JevBench public 231 on Core AI — 2026-09-24 (Mac, M4 Max 128 GB, macOS 27.0 26A428, Xcode 27.0 RC)
Harness: fstandhartinger/jevbench 2fa63fa (v1.4.0) typesafe adapter → coreai-kit adbc755 decide-cli serve (/v1/systemone, port 8097–8099),
one question per request, public items only (easy 48 / original=standard 72 / hard 111). Scoring = jevbench's own (argmax over returned
probabilities, RENORM_TOL band; every row here was strict-valid). Every number was recomputed from results-.jsonl (verify.py);
the jevbench summarize output matched bit for bit. All 8 × 231 requests answered HTTP 200; contract probes 3/3 and hosted-form conformance
25/25 per model (scorer: 20 passed, then the check script's own 60 s client timeout on the 255-option case).
Timing: every run shared the GPU (another session held _GPU_LOCK throughout; r3 ran three serves concurrently). Solo re-check of 30 rows per
r3 model returned bit-identical probabilities (max |Δp| 0.0, argmax 30/30) and ~2× lower latency; a clean timing window is still owed.
| model (kit catalog id, Mac int8, contended) | easy 48 | standard 72 | hard 111 | ECE hard | p50 s (231) | p95 s (231) | hard max s | bundle | note |
|---|---|---|---|---|---|---|---|---|---|
| apus-decision-v1-4b | 1.000 | 0.986 | 0.559 | 0.347 | 5.94 | 142.09 | 217.6 | 5.4G | three model servers on one GPU (30-item solo re-check: identical probabilities) |
| decider-0.8b | 1.000 | 0.833 | 0.414 | 0.320 | 1.69 | 29.10 | 90.6 | 1.2G | server alone |
| laya-multilingual | 0.896 | 0.403 | 0.342 | 0.273 | 0.02 | 0.20 | 0.3 | 649M | encoder s256: hard 95/111 rows truncated at 256 tokens |
| minicpm5-2b | 0.979 | 0.708 | 0.459 | 0.241 | 0.14 | 1.19 | 1.8 | 2.5G | zero-shot chat, catalog temperature 2.93 |
| openthai-systemone | 1.000 | 0.819 | 0.324 | 0.449 | 0.62 | 25.93 | 39.0 | 1.0G | server alone (three short model loads overlapped; 5-item solo re-check identical) |
| qwen3.5-2b | 1.000 | 0.750 | 0.441 | 0.245 | 2.74 | 50.22 | 62.3 | 2.8G | zero-shot chat, no calibration (temperature 1), server alone (kit gate runs overlapped 08:29–09:02) |
| qwen3.5-2b-decision | 1.000 | 0.778 | 0.405 | 0.235 | 3.24 | 92.89 | 138.5 | 2.8G | three model servers on one GPU (30-item solo re-check: identical probabilities) |
| system-one-scorer-4b | 1.000 | 0.861 | 0.505 | 0.164 | 8.96 | 61.61 | 72.0 | 4.7G | author max_len 384/row: hard 70/111 rows state cut; three model servers on one GPU (30-item solo re-check: identical probabilities) |
| published system (same 231 public items, their own serving stack) | easy | standard | hard | p50 s easy/std/hard |
|---|---|---|---|---|
| Jev 1.13.0 (TypeSafe AI) | 1.000 | 0.986 | 0.730 | 0.665/0.653/0.672 |
| Winnow-12B Q8 | 1.000 | 0.958 | 0.730 | 0.203/0.209/0.291 |
| classifier.dev (fast tier) | 1.000 | 0.986 | 0.703 | 0.378/0.387/0.374 |
| decider-35b-a3b (Mapika) | 1.000 | 0.972 | 0.667 | 0.283/0.283/0.312 |
| SemIf, formerly OpenJev (Qwen3.5-4B, TheoLeeCJ) | 1.000 | 0.986 | 0.613 | 0.188/0.188/0.223 |
| reflex 4B (kshetrajna12) | 1.000 | 0.944 | 0.604 | 0.33/1.128/1.879 |
| open-alternative-jev (Qwen3.5-4B, IkerMoel) | 1.000 | 0.833 | 0.568 | 0.191/0.192/0.247 |
| decider-2b (Mapika) | 1.000 | 0.847 | 0.495 | 0.26/0.259/0.268 |
| system-one-open (Gemma 4 E2B LoRA on an L4) | 1.000 | 0.931 | 0.486 | 0.516/0.649/0.682 |
| kev 0.6B (research preview) | 1.000 | 0.806 | 0.432 | 0.421/0.589/0.608 |
| Laya (Convai Innovations, ModernBERT-large 421M) | 0.958 | 0.694 | 0.351 | 0.368/0.382/1.922 |
Footnotes
- laya-multilingual: encoder bundle wfp16-s256 → hard 95/111 rows truncated at 256 input tokens (states up to 3,677 tokens); easy/original untruncated (max 107).
- system-one-scorer-4b: author's encode cuts the state from its end so each row (state + question + option) fits max_len 384 → hard 70/111 rows at the cap.
- minicpm5-2b: kind chat, zero-shot under the kit's JSON decision prompt, catalog calibration temperature 2.93 (fit on SemIf perturbations108).
- decider-0.8b / openthai-systemone / qwen3.5-2b-decision: Qwen3.5-family hybrids, S=1 prefill; a 3k-token hard state costs 40–220 s per question, re-prefilled for every question at this kit commit.
- apus-decision-v1-4b hard p50: 44.0 s over the 55 rows answered while three serves ran, 8.4 s over the 56 rows answered alone.
- Solo subset p50 (recheck30, easy / original / hard): qwen3.5-2b-decision 1.07 / 1.18 / 22.0 s; apus 2.60 / 2.62 / 41.9 s; scorer 2.51 / 2.73 / 20.1 s.
- Published rows: recomputed from jevbench results/v1.2/jevbench-v1.2-per-task.json (per-item outcomes for the same 231 public ids); their runs used the entrants' own serving stacks (bf16 GPU etc.), so same items, not same conditions. JevBench's headline Intelligence/score is chance-corrected over 534 items incl. sealed ones and is not comparable to these accuracies.
- qwen3.5-2b stands in for qwen3.5-4b, which is not in the kit catalog: kind chat, zero-shot under the kit's JSON decision prompt, no calibration entry (temperature 1).
- Solo re-check (30 rows per model of the three-server run + 5 overlapped openthai rows): returned probabilities bit-identical to the concurrent runs (max |Δp| 0.0, argmax 30/30), latency ~2× lower alone.