AI statistics, benchmarks & research.
Find the numbers behind the headlines, compare model performance, and understand how AI works. A curated collection of websites worth exploring.
50 of 50 websites
- Benchmarks & leaderboards
Artificial Analysis
Compare AI models and hosting providers by benchmark results, speed, latency and price.
Useful for Shortlisting models before testing your own workload.
Reading note
Read the methodology and configuration behind each comparison.
- Benchmarks & leaderboards
Arena
Explore model leaderboards based on human preferences across supported text and media tasks.
Useful for Seeing how users rate competing model outputs.
Reading note
Preference rankings depend on the voter pool, task mix and uncertainty.
- Statistics & trends
Epoch AI
Explore research and datasets on AI capability, compute, training and broader industry trends.
Useful for Understanding the evidence behind AI progress claims.
Reading note
Check the date, definitions and uncertainty of each dataset.
- Statistics & trends
Stanford AI Index
Read annual reports and supporting data about AI research, investment, adoption and social impact.
Useful for Finding statistics for business plans and presentations.
Reading note
Annual reports describe a dated snapshot, not live market totals.
- Statistics & trends
Our World in Data AI
Browse charts, data and explanations about the development and impact of artificial intelligence.
Useful for Finding understandable, source-linked AI statistics.
Reading note
Read each chart's underlying source and measurement notes.
- Policy & responsible AI
OECD AI Policy Observatory
Explore AI policy information, country activity and data from the OECD's AI observatory.
Useful for Comparing AI policy developments across countries.
Reading note
Policy trackers are reference material; confirm current rules with the relevant authority.
- Benchmarks & leaderboards
HELM
Explore Stanford's framework and results for evaluating language models across several dimensions.
Useful for Looking beyond a single overall model score.
Reading note
Compare models within the same evaluation setting and version.
- Benchmarks & leaderboards
MLCommons
Find standardised machine-learning benchmarks and published results for systems and workloads.
Useful for Comparing AI hardware and system performance.
Reading note
Hardware, software and benchmark division affect comparability.
- Benchmarks & leaderboards
SWE-bench
View software-engineering benchmark results based on resolving issues in real code repositories.
Useful for Assessing coding agents on repository-level tasks.
Reading note
Check the benchmark variant, agent setup and evaluation rules.
- Benchmarks & leaderboards
Terminal-Bench
Explore evaluations of agents completing tasks in a terminal environment.
Useful for Understanding command-line and agent-workflow capabilities.
Reading note
Results depend on the agent, model, tools and benchmark release.
- Benchmarks & leaderboards
LiveBench
Compare language-model results across periodically refreshed evaluation tasks and releases.
Useful for Checking performance across several task categories.
Reading note
Use the same release when comparing scores.
- Benchmarks & leaderboards
LiveCodeBench
Explore coding-model evaluations built from time-stamped programming problems.
Useful for Comparing coding performance with attention to data contamination.
Reading note
Task dates and evaluation windows affect the reported result.
- Benchmarks & leaderboards
SWE-rebench
Browse a continuously refreshed software-engineering benchmark and its model results.
Useful for Following coding-agent progress on newer repository tasks.
Reading note
Check the task set, date and agent configuration before comparing runs.
- Benchmarks & leaderboards
ARC Prize
Explore reasoning benchmarks, public results and research competitions associated with ARC.
Useful for Understanding evaluation of unfamiliar reasoning tasks.
Reading note
ARC results measure a specific task family, not all forms of intelligence.
- Benchmarks & leaderboards
METR
Read evaluations and research about advanced AI capabilities, autonomous work and associated risks.
Useful for Understanding how longer agent tasks are evaluated.
Reading note
Read the experimental assumptions and uncertainty alongside the headline results.
- Benchmarks & leaderboards
Vals AI
Browse model evaluations across specialist domains and practical workflows.
Useful for Finding domain-specific evidence when choosing an AI model.
Reading note
Read each benchmark's task design and scoring method.
- Benchmarks & leaderboards
SimpleBench
View a benchmark focused on difficult everyday reasoning questions for language models.
Useful for Exploring reasoning weaknesses that broad scores can hide.
Reading note
A narrow benchmark cannot establish overall model quality.
- Model & price trackers
LLM Stats
Browse model specifications, benchmark comparisons and pricing-oriented reference tables.
Useful for Comparing models and identifying candidates to investigate.
Reading note
Composite scores and listed prices should be checked against their sources.
- Model & price trackers
LLM Price Check
Compare published language-model API prices across supported providers.
Useful for Estimating which model APIs may fit a budget.
Reading note
Confirm current provider rates, caching rules and billing units before purchasing.
- Statistics & trends
State of AI Report
Read an annual overview of AI research, industry, policy and safety developments.
Useful for Getting a broad overview of the AI ecosystem.
Reading note
Use the edition date when citing figures or conclusions.
- Research libraries
AI2 Research
Browse research and publications from the Allen Institute for AI.
Useful for Following work from a research organisation building open AI systems.
Reading note
Institutional research describes the authors' own work and priorities.
- Research libraries
arXiv AI
Browse recent artificial-intelligence preprints and links to full papers.
Useful for Tracking new research before formal publication.
Reading note
Preprints may not have completed peer review.
- Research libraries
OpenReview
Read research submissions, reviews and discussion where participating venues make them public.
Useful for Checking both a paper and the surrounding review context.
Reading note
Submission and review visibility varies by venue.
- Research libraries
Journal of Machine Learning Research
Read openly accessible machine-learning research papers from JMLR.
Useful for Finding detailed research with journal publication context.
Reading note
Technical papers may require statistical or machine-learning background.
