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OpenAI released the Decisions API, a beta tool that evaluates text and images to return typed answers—probabilities, category selections, or scored ratings—10x faster than the Responses API. It's built for content classification, request routing, and work prioritization without needing to generate free-form text.
- Three question types handle different tasks: predicates return probability estimates for yes/no conditions, choices select from fixed categories, and scores rate inputs against ordered severity levels using probability-weighted averages.
- The API costs $0.10 per 1M input tokens with no output charges, supports HIPAA and Zero Data Retention for eligible customers, and currently runs only on gpt-6-luna model via a dedicated endpoint.
- Real use cases include flagging product photos with visible damage (predicate), routing customer complaints to departments (choice), and assessing bug severity for triage (score).
Cloudflare open-sourced two decision models called Clef and Clef-flash that classify inputs into typed, structured outputs with probabilities — useful for routing customer support tickets, identifying phishing domains, or triggering automated agent actions. The models outperform Typesafe's Jev system on speed and benchmarks while supporting images and a 64k context window.
- Clef processes decisions 2x faster than general LLMs (2.2s vs 4.7s in Cloudflare's threat intelligence workflow) because it scores schema choices in parallel rather than generating text token-by-token.
- The models beat Jev on most Jev Decision Index benchmarks and Typesafe's own eval suite, with Clef-flash offering exceptional speed-to-quality tradeoffs; median latency is 209ms for Clef and 38.8ms for Clef-flash.
- Both models are open-sourced on Hugging Face under Apache 2.0, hosted on Workers AI at Cloudflare's edge for low network latency, and fully API-compatible with Jev for easy switching.
TypeSafe AI released Jev, a model that takes text input but outputs confidence scores and probability distributions instead of generated text. It's designed for classification tasks like spam detection and ranking, costs $0.042 per million input tokens with free output, and has already spawned open-source recreations and creative experiments.
- Jev returns structured probabilistic outputs (yes/no scores, rankings, category choices) rather than text, making it fast and cheap—you only pay for input tokens, not output.
- The model trades interpretability for simplicity: you get a floating-point score but no explanation of which signals drove the decision, raising serious concerns about hidden bias in high-stakes use cases like hiring.
- The community has already built unconventional projects with it (a chat model, a game player, a left-pad implementation) and created open-weight alternatives like Kev within days of release.
TypeSafe AI released Jev, a new model class designed for automation that outputs type-safe structured decisions instead of text, running 40-200x faster and cheaper than existing LLMs on decision-making tasks. Unlike traditional language models, Jev can't hallucinate, always provides confidence scores, and costs nothing for output tokens.
- Jev achieves comparable intelligence to frontier LLMs on decision tasks while being 40-200x faster (70-500ms vs 3-329 seconds) and drastically cheaper ($0.042 per billion input tokens, free output tokens vs $0.20-$10 per million input tokens for LLMs).
- The model uses a new training method called Reinforcement Learning for Calibrated Decisions (RLCD) that optimizes for accurate probability estimates rather than human preference, and generates all outputs in parallel instead of sequentially, eliminating hallucinations and type errors.
- Jev trades away general text generation to specialize in structured outputs that slot directly into software workflows as fuzzy decision rules—classifying, routing, scoring, and branching without requiring human validation or parsing.
TypeSafe AI released Jev, a new type of AI model designed for automation and structured decision-making rather than text generation. It's 40-200x faster and 444x cheaper than existing large language models for specific tasks, with guaranteed type-safety and calibrated confidence scores instead of hallucinations.
- Jev generates all outputs in parallel rather than token-by-token, achieving 70-500ms response times versus 3-329 seconds for frontier models, while outputting structured data instead of strings
- The model uses a new training method called Reinforcement Learning for Calibrated Decisions (RLCD) that optimizes for epistemically honest probability estimates rather than human preference, making it reliable enough to embed in production software workflows
- Pricing is $0.042 per billion input tokens with free output tokens, versus $0.20-$10 per billion for existing models, with claims backed by publicly available workflow evaluations showing performance across complex automation tasks