summarization benchmark
A large-scale summarization dataset containing over 9 million training instances extracted from Reddit, designed for extreme summarization (generating one-sentence summaries with high compression and abstraction). More than twice larger than previously proposed datasets.
Updated Aug 17, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelME | Score19.0% | Percentile100.0% | 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 tldr9+ (test) 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.
Selection summary
Llama 3.2 3B Instruct currently leads TLDR9+ (test) with 19.0%. 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.
What TLDR9+ (test) measures and how its scores work.
A large-scale summarization dataset containing over 9 million training instances extracted from Reddit, designed for extreme summarization (generating one-sentence summaries with high compression and abstraction). More than twice larger than previously proposed datasets.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about TLDR9+ (test).
Llama 3.2 3B Instruct is currently ranked first with 19.0%.
A large-scale summarization dataset containing over 9 million training instances extracted from Reddit, designed for extreme summarization (generating one-sentence summaries with high compression and abstraction). More than twice larger than previously proposed datasets.
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.