MRCR 1M (pointwise)
MRCR 1M (pointwise) is a variant of the Multi-Round Coreference Resolution benchmark that uses pointwise evaluation for ultra-long contexts (~1M tokens). This version evaluates each response independently rather than comparatively, testing models' absolute performance on long-context reasoning tasks.
Gemini 2.5 Pro from Google currently leads the MRCR 1M (pointwise) leaderboard with a score of 0.829 across 1 evaluated AI models.
What MRCR 1M (pointwise) measures
MRCR 1M (pointwise) is a text benchmark that evaluates large language models on long context, reasoning, and general tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.8, with the leader reaching 0.8.
Compare leaders on the best AI for long context, best AI for reasoning and best AI for general leaderboards.
Publication
- Paper
- Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries
- Authors
- Kiran Vodrahalli, Santiago Ontanon, Nilesh Tripuraneni, Kelvin Xu, and 20 others
- Published
- arXiv
- 2409.12640
Abstract
We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models which is also easy to automatically score. This evaluation is derived via a novel, unifying framework for evaluations over arbitrarily long contexts which measure the model's ability to do more than retrieve a single piece of information from its context. The central idea of the Latent Structure Queries framework (LSQ) is to construct tasks which require a model to ``chisel away'' the irrelevant information in the context, revealing a latent structure in the context. To verify a model's understanding of this latent structure, we query the model for details of the structure. Using LSQ, we produce three diagnostic long-context evaluations across code and natural-language domains intended to provide a stronger signal of long-context language model capabilities. We perform evaluations on several state-of-the-art models and demonstrate both that a) the proposed evaluations are high-signal and b) that there is significant room for improvement in synthesizing long-context information.
Gemini 2.5 Pro leads with 82.9%.
Progress Over Time
Interactive timeline showing model performance evolution on MRCR 1M (pointwise)
MRCR 1M (pointwise) Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Google | — | 1.0M | $1.25 / $10.00 |
FAQ
Common questions about MRCR 1M (pointwise).
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