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long context benchmark

MRCR 1M

MRCR 1M is a variant of the Multi-Round Coreference Resolution benchmark designed for testing extremely long context capabilities with approximately 1 million tokens. It evaluates models' ability to maintain reasoning and attention across ultra-long conversations.

Updated Aug 11, 2026

Models3
Model coverage3
MetricScore
EvidenceB

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  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

MRCR 1M Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

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1DEDeepSeek-V4-Pro-MaxDeepSeek83.5%100.0%3CAug 11, 2026
2DEDeepSeek-V4-Flash-MaxDeepSeek78.7%50.0%3CAug 11, 2026
3GOGemini 2.0 Flash-LiteGoogle58.0%0.0%3CAug 11, 2026

MRCR 1M Score Distribution

A closer view of the leading scores on this benchmark.

MRCR 1M

MRCR 1M Highlights

The leading models and scores on this benchmark.

Rank #1DeepSeek-V4-Pro-Max83.5%Rank #2DeepSeek-V4-Flash-Max78.7%Rank #3Gemini 2.0 Flash-Lite58.0%

What is MRCR 1M?

What MRCR 1M measures and how its scores work.

MRCR 1M is a variant of the Multi-Round Coreference Resolution benchmark designed for testing extremely long context capabilities with approximately 1 million tokens. It evaluates models' ability to maintain reasoning and attention across ultra-long conversations.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
MRCR 1M
Modality
text
Primary category
long context
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
mrcr-1m|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about MRCR 1M.

Which model scores highest on MRCR 1M?

DeepSeek-V4-Pro-Max is currently ranked first with 83.5%.

What does MRCR 1M measure?

MRCR 1M is a variant of the Multi-Round Coreference Resolution benchmark designed for testing extremely long context capabilities with approximately 1 million tokens. It evaluates models' ability to maintain reasoning and attention across ultra-long conversations.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

3 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.

Rankings

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Modalities

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