JevBench · second fresh regular draw, one row added

Refusals, errors and context-bound failures stay in the full 1,500 denominator. Every row kept its own package, model, runtime and context settings; nothing was re-run for presentation.

NOT DIRECTLY COMPARABLE WITH THE LIVE v1.6.1 BOARD OR WITH THE EARLIER FRESH DRAWS. This release is a different draw with its own small-field median: G_med = 9.839526770355711, frozen from the four v1.6.10 members (Uniphore-4B-Decider does not move it), against 2.573387642438244 on the live field. G_med_flag_gt10 is false. The gap penalty is max(0, 1 - max(0, (gap - G_med) - 8)/100), so this field's threshold is G_med + 8 = 17.84. On the merged live board this draw is placed with offset 0 while its anchor runs are pending, exactly like the earlier fresh draws. Measured, not estimated: re-scored at the live field's median 2.573387642438244, Uniphore-4B-Decider Intelligence 34.152 would be 33.682 (gap 11.947); the four earlier rows are as stated in the v1.6.10 notes: standard_one_3b_sh Intelligence 32.786 would be 31.294 and metask_jev_rain_12b_trained 67.826 would be 67.587; krino-2b and metask_jev_rain_31b_trained are unchanged.

Cost: no item is excluded from the cost basis on this draw. Every row is priced from its own measured usage block at a clearly-labelled ESTIMATE rate fixed before scoring (r99 and r101 pre-registrations); none is a bill any of these systems issued. Every row is under the published v1.6 cost ceiling of USD 0.0646 per 1,000 decisions. The cost normalizer C0 is USD 0.001 per 1,000 decisions with 30 cost points per decade.

No subject-topic radar in this release: this draw's items have not been topic-labelled yet, and the shared topic labels of earlier draws do not cover its items. Families, use cases and languages are authoring metadata and are shown.

v1.6.11 ADDS ONE ROW, Uniphore-4B-Decider, to the four of v1.6.10 on the same draw, under the same rule as the earlier same-draw additions: the field median stays the frozen v1.6.10 value, so all four earlier rows keep identical point scores, axes and capability; ranks and bootstrap confidence intervals are recomputed for the five-row roster, and raw_on_A and the field-level diagnostics (G_med_api_basis_P_vs_A, the API-basis offsets) move for that reason; none of them enters a score.

krino-2b: All 1,500 items answered HTTP 200 with native probabilities. The 15 longest items of the draw (the same 15 that standard_one_3b_sh refuses) ran with the state truncated to 65,536 tokens, as the author's submitted serve command documents (KEV_TRUNCATE_STATES=1); every other item is at most 1,802 input tokens. Reference kernels were used (KEV_FUSED=0) because the documented pip install has no flash-linear-attention. Kev reports usage.output_tokens as the token count of the serialized answer JSON although nothing is generated (165,276 over 1,500 decisions); it is priced as reported at the frozen DeepInfra Qwen/Qwen3.5-2B reference, USD 0.02/0.10 per 1M. Measured on an evaluator-owned Lium H100 pod in an offline container (network none).

metask_jev_rain_12b_trained: All 1,500 items answered HTTP 200. The author's service runs the authors' own int4 (W4A16) quantization of Google's Gemma 4 12B QAT checkpoint with their own rank-16 LoRA applied unmerged by vLLM; per its model card it takes a second pass on part of the items, so it reports generated output tokens (84,238 over 1,500 decisions) and its latency has a long tail (raw p95 8.1 s against a raw p50 of 0.13 s). Measured on an evaluator-owned Lium H100 80GB pod (IP geolocated to Nizhny Novgorod, Russia) in an offline container (network none). google/gemma-4-12b-it is not listed on OpenRouter, so the cost uses the nearest listed sibling, google/gemma-3-12b-it at USD 0.05/0.15 per 1M, times its own usage. It is a different system from the earlier metask_jev_rain_12b row.

metask_jev_rain_31b_trained: All 1,500 items answered HTTP 200. The author's service runs Google's unchanged W4A16 Gemma 4 31B QAT checkpoint with the authors' own rank-16 LoRA applied unmerged by vLLM, at max_model_len 6,656 (per the measuring operator's handoff): longer inputs are shortened by the service's own documented extraction rule rather than refused, so the longest items were answered on a shortened state. The authors tested it on H200; it was measured on an evaluator-owned Lium H100 80GB pod (IP geolocated to Nizhny Novgorod, Russia) in an offline container (network none) with unchanged settings. The service reports 1 output token per decision; cost is the OpenRouter google/gemma-4-31b-it list price (USD 0.09/0.34 per 1M) times its own usage.

standard_one_3b_sh: 15 of 1,500 items returned HTTP 422: they are the 15 longest items of the draw and exceed the model's documented 32,768-token context, so the author's server refuses them rather than truncating; scored wrong per method. Measured on an evaluator-owned Lium H100 pod in an offline container (network none). Priced at the M2 base-model floor, OpenRouter mistralai/ministral-3b-2512 at USD 0.10/0.10 per 1M, because the author's own list price (USD 0.038 per 1M input) is below that floor; the schema head generates no text, so output tokens are 0.

uniphore-4b-decider: 15 of 1,500 inputs exceed the author's documented 16,384-token wrapper limit; the author's code refuses them (HTTP 422, no truncation) and they are scored as wrong per method, so 1,485 items were answered. The native readout is one forward pass, so output tokens are 0 and the cost is the input tokens at the frozen DeepInfra Qwen/Qwen3.5-4B reference, USD 0.03/0.15 per 1M (that listing is flagged deprecated in the frozen snapshot but remains the reference). Measured on an evaluator-owned Lium H100 pod in an offline container (network none), in the vllm/vllm-openai v0.31.0 image with transformers 5.17 and reference PyTorch kernels in place of the documented flash-linear-attention / causal-conv1d kernels, so its latency was measured on the fallback path. The author discloses that the public JevBench set was inspected repeatedly during development.