long context benchmark
NoLiMa 128K evaluates latent associative reasoning in long contexts at a 131072-token context length with minimal lexical overlap between questions and needles.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelOP | Score23.86% | 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 nolima 128k 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
MiniCPM-SALA currently leads NoLiMa 128K with 23.86%. 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 NoLiMa 128K measures and how its scores work.
NoLiMa 128K is a long-context benchmark evaluated at a 131072-token context length.
It measures latent associative reasoning in long contexts with minimal lexical overlap between questions and needles, reported as a Score ratio.
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 NoLiMa 128K.
MiniCPM-SALA is currently ranked first with 23.86%.
The current leaders are MiniCPM-SALA (23.86%).
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 latent associative reasoning in long contexts with minimal lexical overlap between questions and needles, reported as a Score ratio.
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.