TypeSafe
Jev 1.14
Total Context
32K
Max Output
N/A
Released
N/A
jev-1.13Jev 1.13 is TypeSafe AI's first System One decision model, built for structured AI judgment rather than text generation. It analyzes provided context and returns structured choices, scores, and confidence-based decisions. Designed for classification, routing, evaluation, and automation workflows, Jev delivers fast, predictable AI decisions with low latency and efficient token usage.
Context Window
32K tokens
Release Date
Sep 18, 2026
Modalities
| Input Price | Output Price |
|---|---|
| $0.042/M | $0/M |
Reasoning
Tool calling
Temperature parameter
Attachments
Knowledge Base
Endpoint Protocols
A TypeSafe model designed for fast, bounded decisions inside software.
Returns choices, rubric scores, and yes/no probabilities within developer-defined answer spaces instead of generating prose.
Trained to make probabilities reflect uncertainty, helping applications set review thresholds. Calibration does not guarantee individual correctness.
Reads shared state once and evaluates multiple questions in parallel, supporting collections of small judgments in one request.
Practical workflows built around explicit choices, scoring criteria, and review thresholds.
Classify incoming messages into defined teams or intents, then use confidence thresholds to identify cases needing review.
Apply explicit rubrics to assess frustration or urgency, producing scores that help prioritize a review queue.
Evaluate refund intent, evidence, and policy conditions separately; combine the results with deterministic checks before routing for action or review.
curl 'https://us-api.tokenhub.com/v1/systemone' \
-X 'POST' \
-H "Authorization: Bearer $TOKENHUB_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "jev-1.13",
"questions": {
"department": {
"criteria": {
"billing": "Charges and refunds",
"technical": "Product defects"
},
"instructions": "Which team should handle this ticket?",
"type": "choice"
},
"needs_refund": {
"instructions": "Does the customer explicitly request a refund?",
"type": "noul"
},
"urgency": {
"criteria": [
"Routine",
"Needs attention",
"Critical"
],
"instructions": "How urgent is this ticket?",
"type": "score"
}
},
"state": {
"ticket": "I was charged twice. Please refund the duplicate charge."
}
}'Capabilities, limitations, model selection, and integration considerations.
It is TypeSafe's System One decision model. It accepts text-based state and typed questions, returning structured decisions and probabilities rather than prose.
Choose it for bounded classification, routing, and rubric scoring. Use a generative or reasoning model when you need written answers, code, explanations, or complex multi-step reasoning.
Defined answer spaces make results easier for code to consume. Parallel evaluation and uncertainty estimates help build decision workflows, but valid output types do not guarantee correct decisions.
It accepts text only. TypeSafe documents weaknesses in arithmetic, date comparisons, indirect instructions, and irrelevant long context. Keep exact calculations in code and validate on representative data.
Confirm TokenHub's availability, model ID, and decision-request format before integration. TypeSafe's native API uses POST /v1/systemone; do not assume ordinary chat-completion compatibility.
As verified on September 21, 2026, TypeSafe lists US$0.042 per million input tokens and free output tokens. TokenHub pricing and account availability must be checked separately.
Use one API key to access Jev 1.13 and more AI models through TokenHub.
Jev 1.13 Media and Demos
Verified public links covering Jev 1.13 integrations and the broader Jev family. Community demonstrations are not independent model benchmarks.
X (Twitter)
TypeSafe AI ·
Jev family: TypeSafe announces its public debut and invites developers to access Jev.
we are officially out of stealth! join the frontier and get access to Jev on our website (link on profile)
View originalTypeSafe AI ·
Jev family: the official account announces access without a waitlist and links to the TypeSafe console.
Jev is now available to everyone. No waitlist. Start using it here: https://console.typesafe.ai/
View originalRuirui Wan ·
A community integration uses Jev 1.13 through OpenRouter to guide browser actions in Ego Lite.
https://pbs.twimg.com/media/HSf25e4aoAAv_lv.jpghttps://pbs.twimg.com/media/HSf25exbsAAIdXd.jpghttps://pbs.twimg.com/media/HSf25e2awAAA5uc.jpgView originalReddit
Tried TypeSafe's new "decision-only" model (Jev) as an agent router: 145-271ms end to end, curious if others have tested it
blackbarata ·
Jev family: a developer shares two agent-routing experiments and asks how others handle uncertain decisions.
View originalI built a side project to test TypeSafe's Jev model with no generative LLM that scores your project idea
stemonte ·
Jev family: an open-source demonstration scores project ideas using parallel questions and classifications.
View originalWhat I measured calling Jev (TypeSafe's decision model) through OpenRouter in Hermes
PoppaBear1950 ·
A community report describes integrating Jev 1.13 through OpenRouter, including request formats, response shapes, and error-handling pitfalls.
