AA-Index

No official academic documentation found for this benchmark. Extensive research through ArXiv, IEEE/ACL/NeurIPS papers, and university research sites yielded no peer-reviewed sources for an 'aa-index' benchmark. This entry requires verification from official academic sources.

GLM-4.5 from Zhipu AI currently leads the AA-Index leaderboard with a score of 0.677 across 3 evaluated AI models.

Zhipu AIGLM-4.5 leads with 67.7%, followed by Zhipu AIGLM-4.5-Air at 64.8% and MiniMaxMiniMax M2 at 61.0%.

Progress Over Time

Interactive timeline showing model performance evolution on AA-Index

State-of-the-art frontier
Open
Proprietary

AA-Index Leaderboard

3 models
ContextCostLicense
1
Zhipu AI
Zhipu AI
355B
2
Zhipu AI
Zhipu AI
106B
3
MiniMax
MiniMax
230B1.0M$0.30 / $1.20
Notice missing or incorrect data?

FAQ

Common questions about AA-Index.

What is the AA-Index benchmark?

No official academic documentation found for this benchmark. Extensive research through ArXiv, IEEE/ACL/NeurIPS papers, and university research sites yielded no peer-reviewed sources for an 'aa-index' benchmark. This entry requires verification from official academic sources.

What is the AA-Index leaderboard?

The AA-Index leaderboard ranks 3 AI models based on their performance on this benchmark. Currently, GLM-4.5 by Zhipu AI leads with a score of 0.677. The average score across all models is 0.645.

What is the highest AA-Index score?

The highest AA-Index score is 0.677, achieved by GLM-4.5 from Zhipu AI.

How many models are evaluated on AA-Index?

3 models have been evaluated on the AA-Index benchmark, with 0 verified results and 3 self-reported results.

What categories does AA-Index cover?

AA-Index is categorized under general. The benchmark evaluates text models.

More evaluations to explore

Related benchmarks in the same category

View all general
GPQA

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.

general
213 models
MMLU-Pro

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.

general
119 models
MMLU

Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains

general
99 models
LiveCodeBench

LiveCodeBench is a holistic and contamination-free evaluation benchmark for large language models for code. It continuously collects new problems from programming contests (LeetCode, AtCoder, CodeForces) and evaluates four different scenarios: code generation, self-repair, code execution, and test output prediction. Problems are annotated with release dates to enable evaluation on unseen problems released after a model's training cutoff.

general
71 models
IFEval

Instruction-Following Evaluation (IFEval) benchmark for large language models, focusing on verifiable instructions with 25 types of instructions and around 500 prompts containing one or more verifiable constraints

general
63 models
MMMU

MMMU (Massive Multi-discipline Multimodal Understanding) is a benchmark designed to evaluate multimodal models on college-level subject knowledge and deliberate reasoning. Contains 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering across 30 subjects and 183 subfields.

generalmultimodal
62 models