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reasoning benchmark

SWE-bench Verified (Agentless) Leaderboard

A human-validated subset of SWE-bench that evaluates language models' ability to resolve real-world GitHub issues using an agentless approach. The benchmark tests models on software engineering problems requiring understanding and coordinating changes across multiple functions, classes, and files simultaneously.

Updated Aug 17, 2026

Models2
Model coverage2
MetricScore
EvidenceB

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SWE-bench Verified (Agentless) Ranking

Higher score ranks better on this benchmark.

2 rows
Columns

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Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelMAKimi K2 InstructMoonshot AIScore51.8%Percentile100.0%Participants2EvidenceCEvaluatedAug 17, 2026
Rank02ModelXIMiMo-V2.5-ProXiaomiScore35.7%Percentile0.0%Participants2EvidenceCEvaluatedAug 17, 2026

SWE-bench Verified (Agentless) Highlights

The leading models and scores on this benchmark.

Rank #1Kimi K2 Instruct51.8%Rank #2MiMo-V2.5-Pro35.7%

SWE-bench Verified (Agentless) Score Distribution

A closer view of the leading scores on this benchmark.

SWE-bench Verified (Agentless)

The Top AI Models for SWE-bench Verified (Agentless)

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

Ranking basisThis swe-bench verified (agentless) 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
    MA
    Kimi K2 InstructMoonshot AI
    Score
    51.8%
    Price
    $0.60 input / $2.5 output per 1M tokens
    Speed
    Up to 45 tok/s via Novita

    Strengths

    • Ranks #1 of 2 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures SWE-bench Verified (Agentless), not total model capability
  2. 02
    XI
    MiMo-V2.5-ProXiaomi
    Score
    35.7%
    Price
    $0.43 input / $0.87 output per 1M tokens
    Speed
    Up to 69 tok/s via Novita

    Strengths

    • Ranks #2 of 2 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures SWE-bench Verified (Agentless), not total model capability

Selection summary

Best AI Models for SWE-bench Verified (Agentless)

Kimi K2 Instruct currently leads SWE-bench Verified (Agentless) with 51.8%. 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 #1Kimi K2 Instruct51.8% · $0.60 input / $2.5 output per 1M tokensBenchmark rank #2MiMo-V2.5-Pro35.7% · $0.43 input / $0.87 output per 1M tokens

What is SWE-bench Verified (Agentless)?

What SWE-bench Verified (Agentless) measures and how its scores work.

A human-validated subset of SWE-bench that evaluates language models' ability to resolve real-world GitHub issues using an agentless approach. The benchmark tests models on software engineering problems requiring understanding and coordinating changes across multiple functions, classes, and files simultaneously.

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

Family
SWE-bench Verified (Agentless)
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
No
Evaluation key
swe-bench-verified-(agentless)|llm-stats-current

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

FAQ

Common questions about SWE-bench Verified (Agentless).

Which model scores highest on SWE-bench Verified (Agentless)?

Kimi K2 Instruct is currently ranked first with 51.8%.

What does SWE-bench Verified (Agentless) measure?

A human-validated subset of SWE-bench that evaluates language models' ability to resolve real-world GitHub issues using an agentless approach. The benchmark tests models on software engineering problems requiring understanding and coordinating changes across multiple functions, classes, and files simultaneously.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

2 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.