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Jev is a hosted AI model from TypeSafe AI designed to transform messy text and application context into structured decisions instead of conversational prose. It supports three core decision types: choice, score, and yes or no probability. Choice decisions select one option from up to 255 labeled values and return probabilities for each option. Score decisions place input on an ordered two to ten level spectrum and provide a fractional score with a distribution. Yes or no decisions return calibrated probabilities between zero and one, making them useful for gates, filters, moderation, and safety checks. Jev is designed for high volume software workflows such as support triage, content moderation, lead scoring, résumé screening, data extraction, viral post analysis, model routing, code review risk assessment, context compaction, and agent tool call protection. It evaluates multiple questions in one request and returns typed values that applications can branch on without parsing prose. Claimed response latency is 70 to 500 milliseconds, with some comparisons describing it as 40 to 200 times faster than a large language model. Input pricing is listed at approximately 0.25 to 0.42 dollars per million tokens, with output free. A browser playground is available without signup, while API access is provided through TypeSafe AI and supported gateways. The model is not open source, although related tools and integrations are open.
