Jev knowledge base·verified September 22, 2026 for Jev; October 2, 2026 for independent models

open jev models and compatible apis

Evaluate open System One and Jev-compatible projects by weights, licenses, API coverage, calibration, benchmarks and operational risks.

the short answer

No equivalence should be assumed. A project may reproduce Jev’s Choice, Score or Noul request shape while using different weights, training data and objectives. Evaluate “open” separately for weights, inference code, training code, data and license. Then test API behavior, probabilities, calibration, limitations, latency and security on the same held-out tasks before calling an implementation compatible or substitutable.

Compatibility
Matching interface behavior
Equivalence
Matching quality and semantics; requires evidence
Open dimensions
Weights, code, data and licenses
Verified model cards
Perplexity Decider, Laya, Kev-9B, Intern-Decision and Julia 1
Required proof
Artifact inspection plus independent benchmark

“Open Jev” Can Describe Several Different Things

These claims form no automatic ladder. Public weights can lack training data; a compatible endpoint can remain hosted and closed; a reimplementation can share primitives while behaving very differently.

ClaimEvidence required
Open clientRepository and license for code that calls an API
Compatible APIDocumented request/response conformance tests
Open-weight modelDownloadable weights and model license
Reproducible modelArchitecture, training code, data lineage and procedure
Jev-equivalent behaviorIndependent task, calibration and stress benchmarks

Treat Community Projects as Named Implementations

Perplexity publishes a Decisions API and model card. Laya, Kev-9B, Intern-Decision and Julia 1 publish separate model cards and local paths. Eric Zhang’s openjev-sglang repository now identifies his earlier experiment and directs users to SGLang’s native decisions endpoint. None of these models is a TypeSafe release unless TypeSafe explicitly says so.

When a repository is available, record its owner, commit, license, model artifact and last activity. Avoid the generic phrase “open Jev” in benchmark charts; use the exact implementation and checkpoint name.

Named Independent Projects Checked October 2, 2026

Ollama lists Nimble and Tev1 with a local /v1/systemone call and requires Ollama 0.35 or later for each. Its listed context windows describe serving limits, not validated decision quality at that length. This is an artifact and provenance inventory, not a quality leaderboard. The dated launch roundup separates hosted APIs from independent models and local runtimes.

ProjectPublic evidenceEditorial status
Perplexity DeciderOfficial API reference and model cardHosted API plus separate open model
LayaModel card and source runtimeLocal model family; language claims need slice tests
Kev-9BModel card and source serverLocal compatible endpoint; thresholds must be refit
Intern-DecisionInternLM model cards, weights and inference sourceMultimodal family; publisher benchmark needs replication
Julia 1Supersonic Labs model card and ONNX/WebGPU artifactSmall local model; Jev figures are supplied references
VevSource, weights and v0.1 model cardImage-capable preview; weights restrict commercial use
JevAltSource, language-specific weights and resultsEnglish, Turkish and German; author-trained test data caveat
GutsySource, GGUF weights and local serverCPU-oriented; author-run performance claims
Mapika deciderSource and checkpoint linksIndependent model family; pin checkpoint
Bespoke NimbleSource and model linksIndependent model project; verify artifact access
Together Tev1Recipe and model linkExperimental release; verify artifact access
Firelex JeffSource and model linksLocal model with task adapters
PostHog JeevesSource and evaluationReasoning-plus-decision experiment
Ollama Nimble and Tev1Official model listings and local /v1/systemone examplesServing route for Nimble and Tev1, not a new model
OllayaServer and registry sourceRuntime, not a model
SGLangNative /v1/decisions and /v1/systemone docsServing framework; no special decision checkpoint required
openjev-sglangSource and migration noteEarlier experiment; README points to SGLang’s native API

Test the Protocol Before Testing Model Quality

Protocol compatibility reduces integration effort. It says nothing yet about calibration or semantic accuracy. Keep a conformance suite separate from the evaluator benchmark.

  • Does the server accept the same state types and primitive definitions?
  • Are Choice criteria, Score levels and Noul answers encoded the same way?
  • Do distributions normalize and expose the same fields?
  • Are batching, limits and error semantics compatible?
  • Does the client require adapter logic that changes the evaluation?

Build Conformance Fixtures Around Observable Behavior

A conformance suite should use synthetic inputs and assert schema and failure behavior, not demand identical probabilities. Preserve native responses beside the normalized representation so a parser bug is distinguishable from a model disagreement. The HTTP API guide defines the direct observable contract and error handling covers retry boundaries.

