torchcast-decision-12b (Torchcast AI, Gemma-4-12B fine-tune, option-letter logprob readout)
Data: JevBench v1.6.1, published 2026-10-06
Capability 71.7 · rank #11 of 92 on the open-weights board
Last measured: 2026-10-04 · measurement release: v1.6.1
cc-by-nc-4.0
intelligence
60.5
calibration
82.9
speed
91.7
cost
56.4
Composite (secondary)
69.9 · Composite rank #6
Cost and measurement conditions
$0.028 per 1,000 decisions (estimated)
v1.5.8 fast-lane delivery row (combined v1.5.8 preview, not yet published) cost per 1,000 decisions carried (pricing rules unchanged; v1.6 item lengths differ): ESTIMATE (v1.5-M2 base-model reference; no public tariff, customer asked for the base-model reference): OpenRouter google/gemma-3-12b-it list price $0.05/M input, $0.15/M output (gemma-4-12b-it is not listed on OpenRouter, read 2026-10-04; same reference as the Cygnet and Jev-Omni Gemma-4-12B rows) x the shim's own measured usage.prompt_tokens/completion_tokens; self-hosted on 1x H100 80GB (Lium), vLLM 0.30.0 BF16; estimated, not charged
Measured p50 latency: 0.037 s (on-GPU, no network, loopback; not directly comparable with API latency, which we time end to end from our measuring host, network included)
Ranking and cap use: 0.22 s (est. incl. network (2x + 150 ms))
Self-hosted model: measured p50 0.037 s on-GPU, no network (timed on our GPU over loopback). Not directly comparable with API latency, which we time end to end from our measuring host, network included. The ranking and latency cap use the same estimate for every self-hosted row: 2 × measured + 150 ms = 0.22 s. Model authors can ask us to time a hosted endpoint instead (on request).
x2 + 0.15 s (assumption, not measured)
evaluator-owned Lium GPU pod, 1x NVIDIA H100 80GB, weights fetched in a separate model-only container, scored run inside docker --network none (loopback only, no secrets, label-free inputs); pod destroyed afterwards; paid public fast-lane evaluation, 4 Oct 2026 (order b113eac1)