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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteRoboKrunch reports spending $0.00737 on 300 successful Jev API decisions in a benchmark run dated September 19, 2026. Each call returned three judgments about a simulated warehouse-robot incident: whether to involve a human, which team should own it, and how urgent it was. The result is a small cost-and-latency observation—not evidence that Jev can safely triage faults on working robots.
What the 300-call Jev test did
RoboKrunch sampled 300 incidents from 41 bilingual Chinese- and English-language warehouse autonomous mobile robot (AMR) failure templates. The scenarios covered issues such as LiDAR degradation, localization drift, battery faults, missed pallet detection, network partitions, and people entering restricted zones.
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For each incident, the benchmark requested three outputs: whether to escalate to a human, the responsible team, and urgency on a 0–2 scale. The calls went through OpenRouter’s decisions endpoint using typesafe/jev-1.13. The project says it did not test TypeSafe’s native POST /v1/systemone endpoint. RoboKrunch’s repository includes demo scripts, per-call data, aggregate results, charts, and a video.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →“Real” describes the API calls, not the incidents: the benchmark used simulated templates and involved no real robot hardware. The distinction matters because a service can respond consistently to generated examples without demonstrating that it will recognize, prioritize, or safely route unfamiliar failures in a live fleet.
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Measured results from the run
| Measure | RoboKrunch result |
|---|---|
| Successful decisions | 300 of 300 |
| Latency | p50: 0.527 seconds; p95: 0.813 seconds; mean: 0.558 seconds |
| Mean input size | 584.9 tokens |
| Cost per decision | $0.0000246 |
| Cost per million decisions | $24.57 |
| Total run spend | $0.00737 |
| Team agreement with template labels | 274 of 300 (91.3%) |
These are results for the project’s particular API run, model identifier, endpoint, and inputs—not a universal Jev price or a guarantee of future latency. The 91.3% figure means team agreement with the benchmark’s template labels. RoboKrunch explicitly cautions that it is not production accuracy on real failures.
What the cost could mean at fleet scale
RoboKrunch modeled a fleet of 10,000 robots making 48 decisions per robot per day over a 30-day month: 14.4 million decisions monthly. Under that workload, it estimated Jev at $353.81 per month and GPT-4o-mini at $1,814.40, about 5.1 times as much.
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The GPT-4o-mini figure is a cost estimate based on assumed token counts and public prices, not a measured comparison run. The project did not measure GPT-4o-mini’s latency or triage quality, so the figures establish neither equal performance nor a quality advantage for either option. Your own costs would depend on decision volume and token usage.
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Jev versus a small 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. This comparison involved 100 incidents.
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| Measure | ModernBERT setup | Jev API run |
|---|---|---|
| Reported p50 latency | 169.3 ms | 527 ms |
| Outputs per call | One label | Three judgments |
| Evaluation size | 100 incidents | 300 decisions |
| Operating approach | Self-hosted classifier with hand-written exemplars | API decision service |
The latency numbers are not an apples-to-apples measure of equivalent work: ModernBERT returned one label per call, while Jev returned three judgments. The project’s estimated self-hosting break-even is about 977,000 decisions per month, assuming a $24-per-month four-vCPU virtual machine. A three-output crossover near 2.9 million decisions per month is described by the project as directional, not measured. The break-even calculation also does not include engineering time.
How to interpret the benchmark before using it
- It measures a narrow workload. The incidents came from a defined set of simulated templates, so the agreement result does not establish behavior on diverse, real operational failures.
- It is not a safety case. The project reports no real-hardware validation, edge NPU test, long soak test, or probability-calibration study. A robot fleet should not treat these results alone as grounds to automate escalation or urgent response.
- The price comparison is conditional. The fleet projection depends on its assumed decision volume and token profile; the LLM comparison is modeled rather than measured.
- The alternatives differ in scope and responsibility. Compare output requirements, latency, volume, real labeled examples, tuning effort, and who operates the service—not just cost per call.
Reproducing the experiment
The repository provides runnable demo scripts and benchmark artifacts. Re-running Demo A requires an OpenRouter API key. The listed ModernBERT weights require an approximately 599 MB download. The repository identifies the code as MIT-licensed and the benchmark data as CC-BY 4.0. A reproduction can help check the reported setup, but it cannot turn template agreement into evidence about real-fault performance.
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For the author’s original account of the run and its caveats, see chorylee’s September 20, 2026 DEV Community post.
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