🤖 AI Leaderboards Tour

Explore the benchmarks that define AI progress

🤗 Hugging Face Open LLM Leaderboard

The most popular community-driven AI model ranking

📈 Comprehensive Evaluation
Tests models across multiple benchmarks including ARC, HellaSwag, MMLU, TruthfulQA, Winogrande, and GSM8k to provide a holistic view of model capabilities.
🌍 Open Source Focus
Specifically designed to evaluate and rank open-source language models, promoting transparency and accessibility in AI development.
👥 Community Driven
Anyone can submit models for evaluation, creating a democratic platform where the community determines the best performing models.
🔄 Regular Updates
Continuously updated with new model submissions and evaluations, keeping pace with the rapidly evolving AI landscape.
Key Evaluation Areas
🎯
Reasoning (ARC)
📚
Knowledge (MMLU)
✅
Truthfulness
🔢
Math (GSM8k)
Top Performing Model Categories:
Llama 2 Variants Mistral Models CodeLlama Falcon Series MPT Models

Quick Facts

  • Most visited AI leaderboard with millions of monthly views
  • Evaluates models from 1B to 70B+ parameters
  • Standardized evaluation pipeline ensures fair comparisons
  • Provides detailed breakdown of performance across different tasks

⚔️ Chatbot Arena (LMSYS)

Human preference-based model evaluation through battle royale

🥊 Head-to-Head Battles
Models compete in anonymous pairwise comparisons where human users judge which response is better, creating an ELO-based ranking system.
👤 Human Preference
Unlike automated benchmarks, this measures what humans actually prefer in real conversations, providing insights into practical utility.
🎭 Anonymous Evaluation
Users don't know which models they're comparing, eliminating bias and ensuring judgments are based purely on response quality.
📊 ELO Rating System
Uses the same rating system as chess tournaments, providing a robust and interpretable ranking that accounts for the strength of opponents.
Battle Statistics
1M+
Battles Fought
100K+
Daily Users
50+
Models Tested
⚡
Live Rankings
Current Top Performers:
GPT-4 Claude-3 Gemini Pro GPT-3.5 Turbo Llama-2-Chat

Why It Matters

  • Captures real-world conversational performance
  • Reveals gaps between benchmark scores and user satisfaction
  • Provides category-specific rankings (coding, creative writing, etc.)
  • Continuously updated with crowd-sourced evaluations

📊 BIG-bench

Beyond the Imitation Game collaborative benchmark

🌐 Massive Scale
Contains over 200 diverse tasks designed to probe different aspects of language model capabilities, from reasoning to knowledge.
🤝 Collaborative Effort
Developed by researchers from 130+ institutions worldwide, ensuring diverse perspectives and comprehensive coverage.
🔮 Future-Focused
Designed to remain challenging as models improve, with many tasks intended to be difficult even for future advanced systems.
🎯 Beyond Imitation
Tests capabilities that go beyond simple pattern matching, probing for genuine understanding and reasoning abilities.
Benchmark Scope
204
Total Tasks
130+
Contributing Institutions
23
Task Categories
∞
Difficulty Levels
Task Categories Include:
Logical Reasoning World Knowledge Common Sense Language Understanding Mathematical Reasoning Bias Detection

Key Features

  • Includes tasks humans find easy but machines struggle with
  • Many tasks designed to be unsolvable by current models
  • Comprehensive evaluation across multiple domains
  • Open-source and freely available for research

🎯 HELM (Holistic Evaluation of Language Models)

Stanford's comprehensive model evaluation framework

🎯 Holistic Approach
Evaluates models across accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency - providing a complete picture.
📊 Standardized Metrics
Uses consistent evaluation protocols across all models and scenarios, enabling fair and meaningful comparisons.
🌍 Diverse Scenarios
Tests models across 42+ scenarios covering question answering, information retrieval, summarization, sentiment analysis, and more.
⚖️ Ethical Focus
Explicitly measures fairness, bias, and harmful content generation, addressing crucial responsible AI concerns.
Evaluation Dimensions
📈
Accuracy
🎯
Calibration
🛡️
Robustness
⚖️
Fairness
⚡
Efficiency
🔒
Safety
Evaluation Scenarios:
Question Answering Summarization Information Retrieval Sentiment Analysis Toxicity Detection Bias Measurement

What Makes HELM Unique

  • First benchmark to systematically evaluate model safety and fairness
  • Provides transparency through detailed methodology documentation
  • Covers both open-source and proprietary models
  • Regularly updated with new models and evaluation criteria

🧠 MMLU (Massive Multitask Language Understanding)

The gold standard for measuring AI knowledge and reasoning

📚 Comprehensive Knowledge
Tests knowledge across 57 subjects from elementary math to advanced law, professional medicine, and academic philosophy.
🎓 Academic Rigor
Questions sourced from actual academic and professional exams, ensuring real-world relevance and difficulty.
🌐 Multitask Design
Evaluates few-shot learning ability by testing across diverse domains without domain-specific training.
📊 Standardized Format
Multiple-choice format ensures objective evaluation and easy comparison across different models and systems.
Knowledge Domains
57
Total Subjects
15.9K
Total Questions
4
Difficulty Levels
🎯
Objective Scoring
Subject Areas Include:
Mathematics Physics Chemistry Biology Computer Science Law Medicine History Philosophy

Why MMLU Matters

  • Widely considered the most comprehensive knowledge benchmark
  • Strong correlation with general intelligence measures
  • Used by major AI companies to report model capabilities
  • Difficulty ranges from high school to professional level

🔗 SuperGLUE

Advanced benchmark for general language understanding

🏆 GLUE's Successor
Created after models surpassed human performance on GLUE, designed to be more challenging and comprehensive.
🤔 Reasoning Focus
Emphasizes complex reasoning tasks including reading comprehension, textual entailment, and coreference resolution.
📏 Human Baseline
Includes human performance baselines for each task, providing clear targets for model improvement.
🎯 Diagnostic Analysis
Includes diagnostic datasets that help identify specific model weaknesses and areas for improvement.
Benchmark Composition
8
Core Tasks
1
Diagnostic Set
🏅
Human Baseline
📊
Overall Score
Task Categories:
Reading Comprehension Textual Entailment Word Sense Disambiguation Coreference Resolution Question Answering Natural Language Inference

SuperGLUE Significance

  • Established the standard for language understanding benchmarks
  • Many tasks still challenging for current state-of-the-art models
  • Widely used in academic research and industry evaluation
  • Provides detailed performance analysis across different reasoning types

Ready to Explore These Leaderboards?

Each leaderboard offers unique insights into AI capabilities. Visit them to see the latest rankings and discover cutting-edge models!

🤗 Hugging Face Leaderboard ⚔️ Chatbot Arena 📊 BIG-bench 🎯 HELM 🧠 MMLU 🔗 SuperGLUE