long context benchmark
CorpusQA is a multi-document, free-form long-context question answering benchmark requiring models to retrieve and reason over information distributed across a large corpus to produce open-ended answers scored by an LLM judge.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMI | Score82.00% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis corpusqa AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
MAI-Thinking-1 currently leads CorpusQA with 82.00%. 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.
What CorpusQA measures and how its scores work.
CorpusQA is a multi-document, free-form long-context question answering benchmark.
It measures performance on retrieving and reasoning over information distributed across a large corpus to produce open-ended answers, reported as a Score ratio and scored by an LLM judge.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about CorpusQA.
MAI-Thinking-1 is currently ranked first with 82.00%.
The current leaders are MAI-Thinking-1 (82.00%).
No matched official input price is currently available.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures performance on retrieving and reasoning over information distributed across a large corpus to produce open-ended answers, reported as a Score ratio and scored by an LLM judge.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.