Jev Alternatives: Local Models, Hardware & Benchmarks

Compare local Jev-style models by GPU and CPU requirements, API support, and independent JevBench results.

Checked: 25 September 2026 · Benchmark snapshot: JevBench v1.4.2, scored 24 September 2026.

Jev itself is a hosted model; this guide compares other projects that bring typed, probability-based decisions to local hardware. “Jev-style” describes a similar task interface, not identical training or decision quality. Start with the machine you have, then compare the evaluation evidence that matches your use case.

Find a model for your hardware · Jump to benchmark results

Quick answer: For a 12 GB NVIDIA card, start by comparing Decider 4B and JevK5 4B. For CPU or smaller devices, look at Laya or Von. For a 16 GB card, Reflex's current stable recipe and Winnow-12B Q8 are options, though Winnow leaves little measured VRAM headroom. Jev still leads Decider on the JevBench decision-quality axes; Decider's speed and cost estimates lift its composite score.

Hardware shortlist

These are practical starting points, not guaranteed minimums. Weight size is only part of peak memory; runtime, context length, batching, and the rest of your system also matter.

Available hardware	Models to investigate	What to keep in mind
CPU or small GPU	Laya, Von, GLiNER2.5-Decide	Laya and Von are compact decision models with CPU paths. GLiNER2.5-Decide is a focused classifier, not a full Jev-compatible replacement.
4–8 GB GPU	Decider 2B, plus the lightweight models above	Decider's project documentation lists about 4 GB for the 2B BF16 model. Leave room for the runtime and your inputs.
12 GB GPU	Decider 4B, JevK5 4B	Published BF16 figures are about 8.4 GB for Decider 4B and about 9 GB for JevK5 4B. Actual peak use varies by setup.
16 GB GPU	Reflex 4B, Winnow-12B Q8	Reflex's current stable setup calls for a 16 GB CUDA GPU. Winnow Q8 reached 14.36 GiB VRAM during one published inference test, so treat it as a tight fit, not a universal guarantee.
24 GB or more	CLM-8B, Bespoke Nimble 9B	CLM's reference setup uses an RTX 4090 but does not establish a universal minimum. Nimble's unquantized weights alone are about 18 GB and its runtime and preparation need more memory.

Apple Silicon

Decider and Reflex document Apple GPU acceleration through MPS; Laya has CPU and MPS device options. Bespoke Nimble also supports Apple Silicon, but its 9B weights alone use about 18 GB before runtime overhead. For any Mac, check the project's current runtime instructions and leave enough unified memory for the operating system and other applications.

JevBench snapshot

Benchmark Heaven's JevBench v1.4.2 scored 534 public and 308 sealed decisions on 24 September 2026. The sealed examples are not released. Its overall score combines Intelligence, Calibration, Speed, and Cost, so it is not an accuracy percentage.

JevBench measure	Decider 4B v2	Jev 1.13.0	JevK5 v0.2.0
Overall score	64.1	63.3	62.0
Intelligence	49.4	53.1	48.9
Calibration	75.0	76.3	74.5
Speed	92.9	83.3	91.1
Cost axis	60.9	52.0	59.5
Public accuracy	83.5%	86.6%	85.3%
Sealed accuracy	34.7%	36.7%	33.1%

The useful reading is not “the highest-ranked model is best at every task.” Jev scores higher than Decider on Intelligence, Calibration, and both accuracy columns. Decider leads the composite because its Speed and Cost axes are stronger; its JevBench cost is an estimate, not a GPU bill. JevK5 is close on the same evaluation, but its sealed accuracy is lower than Jev's.

Other local approaches

Small decision models: Laya and Von are useful when CPU support, low memory use, or fast local calls matter more than matching Jev's benchmark results. Their project benchmarks use different task sets, so do not mix those percentages into the JevBench table above.
Browser experiments: SemIf can run decision experiments in a browser. Its listed model download sizes describe files to download, not guaranteed runtime RAM or VRAM.
Classification and routing: GLiNER2.5-Decide is a compact specialist for classification tasks. Its published 17-domain exact-match benchmark is project-run and is not a general JevBench score.
Agent and verifier workflows: CLM uses a contrastive state/action approach and targets candidate ranking and tool selection. Its fine-tuned verifier results are not zero-shot JevBench comparisons.
High-memory experiments: Bespoke Nimble is a 9B Jev-inspired project with an open training recipe. It is a better fit for a high-memory Mac or GPU than for a typical 8–12 GB gaming card.

Choose by deployment need

Choose hosted Jev when you want TypeSafe to run the model and the current JevBench Intelligence and Calibration results matter more than local operation. Consider Decider or JevK5 when local inference and a TypeSafe-style API are priorities and your GPU has enough headroom. Look at Laya, Von, or GLiNER2.5-Decide when your hardware is constrained, after confirming that the model supports the exact primitive and answer format your application needs.

Before choosing, check the current model version, runtime, supported question types, and hardware notes. Then evaluate a representative labeled set from your own workflow. Keep public benchmark results separate from author-run tests, and do not carry a confidence threshold from one model to another without recalibrating it.

Sources and method

This comparison uses the independent JevBench leaderboard for the shared benchmark snapshot and each linked project's own documentation for model size, runtime, and project-specific tests. Hardware figures are published weights or observed measurements; they are not universal minimum requirements. Recheck the linked project pages before deployment because model revisions and serving recipes can change quickly.