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Jev+40 Positive

GILD Review

GILD's call and the buying facts behind it.

From GILD research

Should we adopt Jev?

Where should Jev, a typed decision model, sit in your software stack?

  1. 30-Second TakePick Jev for the repeated decisions your software makes where a fixed set of answers settles the call.
  2. Best fitTeams with repeated decisions that currently parse model prose or call a larger language model for every branch.GILD Review
  3. Consider another workflowUse a dedicated classifier when labels are stable, training data is available, and the task needs a narrow optimized model.GILD Review

Drawn from 139 sources and 20 operator insights

GILD analysis

Jev Review

Jev gives software typed choices, scores and yes/no answers with probabilities through one API. Teams run it in production for routing, document choice, reranking and bulk labeling: Jev 1.13 passed 100% across Isotopes AI's three runs of 88 scenarios and Serverpod roughly halved its assistant's time to first token. kapa.ai, Major League Hacking, Cisco and Anaconda kept or chose another model, and Brief's code-review test found none of 13 known problems, so test your own workload with a fallback.

  • AI Model APIs
  • AI Costs
  • Governance

The decision

Best fit
Teams with repeated decisions that currently parse model prose or call a larger language model for every branch.
Consider another workflow
Use a dedicated classifier when labels are stable, training data is available, and the task needs a narrow optimized model.
Adoption condition
Keep confidence thresholds, deterministic fallbacks, and human review around every high-impact action.

Basis: TypeSafe documentation, public practitioner accounts on Reddit, GitHub, engineering blogs, X, Hacker News, podcasts and videos, and 20 named-team accounts, from Isotopes AI's production default to kapa.ai's rejection and dessn's move to Claude Opus 5.5.

Buying & deployment factsWhat a team building on it should verify before production traffic grows.
How you buy it

System One API

TypeSafe serves Jev through an account and API key; output tokens remain free.

Source 1, TypeSafe AI, Models: ModelsSource 2, TypeSafe AI, System One API: System One APISource 5, docs.typesafe.ai, Legal: Legal

Details

TypeSafe serves Jev through its System One API. Jev 1.14, the current model, and Jev 1.13 are available by model ID, with access managed through a TypeSafe account and an auth token.

Cost drivers

US$0.042 per million in

The state plus all questions in a request make up its per-request context.

Source 1, TypeSafe AI, Models: Models

Details

Jev costs US$42 per billion input tokens, or US$0.042 per million input tokens. Output tokens are free. A request's context is the state plus all its questions, and Jev ingests the state once and evaluates every question against it.

Where it runs

System One API

The hosted endpoint accepts text or structured state through TypeSafe's Python and JavaScript SDKs.

Source 2, TypeSafe AI, System One API: System One APISource 3, docs.typesafe.ai, Client SDKs: Client SDKsSource 1, TypeSafe AI, Models: Models

Details

The hosted System One endpoint accepts text or structured state and is available through TypeSafe's Python and JavaScript SDKs.

What to verify

Jev 1.14

Check the model ID, context limits, confidence policy, and fallback before an automatic action.

Source 1, TypeSafe AI, Models: ModelsSource 4, TypeSafe AI, Jev jaggedness: Jev jaggednessSource 5, docs.typesafe.ai, Legal: LegalSource 6, trust.typesafe.ai, Trust Center: Trust Center

Details

The current model is Jev 1.14. TypeSafe says Jev is not trained on customer requests or responses. Zero data retention is available to enterprise customers, and the SOC 2 Type II report is available on request through TypeSafe's Trust Center. TypeSafe says accuracy is best in English, so test non-English content separately. Check its context limits, rate limits, confidence policy, and the fallback for adversarial or uncertain inputs.

Sourced from TypeSafe documentation