X · by shree_code
Yes/no, choices and scores, with the published price per token.
Jev does not write, it decides: yes or no, a choice, or a score, each with calibrated confidence in 70 to 500 ms. Input is priced at $0.042 per million tokens and output at nothing, because there is no output text.
Open the source
Shann³
@shannholmberg
how to add a decision model to your AI workflow 1,000 yes/no decisions can cost around: ~$10 on GPT-6 Astra ~$4 on Claude Opus 5.5 ~$0.04 on JEV use language models for planning, writing and coding, and decision models for decisions a decision model can: > check your agent’s output before it ships > approve a draft or request revisions > route a task to the right agent or tool > classify leads, tickets or comments > score fit, quality or priority > check whether content follows your brand rules > pick an option from a shortlist > decide whether to continue, stop or request a review at a much faster speed as well than any regular LLM here’s how to add one 1. find the decisions in your workflow look for repeated steps that need a choice, classification or score start with one decision you can compare against examples you’ve already reviewed 2. define the question and allowed answers give the model the information and references it needs for a content workflow, that could include your draft, voice DNA file, anti slop rules and approved examples ask it to return a choice such as approve, revise or human review 3. connect the answer to an action your software uses that answer to decide what happens next approved drafts move to your review queue, revision requests return to the writing agent, and uncertain cases go to you 4. test before letting it act compare its answers with your reviewed examples if it returns confidence scores, check whether those scores match how often it gets the decision right start with actions you can easily reverse 5. put simple checks first use code for fixed rules, such as required fields and character limits then use the decision model for checks that need context, before starting more expensive work 6. track quality and cost measure cost per completed task, incorrect decisions and how often another model or a person needs to review use the failures to improve your questions, references and allowed answers
XBenchmarks and evals
OpenRouter
@OpenRouter
1/ Jev, a decision model by @typesafeai, sparked a burst of projects and discussion. We tested it using Ori Eval against popular LLMs on OpenRouter at judging. Jev was >5x faster than the next fastest model, and even its slowest requests beat every other model's median.
nader dabit
@dabit3
Also have been playing with @typesafeai Jev, insane! So many immediate use cases and new apps are possible. What a time to be a builder! Sharing some experiments here starting with: Keystroke oracle / predictive launcher: Your launcher ranks by aliases, fuzzy match, and habit. Jev reads intent: type "the pdf I just downloaded" and the newest PDF is already the top hit with a full confidence on every keystroke, in ~100 ms
XBenchmarks and evals