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GPT-6-Luna

OpenAICodexReasoning: high

GPT-6-Luna gets about 2 vocabulary questions in 10 right. It does best in Kabyle. Its weakest spot is writing Tarifit words in Tifinagh. It swaps Tifinagh and Latin letters well (88.3 out of 100).

Rank 4 of 9

Compare with others

TifinaghBench score

18.2out of 100

95% interval 17.3–19.0

17.7 with strict spelling

0–100, with 95% interval

Script score

88.3out of 100

95% interval 87.2–89.4

70.8 with strict spelling

Transliteration score, 0–100

Scores by dialect and task

Share of the score earned in each cell, 0–100. The transliteration column measures the script, not the vocabulary.

Standard Moroccan Amazigh

Tifinagh → English
16.1118 correct, 85 partial, 797 wrong of 1000
English → Tifinagh
3.535 correct, 0 partial, 965 wrong of 1000
Tifinagh ↔ Latin
94.9949 correct, 0 partial, 51 wrong of 1000

Tarifit

Tifinagh → English
19.1142 correct, 96 partial, 755 wrong of 993
English → Tifinagh
2.727 correct, 0 partial, 966 wrong of 993
Tifinagh ↔ Latin
75.6756 correct, 0 partial, 244 wrong of 1000

Kabyle

Tifinagh → English
49.5428 correct, 131 partial, 438 wrong of 997
English → Tifinagh
18.2181 correct, 0 partial, 816 wrong of 997
Tifinagh ↔ Latin
94.4944 correct, 0 partial, 56 wrong of 1000

By dialect

Meaning tasks only

  • Standard Moroccan Amazigh9.8
  • Tarifit10.9
  • Kabyle33.8

By task

 

  • Tifinagh → English28.2
  • English → Tifinagh8.1
  • Tifinagh ↔ Latin88.3

Tifinagh → English grades

Free-text meanings, graded by the judge or matched exactly.

  • Correct
  • Partial
  • Wrong
  • Failed
  • Standard Moroccan Amazigh118 correct, 85 partial, 797 wrong of 1000

  • Tarifit142 correct, 96 partial, 755 wrong of 993

  • Kabyle428 correct, 131 partial, 438 wrong of 997

Character error rate

For exact-match answers: the smallest edit distance to an accepted answer, divided by its length. Lower is better; it shows near misses that a 0 score hides.

English → TifinaghTifinagh ↔ Latin
Standard Moroccan Amazigh86.6%1.1%
Tarifit94.4%24.2%
Kabyle64.8%1.4%

The run

Setup
Official setup
Harness
Codex 0.163.0@openai/codex-sdk 0.163.0
Judge
Claude Haiku 5.5 (Claude Code)
Sample
1,000 words per dialect, seed 1
Batch size
100
Sessions
93 answer, 30 judge
Tokens
1,003,026 in · 282,988 out · 126,226 reasoning
Model cost
Not reported by the harness
Judge cost
$0.20
Dates
19 Oct 2026, 20:44 UTC → 19 Oct 2026, 21:04 UTC
CLI version
0.1.0
Dataset checksums

SHA-256 of each dictionary file, so the run can be checked once the datasets are released.

  • Standard Moroccan Amazigh633505d9ccd2244f9a53b64f56bd153c428cc3dbd7229066845404bf3d7c5060
  • Tarifitd144a5619aa31cc06ffcc4eb26c361c65ca2c71451dfdfbea7fa134206481c4f
  • Kabyleed3a9882ac0f4ef10630917a302fd7d9ff6ac706654bb317b94c715f6b4cf9ea

Run folder on GitHub