long context benchmark
MRCR Leaderboard
MRCR (Multi-Round Coreference Resolution) is a synthetic long-context reasoning task where models must navigate long conversations to reproduce specific model outputs. It tests the ability to distinguish between similar requests and reason about ordering while maintaining attention across extended contexts.
Updated Aug 17, 2026
Models7
Model coverage7
MetricScore
EvidenceB
MRCR Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelGO | Score93.0% | Percentile100.0% | Participants7 | EvidenceC | Evaluated |
| Rank02 | ModelGO | Score82.6% | Percentile83.3% | Participants7 | EvidenceC | Evaluated |
| Rank03 | ModelGO | Score71.9% | Percentile66.7% | Participants7 | EvidenceC | Evaluated |
| Rank04 | ModelGO | Score69.2% | Percentile50.0% | Participants7 | EvidenceC | Evaluated |
| Rank05 | ModelGO | Score54.7% | Percentile33.3% | Participants7 | EvidenceC | Evaluated |
| Rank06 | ModelXI | Score45.7% | Percentile16.7% | Participants7 | EvidenceC | Evaluated |
| Rank07 | ModelGO | Score32.0% | Percentile0.0% | Participants7 | EvidenceC | Evaluated |
MRCR Highlights
The leading models and scores on this benchmark.
MRCR Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for MRCR
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis mrcr 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.
- 01GOGemini 2.5 ProGoogle
- Score
- 93.0%
- Price
- $1.3 input / $10 output per 1M tokens
- Speed
- Up to 86 tok/s via Google
Strengths
- Ranks #1 of 7 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures MRCR, not total model capability
- 02GOGemini 1.5 ProGoogle
- Score
- 82.6%
- Speed
- Up to 85 tok/s via Google
Strengths
- Ranks #2 of 7 compared models
- 83th percentile on this benchmark
- C evidence result
Considerations
- This result measures MRCR, not total model capability
- 03GOGemini 1.5 FlashGoogle
- Score
- 71.9%
- Speed
- Up to 150 tok/s via Google
Strengths
- Ranks #3 of 7 compared models
- 67th percentile on this benchmark
- C evidence result
Considerations
- This result measures MRCR, not total model capability
- 04GOGemini 2.0 FlashGoogle
- Score
- 69.2%
- Speed
- Up to 183 tok/s via Google
Strengths
- Ranks #4 of 7 compared models
- 50th percentile on this benchmark
- C evidence result
Considerations
- This result measures MRCR, not total model capability
- 05GOGemini 1.5 Flash 8BGoogle
- Score
- 54.7%
- Speed
- Up to 150 tok/s via Google
Strengths
- Ranks #5 of 7 compared models
- 33th percentile on this benchmark
- C evidence result
Considerations
- This result measures MRCR, not total model capability
Selection summary
Best AI Models for MRCR
Gemini 2.5 Pro currently leads MRCR with 93.0%. 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 is MRCR?
What MRCR measures and how its scores work.
MRCR (Multi-Round Coreference Resolution) is a synthetic long-context reasoning task where models must navigate long conversations to reproduce specific model outputs. It tests the ability to distinguish between similar requests and reason about ordering while maintaining attention across extended contexts.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- MRCR
- Modality
- text
- Primary category
- long context
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- mrcr|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about MRCR.
Which model scores highest on MRCR?
Gemini 2.5 Pro is currently ranked first with 93.0%.
What does MRCR measure?
MRCR (Multi-Round Coreference Resolution) is a synthetic long-context reasoning task where models must navigate long conversations to reproduce specific model outputs. It tests the ability to distinguish between similar requests and reason about ordering while maintaining attention across extended contexts.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
7 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.