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long context benchmark

Qasper Leaderboard

QASPER is a dataset of 5,049 information-seeking questions and answers anchored in 1,585 NLP research papers. Questions are written by NLP practitioners who read only titles and abstracts, while answers require understanding the full paper text and provide supporting evidence. The dataset challenges models with complex reasoning across document sections for academic document question answering. Each question seeks information present in the full text and is answered by a separate set of NLP practitioners who also provide supporting evidence to answers.

Updated Aug 17, 2026

Models2
Model coverage2
MetricScore
EvidenceB

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Qasper Ranking

Higher score ranks better on this benchmark.

2 rows
Columns

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Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelMIPhi-3.5-mini-instructMicrosoftScore41.9%Percentile100.0%Participants2EvidenceCEvaluatedAug 17, 2026
Rank02ModelMIPhi-3.5-MoE-instructMicrosoftScore40.0%Percentile0.0%Participants2EvidenceCEvaluatedAug 17, 2026

Qasper Highlights

The leading models and scores on this benchmark.

Rank #1Phi-3.5-mini-instruct41.9%Rank #2Phi-3.5-MoE-instruct40.0%

Qasper Score Distribution

A closer view of the leading scores on this benchmark.

Qasper

The Top AI Models for Qasper

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

Ranking basisThis qasper 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
    Phi-3.5-mini-instructMicrosoft
    Score
    41.9%
    Speed
    Up to 23 tok/s via Azure

    Strengths

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

    Considerations

    • This result measures Qasper, not total model capability
  2. 02
    MI
    Phi-3.5-MoE-instructMicrosoft
    Score
    40.0%

    Strengths

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

    Considerations

    • This result measures Qasper, not total model capability

Selection summary

Best AI Models for Qasper

Phi-3.5-mini-instruct currently leads Qasper with 41.9%. 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 #1Phi-3.5-mini-instruct41.9% · Up to 23 tok/s via AzureBenchmark rank #2Phi-3.5-MoE-instruct40.0%

What is Qasper?

What Qasper measures and how its scores work.

QASPER is a dataset of 5,049 information-seeking questions and answers anchored in 1,585 NLP research papers. Questions are written by NLP practitioners who read only titles and abstracts, while answers require understanding the full paper text and provide supporting evidence. The dataset challenges models with complex reasoning across document sections for academic document question answering. Each question seeks information present in the full text and is answered by a separate set of NLP practitioners who also provide supporting evidence to answers.

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

Family
Qasper
Modality
text
Primary category
long context
Score direction
higher
LLMBoard eligible
No
Evaluation key
qasper|llm-stats-current

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

FAQ

Common questions about Qasper.

Which model scores highest on Qasper?

Phi-3.5-mini-instruct is currently ranked first with 41.9%.

What does Qasper measure?

QASPER is a dataset of 5,049 information-seeking questions and answers anchored in 1,585 NLP research papers. Questions are written by NLP practitioners who read only titles and abstracts, while answers require understanding the full paper text and provide supporting evidence. The dataset challenges models with complex reasoning across document sections for academic document question answering. Each question seeks information present in the full text and is answered by a separate set of NLP practitioners who also provide supporting evidence to answers.

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.