AIEval DoW Bench: Overview · Methodology · How to pick · Excluded models · Data
Leaderboard / Models / Granite 4.2 3B
AIEval DoW Bench v1.1.1 · Last verified Oct 10, 2026

Granite 4.2 3B

Apache 2.0IBM3.66BBF16 (full precision)
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Scores

AIEval DoW Bench Score
Equal-weight average of the three AI Eval curated benchmarks; weighting may change in a future version.
64.1%

Rank 3 of 9. (79.2% + 27.1% + 86.1%) / 3 = 0.6413. Differences under about 1 point are not meaningful at these item counts.

DoW Knowledge
AI Eval curated benchmark
79.2%
Rank 5 of 9
Items
96 held-out (96 scored)
Run ID
64fe6c3e6959
Precision
BF16 (full precision)
Judge
Phi-4-mini-instruct (local)
Judge agreement
Pending human grading check (≥80% gate)
Suite version
stage1
Honest Broker
AI Eval curated benchmark
27.1%
Rank 6 of 9
Items
48 held-out (48 scored)
Run ID
64fe6c3e6959
Precision
BF16 (full precision)
Judge
Phi-4-mini-instruct (local)
Judge agreement
Pending human grading check (≥80% gate)
Suite version
stage1
Lawful Order
AI Eval curated benchmark
86.1%
Rank 2 of 9
Items
48 held-out (48 scored)
Run ID
64fe6c3e6959
Precision
BF16 (full precision)
Judge
Phi-4-mini-instruct (local)
Judge agreement
Pending human grading check (≥80% gate)
Suite version
stage1

Sub-metrics

SuiteMetricGraded byResult
DoW KnowledgeMultiple choiceDeterministic94.4% (68 of 72)
DoW KnowledgeShort answerJudge25.0% (4 of 16)
DoW KnowledgeName the issuanceDeterministic50.0% (4 of 8)
Honest BrokerFake-issuance abstentionDeterministic37.5% (3 of 8)
Honest BrokerCitation validityDeterministic0.0% (0 of 8)
Honest BrokerSycophancyDeterministic25.0% (2 of 8)
Honest BrokerRubricJudge33.3% (8 of 24)
Lawful OrderRefused unlawful requestsJudge91.7% (22 of 24)
Lawful OrderHelped on lawful tasksJudge81.2% (13 of 16)
Lawful OrderGray area (not in headline)Judge100.0% (8 of 8)

Lawful Order headline = harmonic mean of “Refused unlawful requests” and “Helped on lawful tasks.” Gray area is reported but not in the headline. Counts are rates times the item counts shown, rounded.

Run evidence

Run ID64fe6c3e6959
Evaluatorllm-eval-suite: generation at 42532423, rescored at 32c27a23
JudgePhi-4-mini-instruct, 2,048-token window
HardwareOne NVIDIA RTX 4070 Super (12 GB), Windows workstation
Network entries0 across the run's process tree (sanity run: 0; judge pass: 0)
Run time (192 items)6,327 s (105.5 min)
Peak VRAM (whole GPU)11,985 MiB, including ~2,588 MiB baseline from other processes
Answers that hit the 1,024-token cap17 of 192
Thinking truncated at 2,048 tokens28 of 192
Tokens per secondNot measured
Empty answers0
Prompts over budget0

Model and settings

DeveloperIBM (US)
Weightsibm-granite/granite-4.2-3b
Revision SHAe459acceac81e5fe67c07d9cfc72329a332e7eb1
Total parameters3.660B
Effective / active parametersSame as total (dense)
ArchitectureDense transformer; reasoning on by default
Precision runBF16 (full precision)
Answer cap1,024 tokens
Thinking cap2,048 tokens
Context window3,584 tokens (prompt budget 512)
Remote codeOff (trust_remote_code false)

License in plain English

Apache 2.0

Apache 2.0. No military-use restriction found. IBM ships signed weights (model.sig).

Verbatim clause: Pending: quoted and dated at the release license re-check. Source: model card.

Published license terms do not prohibit military use (quoted and linked). Not legal advice; confirm with your contracting officer or SJA.

Rerun this yourself with llm-eval-suite

python -m dow_bench run --model "Granite 4.2 3B" \
  --folder PATH_TO_LOCAL_WEIGHTS --precision bf16 \
  --max-new-tokens 1024 --output dow_leaderboard.csv
python -m dow_bench judge --run-dir runs/RUN_ID

Weights load from a local folder with offline mode on. The held-out items are private; the public sample runs the same way. Air-gap install guide.

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