CorpusQA 1M
CorpusQA 1M is a long-context question answering benchmark designed to evaluate models at approximately 1 million token contexts. Models are scored on accuracy when retrieving and reasoning over information distributed across an extremely long input corpus.
DeepSeek-V4-Pro-Max from DeepSeek currently leads the CorpusQA 1M leaderboard with a score of 0.620 across 2 evaluated AI models.
What CorpusQA 1M measures
CorpusQA 1M is a text benchmark that evaluates large language models on long context, reasoning, and general tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.6, with the leader reaching 0.6.
Compare leaders on the best AI for long context, best AI for reasoning and best AI for general leaderboards.
DeepSeek-V4-Pro-Max leads with 62.0%, followed by DeepSeek-V4-Flash-Max at 60.5%.
Progress Over Time
Interactive timeline showing model performance evolution on CorpusQA 1M
CorpusQA 1M Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | DeepSeek | 1.6T | 1.0M | $1.74 / $3.48 | ||
| 2 | DeepSeek | 284B | 1.0M | $0.14 / $0.28 |
FAQ
Common questions about CorpusQA 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