Read full post
I spent today wiring TypeSafe's Jev — the "System One" decision model that launched on Sept 15 — into a decision gate that routes coding tasks. Most of the time went into finding the endpoint, so here is what's actually true as of 2026-09-20. Everything below is measured, not read off the docs. The endpoint nobody points you at It works on OpenRouter with an ordinary OPENROUTER_API_KEY: POST https://openrouter.ai/api/alpha/decisions Content-Type: application/json Authorization: Bearer $OPENROUTER_API_KEY { "model": "typesafe/jev-1.13", "state": "help! my payouts have been failing for 3 days", "questions": { "tier": { "type": "choice", "instructions": "pick a tier", "criteria": { "billing": "payments", "technical": "bugs" } } } } Response: { "model": "typesafe/jev-1.13-20260917", "answers": { "tier": { "type": "choice", "choice": "billing", "probabilities": { "billing": 0.87, "technical": 0.13 }, "confidence": 0.82 } }, "usage": { "input_tokens": 312, "output_tokens": 35, "cost": 1.31e-05 }, "provider": "TypeSafe" } The traps, each one measured: /chat/completions with typesafe/jev → 400 "is not a valid model ID". Jev does not generate text, so it isn't an autoregressive chat model. Don't try to shim it into standard completions. /api/v1/decisions → 404. The route is currently live under /api/alpha, not /api/v1. Wrapping the body in {"decisionsRequest": {...}} → 400 invalid_type ... path: ["model"]. The body must be flat: model, questions, and state at the root level. GET /api/v1/models (447 entries) lists no typesafe/* ID, and GET /api/v1/models/typesafe/jev-1.13 404s. Yet GET /api/v1/models/typesafe/jev-1.13/endpoints returns 200 with the full provider snapshot, pricing, context, and uptime. Any SDK preflight asserting "does the model exist in the list?" will falsely fail. Make a live call instead. There is no typesafe/jev-latest alias on OpenRouter; that alias throws an error. typesafe/jev-1.13 is the only working ID, and every response names the dated snapshot it served. Measured latency and cost: 239–430 ms per decision, $1.3–2.7e-05 per invocation (roughly $13–27 per million decisions). Output is unmetered. The answer shapes are not uniform choice gives you choice + probabilities + confidence. noul (yes/no) does not: {"type": "noul", "noul": 0.01} For noul, the value is a flat scalar float between 0.0 and 1.0 representing true/yes likelihood (e.g., 0.01 means overwhelmingly false), not a structured dict with probability labels. I had code reading answers.x.probabilities.true, which silently defaulted to 0.0 — so a "does this patch match the intent?" gate answered no to everything, including good patches. If you port Jev snippets around, check that field shape. The failure mode worth designing against My first version of the gate had the classic fallback: except Exception: return choices[0]. With a live key pointed at the wrong route, every call 400'd and the gate answered anyway: triage("refactor auth across 14 modules") → bash_direct risk("auth models, 31 dependents") → low (would have auto-applied) verdict(exit 1, "could not read Username") → complete (would have ended the loop) Confident, cheap, and wrong in the direction that costs you. The fix that stuck: the gate returns unavailable plus a default chosen from the stakes — fail open to the cheapest path for low-stakes routing (restricted strictly to non-mutating checks), fail closed (high, abort) for the risky ones — and the process exit code explicitly indicates whether you got a model verdict or a policy default. Two things I'd have missed without an end-to-end smoke test git diff is blind to files a coding agent creates: they're untracked, so a patch review gate sees an empty diff and rejects a perfectly good change. Use git add -N . && git diff (or git add -A && git diff --cached, but remember to unstage cleanly). I pointed the harness log inside the worktree, so __pycache__ and the run log itself landed in the diff. The gate called that scope creep (noul 0.47 vs 0.94 for the clean version) and it was completely right — plus the messier diff cost 2x because you're billed on input token size. Keep run logs outside your worktree or in .gitignore. Happy to share the routing-gate code if anyone wants it; it's stdlib Python and talks directly to the OpenRouter alpha endpoint. Verify the alpha route before you build on it — I measured this on one box today.
YouTube
Jev Explained: Demos and Use Cases
Syntax
Jev family: an introductory walkthrough explains the model with demonstrations and practical use cases.
View originalWhat is Jev and How to Use it?
Codevolution
Jev family: a developer tutorial introduces structured AI decisions and using Jev in applications.
View originalI Tested Jev on 12 Real Use Cases. My Honest Thoughts.
Nate Herk | AI Automation
Jev family: a creator explores twelve use cases and shares impressions from trying the model.
View original