long context benchmark
MRCR (Multi-Round Coreference Resolution) at 128K context length with 8 needles requires models to reproduce specific model outputs from long conversations.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score90.40% | Percentile100.00% | Participants2 | EvidenceC | Evaluated |
| Rank02 | ModelOP | Score10.12% | Percentile0.00% | Participants2 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading 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.
What MRCR 128K (8-needle) measures and how its scores work.
A long-context benchmark using 128K-token contexts and 8 items to retrieve.
It measures Score as a ratio, evaluating attention and reasoning across long conversations.
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 128K (8-needle).
Qwen3.7 Max is currently ranked first with 90.40%.
The current leaders are Qwen3.7 Max (90.40%) and MiniCPM-SALA (10.12%).
Qwen3.7 Max has the lowest matched official input price at $2.5 input / $7.5 output per 1M tokens.
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 Score as a ratio, evaluating attention and reasoning across long conversations.
Yes. Higher values rank better for this benchmark.
2 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis mrcr 128k (8-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
Qwen3.7 Max currently leads MRCR 128K (8-needle) with 90.40%. 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.