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code benchmark

Codegolf v2.2 Leaderboard

Codegolf v2.2 benchmark

Updated Aug 17, 2026

Models4
Model coverage4
MetricScore
EvidenceB

On this page

  • Ranking
  • Highlights
  • Distribution
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  • About
  • FAQ

Codegolf v2.2 Ranking

Higher score ranks better on this benchmark.

4 rows
Columns

Show columns

Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelGOGemma 3n E4B InstructedGoogleScore16.8%Percentile100.0%Participants4EvidenceCEvaluatedAug 17, 2026
Rank02ModelGOGemma 3n E4B Instructed LiteRT PreviewGoogleScore16.8%Percentile66.7%Participants4EvidenceCEvaluatedAug 17, 2026
Rank03ModelGOGemma 3n E2B InstructedGoogleScore11.0%Percentile33.3%Participants4EvidenceCEvaluatedAug 17, 2026
Rank04ModelGOGemma 3n E2B Instructed LiteRT (Preview)GoogleScore11.0%Percentile0.0%Participants4EvidenceCEvaluatedAug 17, 2026

Codegolf v2.2 Highlights

The leading models and scores on this benchmark.

Rank #1Gemma 3n E4B Instructed16.8%Rank #2Gemma 3n E4B Instructed LiteRT Preview16.8%Rank #3Gemma 3n E2B Instructed11.0%Rank #4Gemma 3n E2B Instructed LiteRT (Preview)11.0%

Codegolf v2.2 Score Distribution

A closer view of the leading scores on this benchmark.

Codegolf v2.2

The Top AI Models for Codegolf v2.2

The first five results on this benchmark, with official price and output speed added where the model identity can be matched.

Ranking basisThis codegolf v2.2 AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    GO
    Gemma 3n E4B InstructedGoogle
    Score
    16.8%
    Speed
    Up to 42 tok/s via Together

    Strengths

    • Ranks #1 of 4 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Codegolf v2.2, not total model capability
  2. 02
    GO
    Gemma 3n E4B Instructed LiteRT PreviewGoogle
    Score
    16.8%

    Strengths

    • Ranks #2 of 4 compared models
    • 67th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Codegolf v2.2, not total model capability
  3. 03
    GO
    Gemma 3n E2B InstructedGoogle
    Score
    11.0%

    Strengths

    • Ranks #3 of 4 compared models
    • 33th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Codegolf v2.2, not total model capability
  4. 04
    GO
    Gemma 3n E2B Instructed LiteRT (Preview)Google
    Score
    11.0%

    Strengths

    • Ranks #4 of 4 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Codegolf v2.2, not total model capability

Selection summary

Best AI Models for Codegolf v2.2

Gemma 3n E4B Instructed currently leads Codegolf v2.2 with 16.8%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.

Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.

Benchmark rank #1Gemma 3n E4B Instructed16.8% · Up to 42 tok/s via TogetherBenchmark rank #2Gemma 3n E4B Instructed LiteRT Preview16.8%Benchmark rank #3Gemma 3n E2B Instructed11.0%

What is Codegolf v2.2?

What Codegolf v2.2 measures and how its scores work.

Codegolf v2.2 benchmark

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
Codegolf v2.2
Modality
text
Primary category
code
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
codegolf-v2.2|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about Codegolf v2.2.

Which model scores highest on Codegolf v2.2?

Gemma 3n E4B Instructed is currently ranked first with 16.8%.

What does Codegolf v2.2 measure?

Codegolf v2.2 benchmark

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

4 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.