AIEval DoW Bench: Overview · Methodology · How to pick · Excluded models · Data
Leaderboard / Models / LFM2.5-2.6B
AIEval DoW Bench v1.1.1 · Last verified Oct 10, 2026

LFM2.5-2.6B

≥$10M revenue needs a paid licenseLiquid AI2.70BBF16 (full precision)
Not an ATO, endorsement, or approval for any network. AI Eval Corp is an independent company. This leaderboard is not sponsored, endorsed, or approved by the Department of War or any government agency. A listing is not an ATO or an authorization for any network.
52 of 192 items hit the 2,048-token thinking cap, and 3 judge items were over the judge's 2,048-token window. Same settings as every model.

Scores

AIEval DoW Bench Score
Equal-weight average of the three AI Eval curated benchmarks; weighting may change in a future version.
62.8%

Rank 5 of 9. (56.2% + 42.5% + 89.5%) / 3 = 0.6278. Differences under about 1 point are not meaningful at these item counts.

DoW Knowledge
AI Eval curated benchmark
56.2%
Rank 9 of 9
Items
96 held-out (96 scored)
Run ID
e636eb6b719f
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
42.5%
Rank 4 of 9

1 rubric item exceeded the judge window and is left out of this score (47 of 48 scored).

Items
48 held-out (47 scored)
Run ID
e636eb6b719f
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
89.5%
Rank 1 of 9

2 lawful-task items exceeded the judge window and fell back to the keyword/pattern check.

Items
48 held-out (48 scored)
Run ID
e636eb6b719f
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 choiceDeterministic58.3% (42 of 72)
DoW KnowledgeShort answerJudge68.8% (11 of 16)
DoW KnowledgeName the issuanceDeterministic12.5% (1 of 8)
Honest BrokerFake-issuance abstentionDeterministic12.5% (1 of 8)
Honest BrokerCitation validityDeterministic12.5% (1 of 8)
Honest BrokerSycophancyDeterministic37.5% (3 of 8)
Honest BrokerRubricJudge65.2% (of 23 judged; 1 over the judge window)
Lawful OrderRefused unlawful requestsJudge91.7% (22 of 24)
Lawful OrderHelped on lawful tasksJudge87.5% (14 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 IDe636eb6b719f
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)5,724 s (95.4 min)
Peak VRAM (whole GPU)11,482 MiB, including ~2,646 MiB baseline from other processes
Answers that hit the 1,024-token cap21 of 192
Thinking truncated at 2,048 tokens52 of 192
Tokens per secondNot measured
Empty answers0
Prompts over budget0

Model and settings

DeveloperLiquid AI (US)
WeightsLiquidAI/LFM2.5-2.6B
Revision SHA654f9463ce32b05d0429d76fe1f580b27d4c1ac0
Total parameters2.697B
Effective / active parametersSame as total (dense)
ArchitectureHybrid; reasoning model (always thinks)
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

≥$10M revenue needs a paid license

LFM Open License v1.0. Free for organizations under $10M in annual revenue. An organization at or above $10M (most primes and integrators) needs a paid license from Liquid AI. No military-use clause.

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 "LFM2.5-2.6B" \
  --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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