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OpenAI has made its Decisions API generally available, a fast, cheap model for picking among options with a confidence score, meant to compete with TypeSafe's Jev. The pitch is that for many workflows, a quick classifier-style model can replace a full LLM.
- OpenAI didn't build a dedicated decision model; it repurposed its ChatGPT Luna model and tuned it for lower latency and cost.
- Speed is the main selling point: response times dropped from 1-3 seconds to roughly 100-180 milliseconds, and Speak cut its action gap from 1.4 seconds to 0.13 seconds.
- Benchmarks don't settle which decision model is best. Jev ranks well on some indices and tops the PacMan-based JevMan test, while OpenAI's model ranks lower, though it may have an edge in image classification.
A new category of AI models that pick answers instead of generating them launched in September 2026 and immediately spawned dozens of competitors. The article cuts through the noise by laying out practical criteria for choosing which decision model to use in production rather than tracking which specific model is "best."
- Jev launched September 15 and hit massive adoption (13% of Vercel's paid customers integrated it within a day), but within two weeks Amazon, OpenAI, Cloudflare, and others shipped competing models with different tradeoffs.
- DecideBench, an independent benchmark testing 400 multiple-choice decisions across eight task families, shows Jev at 98% accuracy for $32 per million tasks, but smaller open models like Laya are 35-59% accurate and much faster—accuracy-speed tradeoffs matter depending on your use case.
- The real decision criteria are deployment location (hosted vs. self-hosted), input types (text-only vs. image support), latency requirements, cost at your actual volume, and API compatibility for future switching—not which model has the best benchmark score.
MotherDuck's Jev is a decision model that classifies text by picking from predefined answers, running 30x faster than GPT at 1% of frontier model costs. The article walks through five real use cases—grading AI agent logs, analyzing Hacker News sentiment, screening sales calls, and others—with actual SQL queries and datasets you can attach and rerun.
- Jev classifies 100,000 items in 82 seconds at ~1% the cost of frontier models by constraining outputs to a closed list you define, returning typed columns with confidence scores.
- On Hacker News mentions, 89% of pre-2023 "Claude" and "Anthropic" matches were false positives (Claude Shannon, the anthropic principle), but Jev's name-checking reduced false positives to 0.8% post-2023 when the products actually launched.
- The pattern separates facts from judgment: SQL extracts exact counts from logs or data, Jev only answers subjective questions like "was this turn efficient?" or "is this negative?", keeping costs down while maintaining accuracy.
Musubi Labs released PolicyLM-1.7B, a small open-source language model designed to make moderation decisions on live messages in under 100ms by reading your platform's actual policies instead of relying on fixed taxonomies. It runs on a single GPU or laptop and lets teams update policies instantly without retraining.
- Processes messages in under 100ms (median 35ms on short chat) while scoring against custom policies, making it viable for live chat where fixed classifiers are blind to policy changes and large LLMs are too slow.
- Reads your policy with every message, so policy edits take effect immediately without retraining or annotation work—useful for teams with frequent rule changes or policies that don't fit standard taxonomies.
- Open weights (Apache 2.0) and already proven in production on a platform handling over a million messages daily; includes two threshold presets (precision-focused for rare violations, balanced for higher detection).
Liquid AI released d1 with image support, a decision model designed for yes/no and classification tasks. It matches or beats GPT-6.1 Sol on most benchmarks while costing 19-200x less and running 200-300ms per decision.
- d1 processes images and text to output probabilities for binary, choice, or scoring questions in a single forward pass without token generation
- Benchmarked against GPT-6.1 Sol and Claude Opus 5.5 on six real applications (support ticket filtering, circuit board inspection, etc.), matching or beating GPT-6.1 Sol on four of them
- Costs 19-200x less than comparable frontier models with significantly faster inference speed
TypeSafe AI's decision model Jev autonomously completed Pokémon Red by reading game memory, evaluating legal moves with game facts, and making every decision—from battle tactics to menu choices—over 37 hours and 16,150 decisions. The entire run cost $1.65 in API tokens, with full source code and setup instructions open on GitHub.
- Jev made every game decision (battles, catching, team swaps, navigation) based on legal options and contextual facts, while the harness only handled emulation and button presses—no cheats or memory writes.
- The project cost $1.65 total at $0.042 per million input tokens, with typical calls around 1,200 tokens and median decision time of 0.4 seconds; the full run consumed ~39.2M input tokens.
- All code is open-source (GPL-2.0) and reproducible locally with your own legally obtained Pokémon Red ROM, Node 20+, and macOS or Linux—no Nintendo assets or ROMs are distributed.
Jevflake is a dbt package that lets you query Snowflake data using TypeSafe AI's Jev decision model, which returns typed answers (yes/no probabilities, category picks, or scored ratings) that you can filter and join like regular SQL. It handles API setup, creates SQL functions, and manages answer caching so you don't re-query the same data.
- Jev returns structured answers (probabilities for yes/no, picks with confidence for categories, weighted scores for ratings) rather than text, making answers usable directly in SQL operations
- The package stores answers in an incremental table keyed by row ID and question, so rows are only sent to the API again if their content changes or you modify the questions
- Setup splits between admin (network/integration) and dbt role (function creation), requiring Snowflake external access enabled and a TypeSafe API key