Composite score
Jev
74.4
SemIf
73.1
A 1.3-point spread is small enough that workload fit should lead the decision.
Serving decision · JevBench v1.3.0
Within 1.3 points overall. Your serving model decides the rest.
SemIf is close to Jev on the composite, leads on one evaluation tier, and gives you control of the runtime.
The headline rank hides a real trade: SemIf is an open implementation that you operate; Jev is a managed endpoint. Compare the difficult cases and the cost of keeping a GPU available.
Evidence that changes the decision
A close composite can conceal a wide hard-case gap
The published board shows a split result: SemIf leads the judge tier slightly, while Jev leads the ambiguous hard tier. Match the slices to the failure cost in your product.
Judge-tier accuracy
Jev
94.5%
SemIf
95.2%
SemIf edges this 146-case tier by 0.7 percentage points.
Hard-tier accuracy
Jev
74.1%
SemIf
59.5%
Jev leads on the 220 ambiguous cases; include those in your own holdout set.
Price the whole serving path
A token or decision price is only one line in the operating budget.
01
You need an endpoint this week and do not own GPU operations.
Jev shortens the pathA managed API avoids provisioning, capacity planning, model updates, and idle accelerator cost.
02
Data must remain in your network and the team can own inference.
SemIf fits the boundaryAn open implementation gives you control of hosting and the serving environment.
03
You expect steady high volume on a GPU that is already busy.
Compare total costRun a load test and include idle time, batching, monitoring, and on-call effort—not just active GPU seconds.
Who owns the runtime?
This is the primary architectural difference between the two options.
Jev · hosted service
Pay for decisions, not an idle GPU
The provider owns model hosting. Your team owns application logic, input quality, and validating thresholds on its data.
- One managed endpoint and a versioned model name.
- No accelerator provisioning or inference stack to patch.
SemIf · open implementation
Own placement, throughput, and change control
The team can place inference near private data and tune deployment to its traffic, with corresponding operations work.
- Published benchmark setup uses a Qwen3.5-4B model on an RTX 3090.
- GPU availability, quantization, batching, and model updates affect the result.
Fair test plan
Use the same inputs, then measure the system around the model.
- Hard examples
- Jev74.1% correct in JevBench
- SemIf59.5% correct in JevBench
- Evaluation tier
- Jev94.5% correct
- SemIf95.2% correct
- Serving
- JevManaged production API
- SemIfSelf-hosted GPU or browser demo
- Operating cost
- JevUsage-based credits
- SemIfGPU time plus engineering and idle capacity
Sources and scope
JevBench v1.3.0 ran the same decision set for both systems. The reported scores are not a guarantee for your label set; validate on representative and high-cost failures.