llmboard.aiAI model intelligence
Home

Model Rankings

OverallOpen ModelsAgentCodingReasoningMathKnowledgeInstruction FollowingTextVision
Image GenerationImage Editing
Video GenerationImage to VideoVideo Editing
Text to SpeechSpeech to Text
Embeddings

Efficiency

Chat Token PricingImage PricingVideo PricingAudio Pricing
Chat Speed & LatencyProvider Reliability

Benchmarks

GPQAMMLU-ProAIME 2025SWE-Bench VerifiedMMLUHumanity's Last ExamLiveCodeBenchMATHHumanEvalMMMU-Pro
All Benchmarks

Tools

Model Directory

Scoring & Data

Scoring & Data
1367 models808 benchmarks

Leaderboard Center

Overall RankingCodingCore BenchmarksPrice & ValueRuntime Performance

Modalities

All ModelsImage GenerationImage EditingVideo GenerationImage-to-VideoVideo EditingText-to-SpeechSpeech-to-TextEmbeddings

Data & Methods

Scoring MethodAll Benchmarks
llmboard.aiCopyright 2026 llmboard.ai

agents benchmark

SWE-Marathon Leaderboard

SWE-Marathon is an ultra-long-horizon software engineering benchmark covering tasks such as building compilers, optimizing kernels, and developing production-grade services.

Updated Oct 23, 2026

Models10
Model coverage10
MetricScore
EvidenceB

On this page

  • Ranking
  • Highlights
  • Distribution
  • Top models
  • About
  • FAQ

SWE-Marathon Ranking

Higher score ranks better on this benchmark.

10 rows
Columns

Show columns

Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelMEMuse Wonder FullMetaScore57.50%Percentile100.00%Participants10EvidenceCEvaluatedOct 22, 2026
Rank02ModelMEMuse Wonder FullMetaScore57.50%Percentile88.89%Participants10EvidenceCEvaluatedOct 22, 2026
Rank03ModelMEMuse WonderMetaScore53.10%Percentile77.78%Participants10EvidenceCEvaluatedOct 22, 2026
Rank04ModelMEMuse WonderMetaScore53.10%Percentile66.67%Participants10EvidenceCEvaluatedOct 22, 2026
Rank05ModelXAGrok 4.7xAIScore46.00%Percentile55.56%Participants10EvidenceCEvaluatedOct 22, 2026
Rank06ModelZAGLM-5.3Zhipu AIScore42.50%Percentile44.44%Participants10EvidenceCEvaluatedOct 22, 2026
Rank07ModelMAKimi K3Moonshot AIScore42.00%Percentile33.33%Participants10EvidenceCEvaluatedOct 22, 2026
Rank08ModelTEHy4 previewTencentScore31.90%Percentile22.22%Participants10EvidenceCEvaluatedOct 22, 2026
Rank09ModelXAGrok 4.5xAIScore29.00%Percentile11.11%Participants10EvidenceCEvaluatedOct 22, 2026
Rank10ModelZAGLM-5.2Zhipu AIScore13.00%Percentile0.00%Participants10EvidenceCEvaluatedOct 22, 2026

SWE-Marathon Highlights

The leading models and scores on this benchmark.

SWE-Marathon Score Distribution

A closer view of the leading scores on this benchmark.

SWE-Marathon

The Top AI Models for SWE-Marathon

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

What is SWE-Marathon?

What SWE-Marathon measures and how its scores work.

SWE-Marathon is a software engineering benchmark for evaluating agents on tasks such as building compilers, optimizing kernels, and developing production-grade services.

It measures whether agents can sustain quality across extremely long engineering trajectories, using the Score metric with a ratio unit.

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

Family
SWE-Marathon
Modality
text
Primary category
agents
Score direction
higher
LLMBoard eligible
No
Evaluation key
overall

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

FAQ

Common questions about SWE-Marathon.

Which model scores highest on SWE-Marathon?

Muse Wonder Full is currently ranked first with 57.50%.

What are the top three models on SWE-Marathon?

The current leaders are Muse Wonder Full (57.50%), Muse Wonder Full (57.50%), and Muse Wonder (53.10%).

Which SWE-Marathon model has the lowest official input price?

GLM-5.3 has the lowest matched official input price at $1.2 input / $4.2 output per 1M tokens.

Which models are fastest among SWE-Marathon results?

The fastest matched records are Grok 4.5 (80.00 tok/s via xAI).

Does the highest score result prove overall model quality?

No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.

What does SWE-Marathon measure?

It measures whether agents can sustain quality across extremely long engineering trajectories, using the Score metric with a ratio unit.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

10 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.

Rank #1Muse Wonder Full57.50%
Rank #2Muse Wonder Full57.50%
Rank #3Muse Wonder53.10%
Rank #4Muse Wonder53.10%

Ranking basisThis swe-marathon AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    ME
    Muse Wonder FullMeta
    Score
    57.50%

    Strengths

    • Ranks #1 of 10 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures SWE-Marathon, not total model capability
  2. 02
    ME
    Muse Wonder FullMeta
    Score
    57.50%

    Strengths

    • Ranks #2 of 10 compared models
    • 89th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures SWE-Marathon, not total model capability
  3. 03
    ME
    Meta
    Score
    53.10%
    Price
    $4.0 input / $20 output per 1M tokens

    Strengths

    • Ranks #3 of 10 compared models
    • 78th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures SWE-Marathon, not total model capability
  4. 04
    ME
    Meta
    Score
    53.10%
    Price
    $4.0 input / $20 output per 1M tokens

    Strengths

    • Ranks #4 of 10 compared models
    • 67th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures SWE-Marathon, not total model capability
  5. 05
    XA
    xAI
    Score
    46.00%
    Price
    $2.0 input / $6.0 output per 1M tokens

    Strengths

    • Ranks #5 of 10 compared models
    • 56th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures SWE-Marathon, not total model capability

Selection summary

Best AI Models for SWE-Marathon

Muse Wonder Full currently leads SWE-Marathon with 57.50%. 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.

Muse Wonder
Muse Wonder
Grok 4.7
Benchmark rank #1Muse Wonder Full57.50%
Benchmark rank #2Muse Wonder Full57.50%
Benchmark rank #3Muse Wonder53.10% · $4.0 input / $20 output per 1M tokens