Imajev-9B (mohit67890, Qwen3.5-9B LoRA)
Data: JevBench v1.6.39, published 2026-10-22
Capability 62.4 · Not ranked in the current release
cost 6.3× Jev (v1.5 reference)
Last measured: 2026-10-16 · measurement release: v1.6.24
Apache-2.0 (adapter; base Apache-2.0)
intelligence
36.7
calibration
88.1
speed
79.5
cost
30.8
Composite (secondary)
9.8 · Composite rank #132
Cost and measurement conditions
$0.203 per 1,000 decisions (estimated)
ESTIMATE (*est.), base-model market reference through the frozen pricing_v15 module: declared base Qwen/Qwen3.5-9B -> qwen/qwen3.5-9b USD 0.09999999999999999/M input, USD 0.15/M output. Estimated, not charged, not a quote from the author or any host. Frozen pricing_v15: base-model references unchanged since sha256 dcfcecb0...2835, the module this row was first priced with (the later re-freezes only add hosted-API launch-list keys), USD per 1,000 decisions over the common 1,485-item cost basis (the 15 very long items are left out of cost for every row; refused items cost nothing and still count as wrong answers). Tokens: input = 4-pass sum counted offline (see disclosures), output 0. OVER THE OFFICIAL COST CAP, DISCLOSED: USD 0.202728 per 1,000 decisions on the common basis (USD 0.200701 over all 1,500 items) is 3.138x the official cap USD 0.064595; the row is ranked (v1.6.16 vllm-sr-d3 precedent) and falls outside the official-caps board views by construction. COMMON COST BASIS (v1.6.30): cost per 1,000 decisions is computed on the common 1,485 items of this draw, leaving out the 15 very long items (at least 76,358 input tokens as counted by the rows that answered them). They are the 15 items the Jev reference refused with max_tokens_exceeded, which the other rows that did not answer them refused or failed on; their tokens count for no row, whether the row refused or answered them, and a refused item still counts as a wrong answer for Intelligence. This row: USD 0.202728 per 1,000 decisions (3.138x the official cost cap USD 0.064595), against USD 0.200701 over all 1,500 items (v1.6.29).
Measured p50 latency: 0.383 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: 0.92 s (est. incl. network (2x + 150 ms))
Self-hosted model: measured p50 0.383 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 = 0.92 s. Model authors can ask us to time a hosted endpoint instead (on request).
x2 + 0.15 s (assumption, not measured)
Evaluator-owned rented Lium GPU pod (measurement receipts on file), 1x NVIDIA RTX 5090 32 GB (sm_120, driver 595.99.02, Ukraine node substituted at rental, pre-registration Amendment 2) - different hardware from the RTX PRO 6000 / H100 of the earlier runs, so Speed is as measured on this card and not comparable to the earlier rows. Python packaging: uv venv from a hashed lock at the old pins (declared deviation). Isolation: author server started inside a network namespace with loopback only (unshare -n), the cut proven before and after the sealed input was staged, synthetic smoke gate passed in between; runner unchanged (run_v15.py --resume --resume-keep-failures); one use of input-selfhosted-1500 (custody #20150, under the pre-registration). Published Speed uses the standing x2 + 0.15 s self-hosted adjustment. Author's own scripts/playground/server.py --backend torch, 4 option orders, 4,096-token request limit.