long context benchmark
MRCR (Multi-Round Coreference Resolution) at 64K context length with 4 needles requires models to navigate long conversations and reproduce specific model outputs.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelOP | Score20.57% | 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 mrcr 64k (4-needle) 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 MRCR 64K (4-needle) with 20.57%. 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 MRCR 64K (4-needle) measures and how its scores work.
MRCR (Multi-Round Coreference Resolution) is a long-context benchmark using 64K-token conversations with 4 items to retrieve.
It measures attention and reasoning across 64K-token contexts by evaluating whether models can navigate long conversations and reproduce specific model outputs.
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 MRCR 64K (4-needle).
MiniCPM-SALA is currently ranked first with 20.57%.
The current leaders are MiniCPM-SALA (20.57%).
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 attention and reasoning across 64K-token contexts by evaluating whether models can navigate long conversations and reproduce specific model outputs.
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.