Jev and Kev compared on the same questions
Source: opper-ai/jev-vs-kev ↗Benchmark code and a 362-question test set that sends each item to Jev and to Kev 4B, and reports accuracy, calibration, latency and cost.
Decision Models / Kev
OPEN
Kev
By Jared Palmer · Released 1 Oct 2026
Kev is Jared Palmer's family of open, Jev-compatible decision models, from 0.8B to 27B parameters. A pointer head on a Qwen base scores each option, and the API matches TypeSafe's, so their SDK works against a local Kev server. Kev 1.0, released 1 Oct 2026, pins the family's checkpoints, model cards, evaluation suites and serving code; it introduces no newly trained weights.
Facts
- Sizes
- 0.8B, 4B, 9B, 27B
- Licence
- Apache-2.0
- Base
- Qwen3.5 (27B: Qwen3.8)
- Release
- Kev 1.0, 1 Oct 2026
- Stars
- 9,758 on GitHub (GitHub API, 2 Nov 2026)
- Price
- $0.042 per million input tokens and $0 per million output tokens for jaredpalmer/kev-4b on OpenRouter (VENDOR-REPORTED, 2 Nov 2026) · Compare prices
- Validated context
- VENDOR-REPORTED: 65,536 tokens for Kev-27B; 8,192 for 0.8B, 4B and 9B, measured on CUAD contracts
Claims and sources
- Kev 1.0's breadth-v1 held-out-dataset test scored a chance-corrected index of 23.3 for 0.8B, 38.0 for 4B, 41.0 for 9B and 52.3 for 27B, against Jev's 54.0, in the maker's fp32 evaluation at shipped temperatures. No Kev trained on these datasets. vendor reportedsource
- Kev-27B's 65,536-token validated context is the longest tested state whose CUAD accuracy stays within three percentage points of its 8k-token accuracy at the 95% lower bound. The smaller models already miss that tolerance at 16k. vendor reportedsource
Links
Source: GitHub → Source: Kev 1.0 release → Source: Hugging Face → Source: Kev 4B on OpenRouter → Source: GitHub API: stars →
Related projects
LinkedIn feed filter
Source: GuitarWag/linkedin-focus-filter ↗A Chrome extension that hides LinkedIn posts that don't match your stated interests, using Kev 0.6B running in the browser on WebGPU. Post text never leaves the browser.