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reasoning benchmark

MRCR 128K (8-needle)

MRCR (Multi-Round Coreference Resolution) at 128K context length with 8 needles. Models must navigate long conversations to reproduce specific model outputs, testing attention and reasoning across 128K-token contexts with 8 items to retrieve.

Updated Aug 7, 2026

Published models2
Registry coverage2
MetricScore
EvidenceB

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MRCR 128K (8-needle) leaderboard

Sorted by the source-provided rank. Higher score is better according to the registry.

2 rows
Columns

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1ACQwen3.7 MaxAlibaba Cloud / Qwen Team90.4%100.0%2CAug 7, 2026
2OPMiniCPM-SALAOpenBMB10.1%0.0%2CAug 7, 2026

Score distribution

Top published rows on the benchmark's original scale.

MRCR 128K (8-needle)

MRCR 128K (8-needle) highlights

The top published results on this benchmark's own scale.

Rank #1Qwen3.7 Max90.4%Rank #2MiniCPM-SALA10.1%

What is MRCR 128K (8-needle)?

Definition and scoring fields from the benchmark registry.

MRCR (Multi-Round Coreference Resolution) at 128K context length with 8 needles. Models must navigate long conversations to reproduce specific model outputs, testing attention and reasoning across 128K-token contexts with 8 items to retrieve.

Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.

Family
MRCR 128K (8-needle)
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
No
Evaluation key
mrcr-128k-(8-needle)|llm-stats-current

Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.

FAQ

Common questions about MRCR 128K (8-needle).

Which model scores highest on MRCR 128K (8-needle)?

Qwen3.7 Max is currently ranked first with 90.4%.

What does MRCR 128K (8-needle) measure?

MRCR (Multi-Round Coreference Resolution) at 128K context length with 8 needles. Models must navigate long conversations to reproduce specific model outputs, testing attention and reasoning across 128K-token contexts with 8 items to retrieve.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

2 unique published model results are currently shown.

Does this benchmark affect the overall score?

This benchmark is preserved as source-native evidence but is not eligible for the current overall score.

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