// category · 11 tools · 2 open source
LLM Leaderboards
Public rankings that compare models on preference, accuracy, speed or price. Read the methodology: each leaderboard measures something different.
Leaderboards rank AI models by combining benchmark results, human votes or private test sets into a single table. They are where most people first see claims such as "the best model right now," so this category lists the main boards and what each one actually measures.
They fall into three types.
Aggregated benchmark indexes. Artificial Analysis runs models through the same suite of evaluations under identical settings and combines them into its Intelligence Index, alongside speed, latency and price per token. The Epoch AI Benchmarking Hub tracks results across many benchmarks over time.
Human preference arenas. Arena, formerly LMArena, asks people to compare two anonymous answers and vote, then turns the votes into ratings, with a style control adjustment for longer or heavily formatted answers. That captures what users prefer, which is not the same as accuracy on hard questions.
Private or specialist boards. Scale's SEAL leaderboards use private expert built datasets, which are hard to train on but impossible for outsiders to inspect. Specialist boards cover narrower skills: the Berkeley Function Calling Leaderboard for tool use, Aider's leaderboards for code editing, MTEB for embedding models, and the Vectara hallucination leaderboard for how often summaries stay faithful to the source.
How to read any leaderboard:
- Check the date. Rankings change with every major release.
- Check the method. A benchmark index, a vote based rating and a private test answer different questions.
- Look at the spread. If confidence intervals overlap, models are effectively tied.
- Match it to your task. A model that tops a chat arena may not lead on code or retrieval.
- Ask who runs it. Some operators also sell services to model developers.
No leaderboard replaces testing on your own data. Use boards to build a shortlist, then run your own evaluation set.
Our LLM leaderboards compared guide covers each board in detail, and LLM benchmarks explained describes the tests underneath them. For a single benchmark that many boards include, see the HLE Diamond guide.
Artificial Analysisartificialanalysis.aiIndependent benchmarking of model intelligence, speed and price across providers, updated as new models launch.LeaderboardsFreeHN 92
LiveBenchlivebench.aiBenchmark refreshed with new questions regularly and scored against objective answers to limit test set contamination.LeaderboardsOpen source★ 1.3k
Berkeley Function Calling Leaderboardgorilla.cs.berkeley.eduLeaderboard measuring how accurately models call functions and tools across languages and multiturn settings.LeaderboardsFree★ 13.1k
Aider Leaderboardsaider.chatAider's code editing benchmarks that test how well models edit real source files across several programming languages.LeaderboardsFree★ 49.8k
MTEBhuggingface.coMassive Text Embedding Benchmark and leaderboard for comparing embedding models on retrieval, clustering and more.LeaderboardsOpen source★ 3.5k
Vectara Hallucination Leaderboardgithub.comPublic leaderboard that scores how often models introduce unsupported facts when summarizing documents, using Vectara's open HHEM evaluation model.LeaderboardsFree★ 3.3k
Arena (LMArena)arena.aiCrowdsourced arena, formerly LMArena, where people compare anonymous model responses side by side, producing preference based leaderboards.LeaderboardsFreeHN 4
Fingerprinting Leaderboardfingerprint.sentient.foundationOpen Sentient Foundation leaderboard that ranks LLM fingerprinting schemes on retention and adversarial robustness under a fixed detect neighbor attack.LeaderboardsFreeHN 1
Epoch AI Benchmarking Hubepoch.aiEpoch AI's independent runs of key benchmarks with open data showing how frontier capabilities change over time.LeaderboardsFree
SEAL Leaderboardslabs.scale.comScale AI's expert driven leaderboards using private datasets to reduce contamination and gaming.LeaderboardsFree
Vellum Leaderboardvellum.aiComparison table of recent models across reasoning, coding and cost metrics, maintained by the Vellum platform.LeaderboardsFree// guides
Guides to LLM leaderboards
LLM Leaderboards Compared: Which Ones to Trust
Every major LLM leaderboard compared: how Arena, Artificial Analysis, LiveBench, SEAL, Epoch AI and Vellum score models, and where each one can mislead you.
LiveBench vs Artificial Analysis: Key Differences
LiveBench vs Artificial Analysis: how the two AI leaderboards differ on tasks, grading, weighting and cost, why their rankings disagree, and when to trust each.
LLM Benchmarks Explained: What the Scores Mean
LLM benchmarks explained: what MMLU, GPQA Diamond, Humanity's Last Exam, SWE-bench and ARC-AGI measure, why scores saturate, and how to spot contamination.
FrontierCode Benchmark Explained: How It Scores
The FrontierCode benchmark explained: how Cognition grades whether maintainers would merge AI code, what Main and Diamond mean, and how to read the leaderboards.
MMLU vs GPQA: Key Differences and Which to Use
MMLU vs GPQA explained: broad exam recall versus expert written science questions, why GPQA scores run lower, and which benchmark still means anything in 2026.
SWE-Bench Pro Explained: V1, V2, Scores
SWE-Bench Pro explained: how the Scale AI coding benchmark differs from Verified, what changed in V2, and how to read public and private leaderboard scores.
HLE Diamond Leaderboard and Scores (October 2026)
HLE Diamond leaderboard and scores for October 2026: the 1,000 question Humanity's Last Exam subset, Scale and lastexam.ai results, with and without tools.
Coding Agent Benchmark Rankings (October 2026)
Coding agent benchmark rankings from SWE-Bench Pro, FrontierCode, Terminal-Bench and the Artificial Analysis index, with sourced scores, dates and why they differ.
What Is Faithfulness in RAG Evaluation?
What is faithfulness in RAG evaluation? The share of an answer's claims backed by the retrieved context, and how Ragas, DeepEval, TruLens and HHEM score it.
GDPval Benchmark Explained: Tasks, Scores, Limits
The GDPval benchmark tests AI on real work from 44 occupations. How tasks and grading work, how to read the GDPval-AA leaderboard, and what it misses.
How to Evaluate LLM Model Performance
How to evaluate LLM model performance before you pick one: build a task test set, fix settings, add error bars, test prompt sensitivity, then weigh cost.
MMLU vs MMLU Pro: Key Differences and Which to Use
MMLU vs MMLU Pro explained: how the 4 option and 10 option benchmarks differ, why their scores aren't comparable, and which one to trust for LLMs in 2026.
AI Model Fingerprinting Explained
AI model fingerprinting embeds ownership signals in LLMs. How the Fingerprinting Leaderboard works, SaTML 2026 findings, and how it differs from SynthID.
LLM Hallucination Detection: Methods and Tools
How LLM hallucination detection works in 2026: faithfulness checks, detector models like HHEM, self consistency and semantic entropy, and the tools to use.