
- The model's "Noul" question type refers to the Bernoulli distribution.
- Using such black-box decision models raises concerns about hidden biases that are difficult to audit without explanations.
- There is an emerging trend of open-weight recreations of Jev-class models, including projects like Kev and benchmarks like JevBench.
- Jev is optimized for classification tasks like spam detection, ranking, and labeling.
- The model's "Noul" question type refers to the Bernoulli distribution.
- Using such black-box decision models raises concerns about hidden biases that are difficult to audit without explanations.
- There is an emerging trend of open-weight recreations of Jev-class models, including projects like Kev and benchmarks like JevBench.
