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In Marcelo Taparelli’s reported test, Jev 1.13 scored highest on category and priority accuracy against a deterministic rules baseline and a local Ollama classifier—but the evaluation covered only 70 synthetic tickets. It is a useful benchmark result, not proof that Jev will perform better on real support tickets or in production.
What Taparelli tested
Taparelli compared three approaches using the same taxonomy and 70 synthetic held-out labels from the project’s historical benchmark: deterministic rules, a local Ollama classifier, and Jev 1.13. Development calls came before the freeze. At commit 4c41e0b, he froze the configuration and evaluation, then ran the held-out set once for the official result. This separation helps limit direct tuning against the test set, but the sample remained small and the official result came from a single run. Taparelli’s benchmark report
Jev was called through a separate adapter to OpenRouter’s Decisions API, using typed responses and probability distributions. The adapter implemented the TriageClassifier interface but stayed separate from the Ollama classifier and HybridPolicy; it was not integrated into the application flow or used to replace the local model. Taparelli reports that all 73 API responses resolved to typesafe/jev-1.13-20260917. The 73-response count is distinct from the 70 standalone benchmark calls reported for timing and cost.
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Jev is described as a structured decision model: it receives state and typed questions, then returns typed answers with probabilities rather than free-form explanations. Jev’s plugin documentation
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How the three systems scored
These figures are Taparelli’s reported results on the 70 synthetic tickets, not estimates established for production performance. The table preserves the metrics reported for each system:
| Metric | 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’s category accuracy was 1.0000 in this run, and its priority accuracy was 0.9857. Its risk accuracy, 0.9571, tied the rules baseline. On high-severity cases, Jev and Ollama both had 1.0000 HIGH/CRITICAL priority recall; Jev had higher HIGH risk recall than either comparator in this sample.
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Jev returned the correct category, priority, and risk tuple in 66 of 70 cases (0.9429). Taparelli’s historical benchmark did not report standalone exact-tuple accuracy for rules or Ollama, so this number cannot be used to rank Jev against their per-field metrics or against the hybrid path’s exact tuple.
Latency and reported cost
For Jev’s 70 standalone calls in this OpenRouter run, Taparelli reports mean latency of 569.4 ms, p50 of 545.5 ms, p95 of 712.2 ms, and a maximum of 1,142.2 ms. The run used 90,229 input tokens, cost a reported US$0.003789618 in total, and had zero API or schema failures. These are measurements from that run, not a price or service guarantee.
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The historical Ollama benchmark did not report standalone latency or cost. Its 6–7 seconds describe the full hybrid path, not one standalone Ollama call, so comparing that figure directly with Jev’s single-call timings would mix different measurement boundaries.
What the result establishes—and what it does not
The benchmark shows that a third decision approach produced measurable results under this frozen test setup. It does not establish performance on real tickets, performance across the domain’s full range of cases, or superiority in production. One official run also cannot show run-to-run variance. The exploratory calibration metrics do not validate that Jev’s confidence values are reliable for real decisions.
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A separate MLflow-authored evaluation is a caution about task dependence, not a replication or direct comparison for ticket triage. On a set of 72 answers in English and Japanese, MLflow reports Jev agreed with human labels on 64 of 72 answers in both runs, while GPT-OSS-120B agreed with all 72. MLflow also describes accepted answers containing a material error and warns that an exploratory confidence-routing threshold was selected after the same data had been observed. MLflow’s evaluation
That separate result does not overturn Taparelli’s ticket benchmark; it illustrates why a strong score on one task and dataset is not a general ranking of decision models.
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How to evaluate a follow-up fairly
Before choosing an implementation, compare systems on the same labeled examples and the same task boundary. A useful next evaluation would cover:
- Per-field accuracy and macro-F1, plus exact-tuple accuracy if every system reports it.
- Recall for high-severity cases and the severity of errors, not just their count.
- Abstention and human-review behavior, including what happens when a system is uncertain.
- Confidence calibration on a separate evaluation set; do not choose and validate a routing threshold on the same examples.
- Latency and token or inference cost across equivalent request paths.
- Variance across repeated frozen runs, using a larger and more diverse labeled set.
- Data handling and privacy constraints, along with how easily deterministic policy constraints can be enforced.
- Subtle near-misses and almost-correct answers, which can look acceptable while containing a material error.
Taparelli’s stated next steps are to expand and diversify labeled data, repeat the frozen evaluation to measure variance, and define human-review and severity-error acceptance criteria before considering integration. In the architecture described in his report, rules and Ollama remain in the application’s evaluation and hybrid policy; Jev remains benchmark- and evaluation-only.
What Jev is, and what to check before using it
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 pinned version and the exact build returned in this benchmark are not interchangeable descriptions: the benchmark reports the specific build string listed above. Model aliases, API limits, billing, and endpoint behavior can change, so verify current provider documentation before implementation. Jev model documentation
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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 minuteThe cookbook guide documents an OpenRouter Decisions endpoint separate from the standard chat-completions endpoint. It advises sending only the state fields needed by the questions and putting relevant domain knowledge in the state, instructions, or criteria. Jev cookbook guide
The documented plugin path sends judged state to a hosted model through OpenRouter and TypeSafe. Depending on the application, that state could include ticket text, code, or customer records. This describes the documented integration path, not every provider’s retention or privacy terms; check current terms and data-handling requirements before sending sensitive information. Jev’s plugin documentation
Quick Recap
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