long context benchmark
QMSum Leaderboard
QMSum is a benchmark for query-based multi-domain meeting summarization consisting of 1,808 query-summary pairs over 232 meetings across academic, product, and committee domains. The dataset enables models to select and summarize relevant spans of meetings in response to specific queries. Published at NAACL 2021, QMSum presents significant challenges in long meeting summarization where models must identify and summarize relevant content based on user queries.
Updated Aug 17, 2026
Models2
Model coverage2
MetricScore
EvidenceB
QMSum Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMI | Score21.3% | Percentile100.0% | Participants2 | EvidenceC | Evaluated |
| Rank02 | ModelMI | Score19.9% | Percentile0.0% | Participants2 | EvidenceC | Evaluated |
QMSum Highlights
The leading models and scores on this benchmark.
QMSum Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for QMSum
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis qmsum 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.
- 01MIPhi-3.5-mini-instructMicrosoft
- Score
- 21.3%
- 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 QMSum, not total model capability
- 02MIPhi-3.5-MoE-instructMicrosoft
- Score
- 19.9%
Strengths
- Ranks #2 of 2 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures QMSum, not total model capability
Selection summary
Best AI Models for QMSum
Phi-3.5-mini-instruct currently leads QMSum with 21.3%. 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 is QMSum?
What QMSum measures and how its scores work.
QMSum is a benchmark for query-based multi-domain meeting summarization consisting of 1,808 query-summary pairs over 232 meetings across academic, product, and committee domains. The dataset enables models to select and summarize relevant spans of meetings in response to specific queries. Published at NAACL 2021, QMSum presents significant challenges in long meeting summarization where models must identify and summarize relevant content based on user queries.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- QMSum
- Modality
- text
- Primary category
- long context
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- qmsum|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about QMSum.
Which model scores highest on QMSum?
Phi-3.5-mini-instruct is currently ranked first with 21.3%.
What does QMSum measure?
QMSum is a benchmark for query-based multi-domain meeting summarization consisting of 1,808 query-summary pairs over 232 meetings across academic, product, and committee domains. The dataset enables models to select and summarize relevant spans of meetings in response to specific queries. Published at NAACL 2021, QMSum presents significant challenges in long meeting summarization where models must identify and summarize relevant content based on user queries.
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.