Hard-tier accuracy
Jev
74.1%
djev
69.5%
On 220 ambiguous cases, Jev made fewer incorrect decisions.
Workload brief · JevBench v1.3.0
Should the next step optimize for speed, images, or confidence?
Jev and djev share a decision-shaped interface. Their strongest reasons to choose them show up at different points in your workflow.
djev pairs lower benchmark latency with native visual input. Jev scores better on hard cases and calibration. Start with what your application does after it receives the answer.
Evidence that changes the decision
The aggregate scores are close; the operating signals are not
JevBench ran both systems through the same decision set. These three dimensions map more directly to production behavior than the composite rank.
Calibration score
Jev
82.7
djev
65.4
A useful signal when thresholds, abstention, or escalation depend on confidence.
Speed score
Jev
83.3
djev
91.4
djev is faster in the JevBench setup; test again from your own region and network.
Route by payload and consequence
Ask what the decision is allowed to do next.
01
The request contains a screenshot, image, or camera frame.
djev fits the inputdjev accepts native visual inputs. Jev expects text or structured text, so an image must be interpreted before the request.
02
A probability gates a refund, payment, or account action.
Validate Jev firstCalibration matters when your code acts on confidence. Keep a human review path and measure false approvals on labeled cases.
03
The decision is simple, but every millisecond matters.
Benchmark in your stackModel latency is only part of end-to-end time. Compare the same payload, region, concurrency, and p95 target.
The practical trade
A confidence-first path versus a multimodal fast path.
Jev · managed decision API
When the output controls software
Keep the integration focused on typed decisions and confidence that you can validate against a threshold policy.
- Hard-tier and calibration results lead this comparison.
- No GPU fleet or model-serving stack to maintain.
djev · hosted preview and community implementation
When the input is visual or latency-led
Native images and camera frames can remove an upstream conversion step; the preview terms and deployment target still matter.
- The hosted preview and the open community repository are separate offerings.
- Treat preview pricing and probability quality as items to verify before launch.
Integration boundary
Check these before choosing an endpoint.
- Input
- JevText, JSON objects, or arrays
- djevText, images, image choices, and camera frames
- Hosted median
- Jev0.65 seconds in JevBench
- djev0.24 seconds in JevBench
- Confidence
- JevCalibration score 82.7
- djevCalibration score 65.4; hosted preview docs call it experimental
- Run model yourself
- JevManaged API
- djevCommunity djev-dev can be self-hosted; hosted service is separate
Sources and scope
Benchmark figures are from JevBench v1.3.0, measured on the same 534 decisions. Latency and calibration can change with hardware, network, and service versions; validate on your own examples.