AdvBench
AdvBench (Adversarial Behavior Benchmark) is a red-teaming benchmark dataset for measuring how easily an aligned large language model can be pushed into producing harmful or objectionable content
Large Language Models
Explore language models, how they work, and the techniques used to build applications with them.
Explore articles
Browse subtopics: AI Benchmarks
Articles that also belong to these categories. Counts cover all of Large Language Models.
All articles (545)AI Models (184)Open Source AI (165)Chinese AI (92)Natural Language Processing (58)AI Companies (53)Machine Learning (49)Mixture of Experts (46)Multimodal AI (41)Reasoning Models (39)Artificial Intelligence (37)OpenAI (35)AI Benchmarks (33)Anthropic (31)Google DeepMind (26)AI Agents (22)AI Safety (21)Developer Tools (20)Meta AI (20)AI Code Generation (18)Small Language Models (18)Training & Optimization (18)AI Inference (16)Model Architecture (14)Generative AI (13)NVIDIA (13)Prompt Engineering (13)AI Research (12)Deep Learning (12)Google (12)Model Evaluation (12)Data & Datasets (11)AI Tools & Products (9)Conversational AI (9)Microsoft (9)Transformer Models (9)AI Infrastructure (7)Diffusion Models (7)AI Alignment (5)Enterprise AI (5)Research Organizations (5)AI Hardware (4)Healthcare AI (4)Information Retrieval (4)Interpretability (4)People (4)Mathematics (3)AI History (2)AI for Science (2)Algorithms (2)Computer Vision (2)Legal AI (2)Reinforcement Learning (2)Speech & Audio AI (2)AI Policy (1)AI Policy & Regulation (1)AI in Cybersecurity (1)AI in Gaming (1)Autonomous Vehicles (1)ChatGPT (1)China AI (1)Drug Discovery (1)Neural Networks (1)Robotics (1)Software Development (1)Video Generation (1)
Showing 1-33 of 33 articles
AgentBench
AgentBench is a multi-dimensional benchmark for evaluating large language models (LLMs) as autonomous agents across eight distinct interactive environments
AlpacaEval
AlpacaEval is an automatic evaluation framework for instruction-following large language models (LLMs) developed by Stanford University's Tatsu Lab
Artificial Analysis
Artificial Analysis is an independent benchmarking and analytics platform that evaluates artificial intelligence models and API providers across intelligence, speed, price, and latency, and it is best known…
BABILong
BABILong is a benchmark for testing how well a large language model can reason over facts scattered through very long text.
BIG-Bench
BIG-Bench (Beyond the Imitation Game Benchmark) is a large-scale, collaborative benchmark of 204 tasks, contributed by 450 authors across 132 institutions, built to measure and extrapolate the capabilities of…
Berkeley Function Calling Leaderboard
The Berkeley Function Calling Leaderboard (BFCL) is the standard benchmark for measuring how accurately large language models (LLMs) invoke functions, APIs, and tools, created by the Gorilla project at UC…
Chatbot Arena
Chatbot Arena (now branded simply as Arena, and previously known as LMArena) is a crowdsourced evaluation platform for large language models that ranks AI systems based on human preferences through anonymous…
FACTS Grounding
FACTS Grounding is a factuality benchmark from Google DeepMind and Google Research that measures whether a large language model answers a request using only the information in a provided source document
GSM8K
GSM8K (Grade School Math 8K) is an English-language benchmark of grade-school arithmetic word problems released by OpenAI researchers in 2021.
HaluEval
HaluEval (Hallucination Evaluation) is a large-scale benchmark for measuring how well large language models (LLMs) can recognize hallucinated content, that is, text that conflicts with a source or cannot be…
HarmBench
HarmBench is a standardized evaluation framework for automated red teaming and robust refusal of large language models (LLMs).
IFEval
IFEval (Instruction-Following Evaluation) is a benchmark of 541 prompts that measures how reliably large language models obey explicit, machine-checkable instructions such as "write in more than 400 words,"…
InfiniteBench
InfiniteBench (stylized as ∞Bench) is a long-context benchmark that tests whether large language models (LLMs) can genuinely process and reason over inputs longer than 100,000 tokens, using 12 tasks that span…
JailbreakBench
JailbreakBench is an open-source robustness benchmark for evaluating jailbreak attacks and defenses against large language models (LLMs).
LLM Benchmark Comparison (Leaderboard Overview)
As of July 2026, no single model wins every LLM benchmark, but Anthropic's Claude Fable 5 tops the most: it currently leads MMLU-Pro (91.5%), SWE-bench Verified (95.0%), Humanity's Last Exam in both the…
LLM Evaluation
LLM evaluation is the practice of measuring what a large language model can do, how reliably it does it, and how it behaves under adversarial or high-stakes conditions.
LLM-as-a-judge
LLM-as-a-judge is the practice of using a strong large language model to evaluate the outputs of other models, or of itself, in place of a human annotator.
LegalBench
LegalBench is a collaboratively constructed benchmark for measuring legal reasoning in large language models (LLMs)
LongBench
LongBench is a benchmark suite for evaluating the long-context understanding capabilities of large language models (LLMs).
LongBench v2
LongBench v2 is a benchmark for evaluating how well large language models understand and reason over long contexts.
MBPP
MBPP (Mostly Basic Python Problems) is a code generation benchmark of 974 crowd-sourced Python programming tasks designed to be solvable by entry-level programmers, introduced by Jacob Austin, Augustus Odena…
MMLU-Pro
MMLU-Pro (Massive Multitask Language Understanding Professional) is an artificial intelligence benchmark of 12,032 ten-choice questions across 14 academic domains
MMLU-ProX
MMLU-ProX is a multilingual benchmark for evaluating reasoning and knowledge in large language models
MMMU-Pro
MMMU-Pro is a rigorous benchmark for evaluating multimodal AI systems on college-level, expert questions that genuinely require seeing an image, built as a harder and more robust version of the original MMMU…
MRCR
MRCR (Multi-Round Co-reference Resolution) is a synthetic long-context evaluation that tests whether a large language model can locate and disambiguate among several near-identical "needles" buried inside a…
MT-Bench
MT-Bench (Multi-Turn Benchmark) is a benchmark of 80 hand-written, two-turn questions that evaluates large language models (LLMs) on multi-turn conversation and instruction following by using a strong model…
Needle in a Haystack (NIAH)
Needle in a Haystack (NIAH) is a long-context evaluation that measures whether a large language model can retrieve a single fact (the "needle") inserted at a controlled position inside a long body of text (the…
NoLiMa
NoLiMa, short for "No Literal Matching," is a long-context benchmark for large language models that measures how well a model can find and use a single relevant fact buried in a long document when that fact…
RULER (benchmark)
RULER is a synthetic benchmark from NVIDIA that measures the real, usable context window of large language models (LLMs) by testing them on 13 tasks across four categories (retrieval, multi-hop tracing…
Tokens per second
Tokens per second (TPS) is a key performance metric for measuring the speed of large language model (LLM) inference. It quantifies how many tokens a model can generate or process in one second.
WritingBench
WritingBench is a comprehensive benchmark for evaluating the generative writing capabilities of large language models (LLMs) across diverse real-world writing tasks.
ZebraLogic
ZebraLogic is a benchmark for evaluating the logical reasoning capabilities of large language models (LLMs).