LAB-Bench
LAB-Bench (the Language Agent Biology Benchmark) is an AI benchmark of more than 2,400 multiple-choice questions built to measure how well language models and AI agents can perform practical biology research…
Model Evaluation
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LLM API Pricing Comparison
Every figure in this article is a US dollar list rate per 1,000,000 tokens, taken from the provider's own pricing page.
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 Context Window Comparison
As of July 2026, the largest context window ever announced belongs to Magic's LTM-2-mini at 100,000,000 tokens (100M), but it is a research prototype that has never been publicly released.
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-Verifier
LLM-as-a-Verifier is a probabilistic verification framework and open-source Python package for scoring and selecting large language model agent trajectories.
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.
LM Evaluation Harness
LM Evaluation Harness (the Language Model Evaluation Harness, often abbreviated lm-eval or written lm-evaluation-harness) is an open-source software framework for measuring the performance of large language…
LMArena
LMArena is a crowdsourced artificial intelligence evaluation platform and company that ranks large language models by having anonymous users vote on which of two blind, side-by-side model responses they prefer
LangSmith
LangSmith is a commercial observability, evaluation, and deployment platform for large language model (LLM) applications and AI agents, developed and operated by LangChain Inc. It provides developers and…
Langfuse
Langfuse is an open-source LLM engineering platform that provides observability, tracing, prompt management, evaluation, and dataset tooling for applications built on large language models.
LongBench v2
LongBench v2 is a benchmark for evaluating how well large language models understand and reason over long contexts.
Loss Curve
A loss curve is a plot that shows the value of a loss function over the course of training a machine learning model.
MATH
MATH is a benchmark of 12,500 competition mathematics problems used to evaluate the mathematical problem-solving ability of machine learning systems, particularly large language models.
METEOR (metric)
METEOR (Metric for Evaluation of Translation with Explicit ORdering) is an automatic evaluation metric for machine translation and other text-generation tasks that scores a candidate sentence against one or…
MMLU-Redux
MMLU-Redux is a manually re-annotated, error-corrected subset of the Massive Multitask Language Understanding (MMLU) benchmark
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…
Machine-generated text detection
Machine-generated text detection is the problem of deciding whether a given passage of text was written by a person or produced by a large language model.
Mean Absolute Error (MAE)
Mean Absolute Error (MAE) is a regression accuracy metric and loss function that measures the average absolute difference between predicted values and actual observed values
Mean Squared Error (MSE)
Mean Squared Error (MSE), also called mean squared deviation (MSD), is the average of the squared differences between predicted values and actual (observed) values
Model Capacity
Model capacity is the size and richness of the family of functions a machine learning model can represent and learn, which determines how complex a pattern the model can fit.
MuSR
MuSR (Multistep Soft Reasoning) is a benchmark for evaluating multistep reasoning in large language models, built around long free-text narratives such as murder mysteries, object-placement scenarios, and…
Multi-SWE-bench
Multi-SWE-bench is a multilingual benchmark for evaluating the ability of large language model based coding systems to resolve real-world software issues across seven programming languages.
NIST ARIA
NIST ARIA (Assessing Risks and Impacts of AI) is a testing, evaluation, validation, and verification (TEVV) program operated by the United States National Institute of Standards and Technology (NIST) to…
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…
OlympiadBench
OlympiadBench is an AI benchmark of 8,476 Olympiad-level mathematics and physics problems, designed to test the advanced scientific reasoning of large language models and large multimodal models.
Omni-MATH
Omni-MATH is an AI benchmark of Olympiad-level competition mathematics, introduced in October 2024 to measure the mathematical reasoning ability of large language models on problems far harder than those in…
Out-of-bag evaluation (OOB evaluation)
Out-of-bag (OOB) evaluation, sometimes called out-of-bag estimation or OOB error, is a model validation technique used with bagging-based ensemble methods such as random forests and bagged decision trees.
Overfitting
Overfitting is a failure of generalization: a fitted model or a model-selection procedure performs better on the observations used to develop it than on new observations from the population of interest.
PR AUC
PR AUC (Precision-Recall Area Under the Curve), also referred to as AUPRC or AUC-PR, is a classification evaluation metric that quantifies the area beneath a precision-recall curve.
Pass@k
Pass@k is the standard metric for evaluating code generation models: it measures the probability that at least one of k generated candidate solutions passes all of a problem's unit tests.
Patronus AI
Patronus AI is an automated LLM evaluation, observability, and guardrails platform founded in 2023 and headquartered in San Francisco.
