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Owl Alpha

Owl Alpha is openrouter's model. On the un-leaked meo benchmark it ranks #17 of 82 with 73.7% accuracy and an Effective Value (𝕍) of 0.0158 β€” earning a correct answer for about $0.0000 in 53s each.

as of 2026-10-18

meo rank
#17
of 82
Accuracy
73.7%
overall
𝕍 index (top=100)
0.37
raw 𝕍 0.0158 Β· N=10
$ / correct
$0.0000
53s each

Per-domain accuracy

109 correct of 148 questions asked across 10 domains Β· answer rate 100% Β· macro accuracy 69.1%. Headline accuracy is micro-averaged per meo’s methodology; per-domain question counts (correct/asked) are shown so you can judge significance.

State tracking
100%1/1
Theory of mind
100%3/3
Base-rate bias
93%38/41
Tape machine
87%13/15
Regex automaton
75%12/16
Logic / math / CS
71%24/34
Framework bias
67%2/3
CSP (unsatisfiable)
56%9/16
Watson-Glaser
50%2/4
Long arithmetic
33%5/15

Efficiency

Cost per correct
$0.0000
Seconds per correct
53
Tokens per correct
1,982
Correct answers
109 / 148
Total benchmark cost
$0.00
Avg tokens / item
1,460

Model details

Released
2026-04-28
Weights
β€”
Context
1.0M
Max output
262K
Knowledge cutoff
β€”
Price in
$0.00/M
Price out
$0.00/M
Modality
text->text

Sources: openrouter Β· meo

FAQ

How accurate is Owl Alpha?

Owl Alpha scores 73.7% overall on the meo benchmark (rank #17 of 82), an un-leaked private holdout graded objective-first.

What is Owl Alpha's Effective Value?

Its Effective Value (𝕍) is 0.0158 at N=10, Ο‰=1, Ξ΄=1.5 β€” a fused metric of accuracy, speed, cost, and error-cascade. 𝕍 rankings shift with task depth.

How much does Owl Alpha cost per correct answer?

About $0.0000 per correct answer (1,982 tokens, 53 seconds each) at benchmark-time pricing via OpenRouter.

Is Owl Alpha open weights?

Open-weights status for Owl Alpha is not recorded.

Benchmark updates affecting this model

  • 2026-06-09majorlogic_math_cs ground truth was broken β€” culled and rebuilt with solver-verified items

    A 5-lab frontier jury revealed that 29 of 37 LLM-authored logic_math_cs items had broken ground truth: on most, five independent frontier models unanimously concluded the clue sets were contradictory while the stored answer claimed a number, and on eight, all five agreed on the SAME different number (author arithmetic errors). Models were being marked wrong for being right. The 29 were retired (full audit published in the repo) and the domain rebuilt with generator-as-oracle items: an embedded constraint solver PROVES each puzzle has exactly one solution (or provably none), verified by an independent exhaustive enumerator. A 3-round difficulty study found frontier models saturate consistent human-scale logic puzzles, so items use anonymous global constraints and carry a measured difficulty gradient.

  • 2026-06-08majorCorrected base_bias grading (LLM-equivalence judge fix)

    The LLM-equivalence grader uses a reasoning model whose verdict token budget was too small, so its YES/NO answer was emitted as empty and silently counted as a failure. This understated the base_bias domain to ~7% for every model when the true accuracy is ~85-90%. We raised the judge budget, excluded its reasoning tokens from the verdict, switched to verbosity-robust conclusion matching, and re-graded the whole board.

Full benchmark changelog β†’

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Related: all OpenRouter models Β· Multi-step state tracking domain Β· best Effective Value Β· biggest movers Β· methodology Β· full leaderboard.