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

FrontierSWE Leaderboard

FrontierSWE evaluates whether an agent can complete open-ended technical projects spanning systems optimization, large-scale code construction, and applied ML research over hours to tens of hours.

Updated Oct 23, 2026

Models17
Model coverage17
MetricScore
EvidenceB

On this page

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

FrontierSWE Ranking

Higher score ranks better on this benchmark.

17 rows
Columns

Show columns

Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelANClaude Fable 5AnthropicScore90.00%Percentile100.00%Participants17EvidenceBEvaluatedOct 22, 2026
Rank02ModelMAKimi K3Moonshot AIScore81.20%Percentile93.75%Participants17EvidenceCEvaluatedOct 22, 2026
Rank03ModelZAGLM-5.3Zhipu AIScore78.10%Percentile87.50%Participants17EvidenceCEvaluatedOct 22, 2026
Rank04ModelINLing 3.1 FlashInclusionaiScore75.16%Percentile81.25%Participants17EvidenceCEvaluatedOct 12, 2026
Rank05ModelANClaude Opus 4.8AnthropicScore75.00%Percentile75.00%Participants17EvidenceBEvaluatedOct 22, 2026
Rank06ModelZAGLM-5.2Zhipu AIScore74.00%Percentile68.75%Participants17EvidenceBEvaluatedOct 22, 2026
Rank07ModelACQwen3.8 MaxAlibaba Cloud / Qwen TeamScore73.50%Percentile62.50%Participants17EvidenceCEvaluatedOct 22, 2026
Rank08ModelOPGPT-5.5OpenAIScore73.00%Percentile56.25%Participants17EvidenceBEvaluatedOct 22, 2026
Rank09ModelANClaude Opus 4.7AnthropicScore63.00%Percentile50.00%Participants17EvidenceBEvaluatedOct 22, 2026
Rank10ModelANClaude Opus 4.6AnthropicScore56.00%Percentile43.75%Participants17EvidenceBEvaluatedOct 22, 2026
Rank11ModelOPGPT-5.4OpenAIScore54.00%Percentile37.50%Participants17EvidenceBEvaluatedOct 22, 2026
Rank12ModelGOGemini 3.1 ProGoogleScore40.00%Percentile31.25%Participants17EvidenceBEvaluatedOct 22, 2026
Rank13ModelZAGLM-5.1Zhipu AIScore31.00%Percentile25.00%Participants17EvidenceBEvaluatedOct 22, 2026
Rank14ModelDEDeepSeek-V4-Pro-MaxDeepSeekScore29.00%Percentile18.75%Participants17EvidenceBEvaluatedOct 22, 2026
Rank15ModelMAKimi K2.6Moonshot AIScore27.00%Percentile12.50%Participants17EvidenceBEvaluatedOct 22, 2026
Rank16ModelMAKimi K2.5Moonshot AIScore26.00%Percentile6.25%Participants17EvidenceBEvaluatedOct 22, 2026
Rank17ModelACQwen3.6 PlusAlibaba Cloud / Qwen TeamScore22.00%Percentile0.00%Participants17EvidenceBEvaluatedOct 22, 2026

FrontierSWE Highlights

The leading models and scores on this benchmark.

FrontierSWE Score Distribution

A closer view of the leading scores on this benchmark.

FrontierSWE

The Top AI Models for FrontierSWE

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

What is FrontierSWE?

What FrontierSWE measures and how its scores work.

FrontierSWE is an agents benchmark for open-ended technical projects at the scale of hours to tens of hours.

It measures agent performance on systems optimization, large-scale code construction, and applied ML research, reported as a dominance score where higher is better.

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

Family
FrontierSWE
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 FrontierSWE.

Which model scores highest on FrontierSWE?

Claude Fable 5 is currently ranked first with 90.00%.

What are the top three models on FrontierSWE?

The current leaders are Claude Fable 5 (90.00%), Kimi K3 (81.20%), and GLM-5.3 (78.10%).

Which FrontierSWE model has the lowest official input price?

Qwen3.6 Plus has the lowest matched official input price at $0.50 input / $3.0 output per 1M tokens.

Which models are fastest among FrontierSWE results?

The fastest matched records are Gemini 3.1 Pro (90.00 tok/s via Google), GPT-5.4 (50.00 tok/s via OpenAI), and Claude Opus 4.8 (42.00 tok/s via Vertex AI).

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 FrontierSWE measure?

It measures agent performance on systems optimization, large-scale code construction, and applied ML research, reported as a dominance score where higher is better.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

17 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 #1Claude Fable 590.00%
Rank #2Kimi K381.20%
Rank #3GLM-5.378.10%
Rank #4Ling 3.1 Flash75.16%

Ranking basisThis frontierswe 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
    AN
    Claude Fable 5Anthropic
    Score
    90.00%
    Price
    $10 input / $50 output per 1M tokens

    Strengths

    • Ranks #1 of 17 compared models
    • 100th percentile on this benchmark
    • B evidence result

    Considerations

    • This result measures FrontierSWE, not total model capability
  2. 02
    MA
    Kimi K3Moonshot AI
    Score
    81.20%
    Price
    $3.0 input / $15 output per 1M tokens

    Strengths

    • Ranks #2 of 17 compared models
    • 94th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures FrontierSWE, not total model capability
  3. 03
    ZA
    Zhipu AI
    Score
    78.10%
    Price
    $1.2 input / $4.2 output per 1M tokens

    Strengths

    • Ranks #3 of 17 compared models
    • 88th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures FrontierSWE, not total model capability
  4. 04
    IN
    Inclusionai
    Score
    75.16%

    Strengths

    • Ranks #4 of 17 compared models
    • 81th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures FrontierSWE, not total model capability
  5. 05
    AN
    Anthropic
    Score
    75.00%
    Price
    $5.0 input / $25 output per 1M tokens
    Speed
    Up to 42.00 tok/s via Vertex AI

    Strengths

    • Ranks #5 of 17 compared models
    • 75th percentile on this benchmark
    • B evidence result

    Considerations

    • This result measures FrontierSWE, not total model capability

Selection summary

Best AI Models for FrontierSWE

Claude Fable 5 currently leads FrontierSWE with 90.00%. 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.

GLM-5.3
Ling 3.1 Flash
Claude Opus 4.8
Benchmark rank #1Claude Fable 590.00% · $10 input / $50 output per 1M tokens
Benchmark rank #2Kimi K381.20% · $3.0 input / $15 output per 1M tokens
Benchmark rank #3GLM-5.378.10% · $1.2 input / $4.2 output per 1M tokens