long context benchmark
MRCR v2 is an enhanced synthetic long-context reasoning task that evaluates models across extended contexts.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score91.70% | Percentile100.00% | Participants3 | EvidenceC | Evaluated |
| Rank02 | ModelGO | Score32.00% | Percentile50.00% | Participants3 | EvidenceC | Evaluated |
| Rank03 | ModelGO | Score16.60% | Percentile0.00% | Participants3 | 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 v2 measures and how its scores work.
MRCR v2, or Multi-Round Coreference Resolution version 2, extends the original MRCR framework with improved evaluation criteria and additional complexity.
It measures models' ability to maintain attention and reasoning across extended contexts using the Score metric, reported as a 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 MRCR v2.
Qwen3.7-Plus is currently ranked first with 91.70%.
The current leaders are Qwen3.7-Plus (91.70%), DiffusionGemma 26B-A4B (32.00%), and Gemini 2.5 Flash-Lite (16.60%).
Gemini 2.5 Flash-Lite has the lowest matched official input price at $0.10 input / $0.40 output per 1M tokens.
The fastest matched records are Gemini 2.5 Flash-Lite (5.69 tok/s via Google).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures models' ability to maintain attention and reasoning across extended contexts using the Score metric, reported as a ratio.
Yes. Higher values rank better for this benchmark.
3 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis mrcr v2 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-Plus currently leads MRCR v2 with 91.70%. 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.