Precision
Precision is a classification metric defined as the fraction of positive predictions that are correct: Precision = TP / (TP + FP), where TP is the number of true positives and FP is the number of false…
Precision-Recall Curve
A precision-recall curve (PR curve) is a graph that plots precision on the y-axis against recall on the x-axis at every possible classification threshold for a binary classification model.
Prediction Bias
Prediction bias is the difference between the average of a machine learning model's predictions and the average of the ground-truth labels in a dataset.
Process reward model (PRM)
A process reward model (PRM), also called a process-supervised reward model or step-level verifier, is a learned scoring model that evaluates the correctness or quality of each intermediate step in a large…
ProcessBench
ProcessBench is a benchmark for step-level verification of mathematical reasoning, built by the Qwen Team at Alibaba and released in December 2024.
PutnamBench
PutnamBench is a benchmark for evaluating automated theorem proving systems, especially neural and large-language-model-based provers, on competition mathematics.
RE-Bench
RE-Bench (short for Research Engineering Benchmark) is a benchmark for evaluating the frontier AI research-and-development capabilities of large language model agents, developed by the nonprofit METR (Model…
ROC (Receiver Operating Characteristic) Curve
A Receiver Operating Characteristic (ROC) curve is a graph that measures how well a binary classification system separates two classes by plotting its true positive rate (TPR, also called sensitivity or…
ROUGE
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of automatic metrics that score the quality of a machine-generated text summary by counting how many overlapping units (n-grams, word…
Real-SWE
Real-SWE is a coding-agent benchmark published in September 2026 by Specific Labs, a San Francisco company that licenses operational data and source code from businesses and packages them as training and…
Recall (metric)
Recall is a classification and retrieval metric that measures the proportion of actual positive instances a model correctly identifies, defined as TP / (TP + FN)
RewardBench
RewardBench is a benchmark and public leaderboard for evaluating reward models, the scoring functions that sit at the center of reinforcement learning from human feedback (RLHF).
Root Mean Squared Error (RMSE)
Root Mean Squared Error (RMSE), also known as root mean square deviation (RMSD), is a regression evaluation metric equal to the square root of the average of the squared differences between predicted and…
SWE-bench Verified
SWE-bench Verified is a 500-problem, human-validated subset of the SWE-bench software engineering benchmark, released on August 13
Sabotage evaluations
Sabotage evaluations are a set of tests, introduced by Anthropic in October 2024, that try to measure whether a frontier language model is capable of covertly subverting human oversight, decision-making, and…
Sandbagging (artificial intelligence)
Sandbagging, in the context of AI safety, refers to the strategic and intentional underperformance of an AI system on a capability evaluation or specific task, typically to hide a capability from human…
Scale SEAL Leaderboards
The SEAL Leaderboards are a set of expert-curated, contamination-resistant evaluation leaderboards for frontier large language models, produced by the Safety, Evaluations and Alignment Lab (SEAL) at Scale AI.
ScienceAgentBench
ScienceAgentBench is an AI benchmark for evaluating whether language model agents can perform real, data-driven scientific analysis by writing and executing code.
Self-Taught Evaluator
Self-Taught Evaluator is a method for training a strong LLM-as-a-judge without any human preference annotations, using synthetic training data and an iterative self-improvement loop.
SimpleQA Verified
SimpleQA Verified is a short-form factuality benchmark released by Google DeepMind and Google Research in September 2025 that measures the parametric knowledge of large language models using roughly 1,000…
SkillEvaluator
SkillEvaluator is an open-source command-line framework developed by NVIDIA for evaluating artifacts used by AI agents.
Spider 2.0
Spider 2.0 is a benchmark for evaluating large language models on real-world enterprise text-to-SQL workflows.
Splitter
A splitter is a term used in two distinct senses in machine learning. The first and most common sense is a utility that partitions a dataset into subsets such as training, validation, and test sets, or into…
Stanford HAI AI Index Report
The AI Index Report is an annual, independent, data-driven publication that tracks, distills, and visualizes trends in artificial intelligence across research and development, technical performance, the…
State of AI Report
The State of AI Report is a free, annual review of progress in artificial intelligence, published every year since 2018.
StrongREJECT
StrongREJECT is a benchmark and automated evaluator for measuring how well jailbreaking attacks actually work against large language models.
SuperGPQA
SuperGPQA is a large graduate-level knowledge and reasoning benchmark for evaluating large language models across 285 academic disciplines.
Task-completion time horizon (METR)
The task-completion time horizon is a metric for AI capability proposed by METR that expresses a model's ability in units of human time: it is the length of task