ActiveLong Context
OpenAI-MRCR
MRCR v2 8-needle long-context benchmark where a model must recover the requested instance from repeated similar requests in a synthetic conversation.
Top Benchmarks & Trusted AI Evaluation Directory
Comprehensive directory of 63+ LLM benchmarks with honest status labels, contamination risk assessments, and vendor vs independent score provenance as of September 2026.
Capability Domains
Status
Contamination
Sort By
Displaying 11 of 11 benchmarks (filtered from 63)
Nearing SaturationLong Context
RULER
Configurable synthetic benchmark that expands needle retrieval into multi-needle, multi-hop tracing, and aggregation tasks.
ActiveLong Context
LongBench-Pro
Realistic bilingual long-context benchmark with 1,500 natural samples across 11 primary and 25 secondary tasks from 8k to 256k tokens.
Nearing SaturationLong Context
QuALITY
Multiple-choice long-document QA where writers and validators read the full article, with a hard subset designed to defeat skimming.
Nearing SaturationLong Context
ZeroSCROLLS
Zero-shot long-text suite derived from SCROLLS with test-only tasks and new aggregation-style long-context challenges.
Nearing SaturationLong Context
QASPER
Scientific-paper QA over full NLP papers, with abstractive, extractive, yes/no, and unanswerable answers plus supporting evidence.
Nearing SaturationLong Context
QMSum
Query-focused meeting summarization over long transcripts from academic, product, and committee meetings.
Nearing SaturationLong Context
SCROLLS
Seven-task long-sequence suite covering summarization, QA, and NLI over naturally long English texts.
Nearing SaturationLong Context
InfiniteBench
100k+ token benchmark mixing realistic and synthetic tasks across English, Chinese, code, math, and retrieval.
Nearing SaturationLong Context
LongBench
Bilingual multitask suite for long-context understanding across QA, summarization, few-shot learning, synthetic tasks, and code completion.
SaturatedLong Context
Needle-in-a-Haystack
Synthetic retrieval stress test that hides facts at different depths and context lengths, then asks the model to recover them.