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.
DeepSeek-V4-Pro-Max from DeepSeek currently leads the MRCR 1M leaderboard with a score of 0.835 across 3 evaluated AI models.
DeepSeek-V4-Pro-Max leads with 83.5%, followed by DeepSeek-V4-Flash-Max at 78.7% and 
Gemini 2.0 Flash-Lite at 58.0%.
Progress Over Time
Interactive timeline showing model performance evolution on MRCR 1M
MRCR 1M Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  DeepSeek | 1.6T | 1.0M | $1.74 / $3.48 | ||
| 2 | DeepSeek | 284B | 1.0M | $0.14 / $0.28 | ||
| 3 | Google | — | 1.0M | $0.07 / $0.30 |
FAQ
Common questions about MRCR 1M.
More evaluations to explore
Related benchmarks in the same category
A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.
A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.
Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions