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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.
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.