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TypeSafe AI releases Jev model for type-safe structured decisions
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TypeSafe AI has launched Jev, a frontier model designed for structured outputs and calibrated decisions rather than string generation.
The model replaces sequential token generation with a parallel sampler to reduce latency and cost. It targets software automation by providing type-safe values and confidence scores that eliminate the need for parsing and validation.
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Jev uses Reinforcement Learning for Calibrated Decisions to provide outputs with epistemically honest probabilities.
The model architecture utilizes a parallel sampler to achieve response times between 70ms and 500ms.
Output tokens are free, while input tokens are priced at $0.042 per million tokens.
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TypeSafe AI has introduced Jev as part of a new class of System One models. Unlike standard LLMs that optimize for human preference via RLHF, Jev is trained using Reinforcement Learning for Calibrated Decisions. This shift moves the model away from chat-based string generation toward producing typed probabilistic decisions.
Adopting Jev requires a change in how software interacts with AI, as the model cannot generate strings. Instead, it outputs type-safe structured values where the possible outputs and structure are defined in advance. This removes the requirement for developers to parse and validate responses, as the model is designed to avoid type errors and hallucinations.
The operational cost is significantly lower than existing frontier models, with input tokens costing $0.042 per million and output tokens being free. Performance gains come from a parallel sampler that generates all outputs in a single query. This results in end-to-end response times of 70ms to 500ms, compared to the 3 to 329 seconds seen in other frontier models.
Jev stops working for use cases requiring flexible text generation, such as chatbots or copilots. It is specifically optimized for AI-powered workflows and smart if-statements where unstructured state is converted into structured decisions. The model is intended to function as a frontier-intelligence function call within ordinary software.
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