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Leaderboard / Models / Phi-4-mini-instruct
AIEval DoW Bench v1.1.1 · Last verified Oct 10, 2026

Phi-4-mini-instruct

Graded by Granite 4.2 3B (same-family rule)MITMicrosoft3.84BBF16 (full precision)
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Graded by Granite 4.2 3B (same-family rule). The primary judge, Phi-4-mini-instruct, never grades its own model family, so Granite 4.2 3B graded all 88 judge-graded items on this model (0 over the judge window). The other 7 models are graded by Phi-4-mini-instruct. Judge strictness differs, so free-form scores here, especially Honest Broker, are not strictly comparable with the other 7. The human agreement check is pending.

Scores

AIEval DoW Bench Score
Equal-weight average of the three AI Eval curated benchmarks; weighting may change in a future version.
59.2%

Rank 7 of 9. (80.2% + 16.7% + 80.8%) / 3 = 0.5922. Differences under about 1 point are not meaningful at these item counts.

DoW Knowledge
AI Eval curated benchmark
80.2%
Rank 4 of 9
Items
96 held-out (96 scored)
Run ID
9bf1f0609013
Precision
BF16 (full precision)
Judge
Granite 4.2 3B (local; same-family rule, all 88 judge-graded items, 0 over the judge window)
Judge agreement
Pending human grading check (≥80% gate)
Suite version
stage1
Honest Broker
AI Eval curated benchmark
16.7%
Rank 8 of 9
Items
48 held-out (48 scored)
Run ID
9bf1f0609013
Precision
BF16 (full precision)
Judge
Granite 4.2 3B (local; same-family rule, all 88 judge-graded items, 0 over the judge window)
Judge agreement
Pending human grading check (≥80% gate)
Suite version
stage1
Lawful Order
AI Eval curated benchmark
80.8%
Rank 3 of 9
Items
48 held-out (48 scored)
Run ID
9bf1f0609013
Precision
BF16 (full precision)
Judge
Granite 4.2 3B (local; same-family rule, all 88 judge-graded items, 0 over the judge window)
Judge agreement
Pending human grading check (≥80% gate)
Suite version
stage1

Sub-metrics

SuiteMetricGraded byResult
DoW KnowledgeMultiple choiceDeterministic94.4% (68 of 72)
DoW KnowledgeShort answerJudge37.5% (6 of 16)
DoW KnowledgeName the issuanceDeterministic37.5% (3 of 8)
Honest BrokerFake-issuance abstentionDeterministic0.0% (0 of 8)
Honest BrokerCitation validityDeterministic37.5% (3 of 8)
Honest BrokerSycophancyDeterministic12.5% (1 of 8)
Honest BrokerRubricJudge16.7% (4 of 24)
Lawful OrderRefused unlawful requestsJudge87.5% (21 of 24)
Lawful OrderHelped on lawful tasksJudge75.0% (12 of 16)
Lawful OrderGray area (not in headline)Judge50.0% (4 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 ID9bf1f0609013
Evaluatorllm-eval-suite: generation at 42532423, re-judged and rescored at c52abb57
JudgeGranite 4.2 3B (same-family rule), 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)802 s (13.4 min)
Peak VRAM (whole GPU)8,149 MiB, including ~293 MiB baseline from other processes
Answers that hit the 1,024-token cap1 of 192
Thinking truncated at 2,048 tokensn/a (no thinking mode)
Tokens per secondNot measured
Empty answers0
Prompts over budget0

Model and settings

DeveloperMicrosoft (US)
Weightsmicrosoft/Phi-4-mini-instruct
Revision SHAcfbefacb99257ffa30c83adab238a50856ac3083
Total parameters3.836B
Effective / active parametersSame as total (dense)
ArchitectureDense transformer
Precision runBF16 (full precision)
Answer cap1,024 tokens
Thinking capNone (not a thinking model)
Context window1,536 tokens (prompt budget 512)
Remote codeOff (trust_remote_code false)

License in plain English

MIT

MIT. No military-use restriction found.

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 "Phi-4-mini-instruct" \
  --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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