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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteIn Marcelo Taparelli’s reported test, Jev 1.13 scored higher than a deterministic rules baseline and a local Ollama classifier on several measures across 70 synthetic tickets. That is a promising result for further evaluation—not proof that Jev is more accurate on real tickets or ready to replace an application’s existing triage system.
What Taparelli tested
Taparelli evaluated three approaches on the same taxonomy and 70 synthetic held-out labels drawn from the project’s historical benchmark: deterministic rules, a local Ollama classifier, and Jev 1.13. The task was to assign each ticket a category, priority, and risk level.
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He made development calls before freezing the configuration and evaluation at commit 4c41e0b, then ran the held-out set once for the official result. Freezing the setup helps limit direct tuning against the test set, but the evidence still comes from a small synthetic sample and one official run. Taparelli’s benchmark article describes the setup and results.
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Jev was accessed through a separate adapter to OpenRouter’s Decisions API, using typed responses and probability distributions. The article reports that all 73 API responses resolved to typesafe/jev-1.13-20260917. The adapter implemented the project’s TriageClassifier interface but remained separate from the Ollama classifier and HybridPolicy; Jev was not integrated into the application flow or used to replace the local model.
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This distinction matters when interpreting the comparison. Jev is documented as a structured decision model: it receives state and typed questions and returns typed answers with probabilities, rather than producing free-form explanations. Jev’s plugin documentation describes that model and the hosted integration path.
How the three approaches scored
These are the reported results on the 70 synthetic tickets, not production estimates.
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| Measure | Deterministic rules | Local Ollama | Jev 1.13 |
|---|---|---|---|
| Category accuracy | 0.8286 | 0.9571 | 1.0000 |
| Category macro-F1 | 0.8512 | 0.9550 | 1.0000 |
| Priority accuracy | 0.9000 | 0.9143 | 0.9857 |
| Risk accuracy | 0.9571 | 0.9143 | 0.9571 |
| HIGH/CRITICAL priority recall | 0.7857 | 1.0000 | 1.0000 |
| HIGH risk recall | 0.5714 | 0.7143 | 0.8571 |
Jev got the category, priority, and risk tuple right in 66 of 70 cases (0.9429). Taparelli does not report a standalone exact-tuple score for either the rules classifier or Ollama in the historical benchmark, so this figure cannot support a like-for-like exact-tuple ranking.
What the latency and cost figures cover
For Jev’s 70 standalone calls in this OpenRouter run, Taparelli reports a mean latency of 569.4 ms, a median (p50) of 545.5 ms, a p95 of 712.2 ms, and a maximum of 1,142.2 ms. The run used 90,229 input tokens, had a reported total cost of US$0.003789618, and had no API or schema failures.
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The historical Ollama benchmark did not report standalone cost or latency. Its 6–7-second figure covers the full hybrid path, not one standalone Ollama call, so comparing it directly with Jev’s standalone-call timing would mix different measurement boundaries.
Why this does not establish production superiority
The result supports continuing evaluation, not declaring a winner. A perfect category score on this set means Jev classified all 70 examples correctly for that field in this run; it does not show how it will perform on real tickets or on a broader distribution of cases. One official run also cannot reveal run-to-run variation, and exploratory calibration metrics do not establish that Jev’s probabilities are trustworthy for operational decisions.
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A separate MLflow-authored evaluation illustrates why results depend on the task and evaluation design; it is not a replication of Taparelli’s ticket-triage test. On a 72-answer set in English and Japanese, MLflow reports Jev agreed with human labels on 64 of 72 answers in both runs, while GPT-OSS-120B agreed on all 72. The report also describes accepted answers containing material errors and cautions that an exploratory confidence-routing threshold was selected after observing the same data. Read the separate MLflow evaluation; its figures should not be treated as a direct ranking for triage.
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Taparelli’s stated next steps are to expand and diversify labeled data, repeat the frozen evaluation to measure variance, and set human-review and severity-error acceptance criteria before considering integration. For a useful comparison, each system should be tested on the same labeled examples and the same task boundary. Include:
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- Per-field accuracy and macro-F1, plus exact-tuple accuracy when every system reports it.
- Recall for high-severity cases, with the seriousness of missed or misclassified cases assessed explicitly.
- Abstention and human-review behavior, so an uncertain prediction is not treated like a confident one.
- Confidence calibration tested on separate examples, rather than choosing a routing threshold after inspecting the evaluation set.
- Latency and token or inference cost measured over equivalent requests and paths.
- Repeated frozen runs to measure variation, along with checks for subtle, almost-correct errors.
- Data-handling constraints and how easily deterministic policy rules can be enforced.
The existing rules baseline and Ollama classifier remained part of the application’s evaluation and hybrid policy in the described architecture; Jev remained evaluation-only. The benchmark is evidence that a third decision approach is worth examining under controlled conditions, not that it should replace either existing component.
Implementation details to verify before use
Jev’s documentation distinguishes the pinned jev-1.13 model from the rolling jev-latest alias and recommends logging the returned build version when reproducibility matters. The cookbook describes sending only the state fields needed to answer the typed questions, documenting relevant domain knowledge in state, instructions, or criteria, and using a Decisions endpoint separate from standard chat completions. Model documentation and the cookbook guide explain those details. Aliases, API limits, billing, and endpoint behavior can change, so check current provider documentation before implementing or deploying.
The documented plugin path sends judged state to OpenRouter and TypeSafe because Jev is hosted. Depending on the task, that state could include ticket text, code, or customer records. This describes the integration path; it does not establish every provider’s retention or privacy terms. Review the current terms of the relevant providers before sending sensitive information. The plugin documentation describes the hosted arrangement.
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