JevBench
An open benchmark suite for comparing Jev-class typed decision models across correctness, latency, reliability, and cost.
By JevBench contributors · reviewed 2026-09-26
Deep profile checked 2026-09-26 · JevBench official repository, tagged benchmark releases, mappings, and methodology records
What JevBench does
JevBench runs a frozen collection of bounded decision requests against hosted and local decision systems, records endpoint and revision conditions, and publishes comparable result rows. It is useful as an independent evidence source and as a template for building a private golden set. Its public ranking is not a substitute for workload-specific evaluation because option counts, languages, latency paths, prices, and task distributions may differ from a real application.
Pricing model
Free and open source; model/provider costs may apply
Position in the stack
Where JevBench fits
JevBench is the evidence and regression layer for decision engines. It does not serve decisions to an application; it runs a frozen protocol, records conditions, and helps compare systems before a team builds its own representative golden set.
Typical deployment flow
A practical sequence for evaluating JevBench before making it part of a permanent stack.
- STEP 1
Inspect the pinned protocol
Review the frozen decisions, scoring rules, endpoint mappings, versions, and cost assumptions.
- STEP 2
Reproduce relevant systems
Run candidates under equivalent hardware, network, and model revisions where possible.
- STEP 3
Add a private golden set
Measure real routing, guardrail, smart-home, or agent decisions and preserve failures as regression cases.
Best for
- Comparing decision engines under one frozen protocol
- Tracking rapidly changing Jev-compatible projects
- Designing an internal decision-model evaluation set
Not the right layer for
- Selecting a model solely from one public aggregate score
- Replacing application-specific acceptance tests
- Running a decision model by itself
System fit
Requirements and hardware notes
Application requirements are separate from the memory needed by the selected model and context window.
Check your hardwareMinimum baseline
- Git and the benchmark's documented runtime
- Access to at least one compatible decision endpoint
- Credentials or local hardware required by each evaluated system
Recommended baseline
- Pinned benchmark tag and participant revisions
- Comparable endpoint geography and hardware
- A separate private golden set
Hardware-specific notes
- The benchmark controller is lightweight; evaluated local models determine accelerator needs.
- Hosted participants require no local GPU but include network latency.
- Comparing local and remote systems requires recording geography and endpoint conditions.
Installation and deployment paths
Choose one path that matches the number of users and the level of operations you can maintain.
Data boundary
What stays local and what may leave
- Local data
- Public benchmark items are stored with the suite; private extensions should remain in a controlled evaluation environment.
- Network dependencies
- Hosted participants and author-operated endpoints receive the submitted benchmark state.
- Accounts and telemetry
- Provider billing, logs, telemetry, and retention apply whenever a hosted endpoint is evaluated.
License checkpoints
- Verify the repository license and each included dataset or result artifact separately.
- Participant models and providers retain independent licenses and terms.
Operational checkpoints
- Use an immutable benchmark tag for every published conclusion.
- Record failed requests rather than silently retrying or substituting models.
- Separate correctness, calibration, speed, cost, and reliability instead of relying only on one aggregate rank.
Capabilities
Capabilities refer to the tool's application layer. Hardware and model support can still depend on a connected inference engine.
Deployment and compatibility
- Deployment
- Self-hosted
- Platforms
- macOS · Windows · Linux
- Hardware backends
- Depends on evaluated model or provider
- Install methods
- Git repository · Benchmark scripts
Models and integrations
- Model formats
- Jev-class HTTP endpoints · Local adapters
- Common integrations
- JevLayaNimbleOpenDecisionLocal decision servers
Strengths
- Independent cross-project evidence
- Pinned conditions and reproducible result records
- Measures more than headline accuracy
Limitations
- Rankings change quickly as endpoints and models are added
- Some systems are supplied through author-operated endpoints
- Network location can materially affect latency results
Planning checklist
Before you choose JevBench
Answer these questions with the exact models, hardware, users, and data you expect to operate.
- Does the public task distribution resemble the intended workflow?
- Were model revisions, temperatures, thresholds, and endpoints pinned?
- Does network geography distort the latency comparison?
- Can the result be reproduced before it affects a recommendation?
JevBench FAQ
Is the top JevBench model automatically the best choice?
No. Aggregate rank reflects one frozen suite and its cost and latency assumptions. Hardware, language, option count, risk, and workload distribution can change the result.
Does JevBench provide model weights?
No. It links to or calls participating systems and records benchmark evidence; each model or provider remains a separate dependency.
Why keep an internal benchmark too?
Production decisions have different labels, ambiguity, costs, and failure consequences. A private golden set measures the behavior that actually matters.
Official sources
Use these links to confirm current compatibility and installation requirements.
Related tools
Execution evidence
Known working recipes using JevBench
Recipes connect hardware, a model artifact, tools, settings, verification, and a reportable result.
No verified recipe is linked to this record yet.
Compatibility estimates remain available in the planner. A recipe appears here only after its exact stack and verification protocol are documented.