RoboKrunch reports that 300 Jev API decisions cost a total of $0.00737 in a benchmark run on September 19, 2026. Each call returned three judgments for a simulated warehouse-robot incident: whether to escalate to a person, which team should handle it, and an urgency score. The result is a small cost-and-latency measurement—not evidence that Jev can safely triage faults on working robots.
What the benchmark tested
RoboKrunch sampled 300 incidents from 41 bilingual Chinese- and English-language templates describing warehouse autonomous mobile robot (AMR) failures. The simulated scenarios included degraded LiDAR, localization drift, battery faults, missed pallet detections, network partitions, and people entering restricted zones.
For each incident, the benchmark called OpenRouter’s decisions endpoint with typesafe/jev-1.13. It asked Jev for three outputs: whether to escalate to a human, the team responsible, and urgency on a 0–2 scale. The project says it had not tested TypeSafe’s native POST /v1/systemone endpoint. See the RoboKrunch project README for its setup and files.
Measured results from the 300-call run
The figures below are RoboKrunch’s reported results for that run, dated September 19, 2026. “Agreement” means agreement with the simulated templates’ labels, not accuracy against real-world failures.
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| Measure | Reported result |
|---|---|
| Successful decisions | 300 of 300 |
| Latency, median (p50) | 0.527 seconds |
| Latency, 95th percentile (p95) | 0.813 seconds |
| Mean latency | 0.558 seconds |
| Mean input size | 584.9 tokens |
| Cost per decision | $0.0000246 |
| Cost per million decisions | $24.57 |
| Total spend | $0.00737 |
| Team-label agreement with templates | 274 of 300 (91.3%) |
The 91.3% figure is specifically team agreement with template labels. RoboKrunch says no real robot hardware was involved and explicitly cautions that agreement on simulated templates is not production accuracy. The author, chorylee, likewise described the result as sub-second triage with three judgments per call and said it does not establish production accuracy in the DEV Community post published September 20, 2026.
What the fleet-scale cost estimate means
RoboKrunch modeled a fleet of 10,000 robots making 48 decisions per robot per day for 30 days: 14.4 million decisions per month. Applying its benchmark cost, the project estimated Jev at $353.81 per month. It estimated GPT-4o-mini at $1,814.40 per month, or roughly 5.1 times as much.
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Those are modeled costs, not a head-to-head test. The GPT-4o-mini figure uses assumed token counts and public prices; the project did not measure that model’s latency or triage quality. Actual API spend will depend on a deployment’s input sizes, request volume, pricing at the time, and any other applicable charges.
How Jev compared with the self-hosted classifier
RoboKrunch also tested answerdotai/ModernBERT-base, a 149-million-parameter model, on a two-core AMD EPYC CPU. Its setup used batch size one, a frozen encoder, mean pooling, nearest-centroid classification, and 18 hand-written exemplars. That test covered 100 incidents.
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| Comparison | Jev API run | ModernBERT test |
|---|---|---|
| Reported p50 latency | 527 ms | 169.3 ms |
| Outputs per call | Three judgments: escalation, team, urgency | One label |
| Test size | 300 decisions | 100 incidents |
| Setup described | OpenRouter decisions endpoint; no training data stated as needed for the run | Frozen encoder, mean pooling, nearest-centroid classification, 18 hand-written exemplars |
The latency numbers do not compare identical outputs: Jev returned three judgments per call, while ModernBERT returned one label. The repository’s estimated self-hosting break-even is about 977,000 decisions per month, assuming a $24-per-month four-vCPU virtual machine. It calls a roughly 2.9-million-decision crossover for three outputs directional, not measured. The estimate excludes engineering time, so it is not a universal point at which self-hosting becomes cheaper.
What this does—and does not—show about deployment
The benchmark is useful as a narrow API cost-and-latency observation under a defined simulated workload. It does not validate whether the chosen escalation, team, or urgency judgments are safe or correct during actual warehouse incidents. RoboKrunch also reports no edge-NPU test, long-duration soak test, or probability-calibration study.
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Before using any model as a fleet decision layer, an operator would need evaluation on representative real incidents, clear human-escalation rules, and operational safeguards appropriate to the consequences of a wrong decision. The reported template agreement alone cannot establish those properties.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to reproduce the experiment
RoboKrunch’s repository includes demo scripts, per-call data, aggregate results, charts, and a video. Reproducing Demo A requires an OpenRouter API key. The listed ModernBERT weights are approximately 599 MB. The project identifies its code as MIT-licensed and benchmark data as CC-BY 4.0; consult the repository for the applicable terms and the exact run artifacts.
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