Nemotron Diffusion 8B (pst2154, optimized vLLM)
Data: JevBench v1.6.1, published 2026-10-06
Capability 47.5 · rank #60 of 92 on the open-weights board
Last measured: 2026-10-04 · measurement release: v1.6.1
MIT (application code); modified vLLM runtime Apache-2.0; nvidia/Nemotron-Labs-Diffusion-8B keeps its upstream licence
intelligence
20.4
calibration
74.5
speed
93.8
cost
55.5
Composite (secondary)
7.3 · Composite rank #85
Cost and measurement conditions
$0.030 per 1,000 decisions (estimated)
v1.5.4 published cost per 1,000 decisions carried (pricing rules unchanged; v1.6 item lengths differ): documented hosted-model estimate; no exact base-model floor applies
Measured p50 latency: 0.022 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: 0.19 s (est. incl. network (2x + 150 ms))
Self-hosted model: measured p50 0.022 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 = 0.19 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 (H100), offline read-only container