FixtureAssertion
Choice with three candidatesSelected key belongs to the candidate set and every documented probability is preserved
Score with three ordered levelsLegend, level distribution and expected value map consistently
Noul with positive and negative criteriaResponse exposes the documented yes-probability shape without invented confidence
Several independent questionsEvery question ID maps back to exactly one typed answer
Invalid state or questionThe adapter exposes a stable error instead of manufacturing a default answer
Timeout or overloadThe caller can distinguish no result from a negative semantic decision

Evaluate Behavioral Substitution on Identical Evidence

Run TypeSafe Jev and each alternative on blind held-out labels with equivalent questions and state. Compare class errors, full distributions, calibration, repeatability, language slices, adversarial cases, throughput and total cost. Pin the hosted provider and open checkpoint.

Do not copy thresholds between implementations. Even when both return 0.8, the empirical error associated with that number may differ. The benchmark protocol defines the required reporting.

A Substitution Report Needs More than Agreement

Raw agreement with Jev is not ground truth: two systems can agree and both be wrong. Score each against blind labels or downstream outcomes first, then use disagreement analysis to explain differences. Publish the exact checkpoint, quantization, runtime, hardware, provider region and date so another team can reproduce the result.

MeasureWhy it matters
Task accuracy or ordinal errorTests whether each model matches independent labels
Pairwise disagreementFinds examples where one implementation changes the action
Calibration and Brier/log lossTests whether probabilities support thresholds
Coverage at equal riskCompares how much traffic each can automate safely
Perturbation stabilityTests paraphrases, irrelevant text and adversarial additions
Latency, throughput and failuresMeasures the deployed service rather than model quality alone
Total costIncludes hardware or provider fees, retries, review and operations

Open Deployment Changes the Risk Owner

AreaQuestions
Supply chainWho published weights and dependencies; are artifacts signed?
LicenseAre model, code and data terms compatible with the use?
ServingWho patches the runtime and isolates tenants?
TelemetryWhat inputs, outputs and errors are retained?
UpdatesHow are checkpoints evaluated and rolled back?

Use Precise Language in Public Comparisons

  • Say “Jev-compatible API” only after conformance testing.
  • Say “System One-inspired” when the project adopts the interface idea.
  • Say “open weight” only when weights and their license are available.
  • Never attribute community benchmarks to TypeSafe Jev without running TypeSafe Jev.
  • Publish the exact commit, checkpoint, provider and date.

FAQ

Is Open System One made by TypeSafe?

The cited repository is a separate community project. Do not present it as an official TypeSafe model release.

Does API compatibility prove calibration parity?

No. It proves an interface contract, not matching probabilities, accuracy, training or failure behavior.

Can an open alternative be self-hosted?

Only if its weights, runtime and licenses permit it. Verify those artifacts and benchmark the exact checkpoint.

Should thresholds transfer from Jev?

No. Fit thresholds independently because probability meaning and calibration may differ.

Sources

Checked against the sources below on September 22, 2026 for Jev; October 2, 2026 for independent models. Model versions, prices and limits change.

  1. Eric Zhang on X: Jev-compatible public API
  2. Community index: awesome-jev
  3. TypeSafe AI docs: Primitives
  4. TypeSafe AI docs: Jev 1.13 jaggedness
  5. Pineau et al.: Improving Reproducibility in Machine Learning Research
  6. Perplexity: Decisions API quickstart
  7. Convai Innovations: Laya model card
  8. Jared Palmer: Kev-9B model card
  9. InternLM: Intern-Decision-4B model card
  10. Supersonic Labs: Julia 1 model card
  11. Vev: model source and license notes
  12. JevAlt: model source and evaluation
  13. Gutsy: local decision model
  14. Mapika: decider source and model links
  15. Bespoke Labs: Nimble source
  16. Together: Tev1 source and training recipe
  17. Firelex: Jeff source and release notes
  18. PostHog: Jeeves source
  19. Ollama: Nimble model listing and local API
  20. Ollama: Tev1 model listing and local API
  21. Ollaya: local decision-model runtime
  22. SGLang: native decision APIs
  23. Eric Zhang: openjev-sglang source

Change note: Added named model sources and separate Ollama, Ollaya and SGLang serving routes.