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agents benchmark

Meta Internal Coding Bench Leaderboard

Meta's internal evaluation of coding-agent performance.

Updated Aug 17, 2026

Models1
Model coverage1
MetricScore
EvidenceB

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Meta Internal Coding Bench Ranking

Higher score ranks better on this benchmark.

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Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelMEMuse Spark 1.2MetaScore70.6%Percentile100.0%Participants1EvidenceCEvaluatedAug 17, 2026

Meta Internal Coding Bench Highlights

The leading models and scores on this benchmark.

Rank #1Muse Spark 1.270.6%

The Top AI Models for Meta Internal Coding Bench

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

Ranking basisThis meta internal coding bench 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
    ME
    Muse Spark 1.2Meta
    Score
    70.6%
    Price
    $1.3 input / $4.3 output per 1M tokens
    Speed
    Up to 23 tok/s via Meta Model API

    Strengths

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

    Considerations

    • This result measures Meta Internal Coding Bench, not total model capability

Selection summary

Best AI Models for Meta Internal Coding Bench

Muse Spark 1.2 currently leads Meta Internal Coding Bench with 70.6%. 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 #1Muse Spark 1.270.6% · $1.3 input / $4.3 output per 1M tokens

What is Meta Internal Coding Bench?

What Meta Internal Coding Bench measures and how its scores work.

Meta's internal evaluation of coding-agent performance.

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

Family
Meta Internal Coding Bench
Modality
text
Primary category
agents
Score direction
higher
LLMBoard eligible
No
Evaluation key
meta-internal-coding-bench|llm-stats-current

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

FAQ

Common questions about Meta Internal Coding Bench.

Which model scores highest on Meta Internal Coding Bench?

Muse Spark 1.2 is currently ranked first with 70.6%.

What does Meta Internal Coding Bench measure?

Meta's internal evaluation of coding-agent performance.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

1 model results are currently shown.

Does this benchmark affect the overall score?

No. This benchmark is shown for reference but does not contribute to the overall score.