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long context benchmark

GraphWalks Leaderboard

GraphWalks is a synthetic multi-hop long-context reasoning benchmark in which a model is given an edge-list representation of a graph and must traverse it to find neighboring nodes (via breadth-first search) or parent nodes for a given start node. Performance is reported as F1 of the model-predicted answer set versus the ground truth.

Updated Aug 17, 2026

Models3
Model coverage3
MetricScore
EvidenceB

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GraphWalks Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

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Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelMIMAI-Thinking-1MicrosoftScore90.0%Percentile100.0%Participants3EvidenceCEvaluatedAug 17, 2026
Rank02ModelXIMiMo-V2.5XiaomiScore87.0%Percentile50.0%Participants3EvidenceCEvaluatedAug 17, 2026
Rank03ModelXIMiMo-V2.5-ProXiaomiScore62.0%Percentile0.0%Participants3EvidenceCEvaluatedAug 17, 2026

GraphWalks Highlights

The leading models and scores on this benchmark.

Rank #1MAI-Thinking-190.0%Rank #2MiMo-V2.587.0%Rank #3MiMo-V2.5-Pro62.0%

GraphWalks Score Distribution

A closer view of the leading scores on this benchmark.

GraphWalks

The Top AI Models for GraphWalks

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

Ranking basisThis graphwalks 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
    MI
    MAI-Thinking-1Microsoft
    Score
    90.0%

    Strengths

    • Ranks #1 of 3 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures GraphWalks, not total model capability
  2. 02
    XI
    MiMo-V2.5Xiaomi
    Score
    87.0%
    Price
    $0.14 input / $0.28 output per 1M tokens
    Speed
    Up to 87 tok/s via Novita

    Strengths

    • Ranks #2 of 3 compared models
    • 50th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures GraphWalks, not total model capability
  3. 03
    XI
    MiMo-V2.5-ProXiaomi
    Score
    62.0%
    Price
    $0.43 input / $0.87 output per 1M tokens
    Speed
    Up to 69 tok/s via Novita

    Strengths

    • Ranks #3 of 3 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures GraphWalks, not total model capability

Selection summary

Best AI Models for GraphWalks

MAI-Thinking-1 currently leads GraphWalks with 90.0%. 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 #1MAI-Thinking-190.0%Benchmark rank #2MiMo-V2.587.0% · $0.14 input / $0.28 output per 1M tokensBenchmark rank #3MiMo-V2.5-Pro62.0% · $0.43 input / $0.87 output per 1M tokens

What is GraphWalks?

What GraphWalks measures and how its scores work.

GraphWalks is a synthetic multi-hop long-context reasoning benchmark in which a model is given an edge-list representation of a graph and must traverse it to find neighboring nodes (via breadth-first search) or parent nodes for a given start node. Performance is reported as F1 of the model-predicted answer set versus the ground truth.

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

Family
GraphWalks
Modality
text
Primary category
long context
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
graphwalks|llm-stats-current

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

FAQ

Common questions about GraphWalks.

Which model scores highest on GraphWalks?

MAI-Thinking-1 is currently ranked first with 90.0%.

What does GraphWalks measure?

GraphWalks is a synthetic multi-hop long-context reasoning benchmark in which a model is given an edge-list representation of a graph and must traverse it to find neighboring nodes (via breadth-first search) or parent nodes for a given start node. Performance is reported as F1 of the model-predicted answer set versus the ground truth.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

3 model results are currently shown.

Does this benchmark affect the overall score?

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