- Research libraries
Distill
Explore visual and interactive explanations of machine-learning research.
Useful for Developing intuition for technical concepts.
Reading note
This is an archive; check individual article dates.
- News & analysis
The Gradient
Read essays, interviews and analysis about AI research and its implications.
Useful for Finding accessible perspectives on technical and social AI topics.
Reading note
Distinguish author commentary from primary research evidence.
- Research libraries
Proceedings of Machine Learning Research
Browse open proceedings from machine-learning conferences and workshops.
Useful for Finding published papers by venue or volume.
Reading note
Review practices and scope vary between conferences and workshops.
- Research libraries
NeurIPS Proceedings
Search and read papers from NeurIPS proceedings and benchmark tracks.
Useful for Exploring published work from a major machine-learning conference.
Reading note
Check the publication year and paper-specific evidence.
- Research libraries
ICLR
Find conference programmes, accepted research and links to ICLR proceedings.
Useful for Following research on representation learning and related AI methods.
Reading note
Conference pages can point to different years; check the selected edition.
- Research libraries
ICML
Find conference information, accepted papers and programme resources from ICML.
Useful for Tracking machine-learning research and conference activity.
Reading note
Use the relevant conference year when citing work.
- Research libraries
ACL Anthology
Search a large collection of computational-linguistics and natural-language-processing publications.
Useful for Researching language technology and NLP methods.
Reading note
Read the original paper and venue details before relying on an abstract.
- Research libraries
OpenML
Explore shared datasets, machine-learning tasks and experiment results.
Useful for Finding datasets and reproducible evaluation material.
Reading note
Dataset licences, provenance and task definitions vary.
- Policy & responsible AI
Center for Security and Emerging Technology
Read data-led research on AI, emerging technology and public policy.
Useful for Understanding AI investment, talent and strategic policy questions.
Reading note
Reports reflect their scope and publication date.
- Policy & responsible AI
AI Incident Database
Explore documented reports of incidents involving AI systems and their impacts.
Useful for Learning from reported failures and operational risks.
Reading note
Reports and classifications are not a complete or uniformly verified census.
- Policy & responsible AI
AIAAIC Repository
Browse a repository of AI, algorithmic and automation incidents and controversies.
Useful for Researching the social effects and reported harms of automated systems.
Reading note
Check the linked reporting and distinguish allegations from established facts.
- Policy & responsible AI
NIST AI Resource Center
Find resources for AI risk management, evaluation and trustworthy deployment.
Useful for Planning an organisation's approach to AI risk.
Reading note
Guidance is a framework for applying judgement, not an automatic compliance certificate.
- Policy & responsible AI
Partnership on AI
Read research, practice guidance and resources on responsible AI.
Useful for Finding practical discussions of AI governance and impacts.
Reading note
Check the contributors, intended audience and scope of each resource.
- Policy & responsible AI
GovAI
Read research about governing increasingly capable AI systems.
Useful for Understanding policy options and institutional challenges.
Reading note
Research recommendations are analysis, not enacted law.
- Policy & responsible AI
AI Standards Hub
Explore AI standards information, learning resources and community activity.
Useful for Understanding standards relevant to AI development and governance.
Reading note
Confirm the version and applicability of any standard with its publisher.
- News & analysis
Import AI
Read a research-focused AI newsletter with commentary on technical and policy developments.
Useful for Keeping up with research in an editorial digest.
Reading note
Follow links to original research; access conditions can vary by post.
- News & analysis
AI as Normal Technology
Read analysis of AI capabilities, limitations and societal effects from AI as Normal Technology.
Useful for Examining claims about what AI can and cannot do.
Reading note
Commentary expresses the authors' analysis; consult the evidence they cite.
- News & analysis
Interconnects
Read technical commentary on language models, post-training and the AI research ecosystem.
Useful for Following model-development ideas and research debates.
Reading note
Some posts may have subscriber access requirements.
- News & analysis
The Batch
Read a digest of AI news, research and industry developments from DeepLearning.AI.
Useful for Getting a concise overview of recent AI developments.
Reading note
Check linked primary sources for the complete context.
- Policy & responsible AI
AI Now Institute
Read research and policy analysis on the power, incentives and social effects of AI.
Useful for Understanding institutional and societal questions around AI deployment.
Reading note
Policy analysis reflects a particular research perspective and publication date.
- Visual explainers & learning
Dive into Deep Learning
Read an interactive deep-learning textbook with mathematical explanations and runnable examples.
Useful for Studying how machine-learning models are built.
Reading note
Programming and mathematics background help; environment setup may be required.
- Visual explainers & learning
Full Stack Deep Learning
Explore material about building and operating AI-powered products.
Useful for Learning how model work connects to deployed applications.
Reading note
Check course and session dates because development practices change.
- Visual explainers & learning
LLM Visualization
Explore a three-dimensional walkthrough of a language model's computation.
Useful for Building a visual understanding of how a transformer processes text.
Reading note
The visualisation illustrates a particular model and simplifies the wider landscape.
- Visual explainers & learning
Transformer Explainer
Interact with a visual explanation of transformer components and text generation.
Useful for Learning how attention and generation fit together.
Reading note
It is an educational demonstration, not a model-quality benchmark.
- Visual explainers & learning
3Blue1Brown Neural Networks
Watch visual mathematical explanations of neural networks and related ideas.
Useful for Developing intuition before diving into implementation.
Reading note
Conceptual lessons do not replace hands-on training or current implementation guidance.
- Visual explainers & learning
The Illustrated Transformer
Read a visual walkthrough of the original transformer architecture.
Useful for Understanding attention and the foundations of language-model architecture.
Reading note
This is a foundational explanation; newer model designs differ.