Explore rankings of large language models

LLMs are trained via modeling, imitation learning, and reinforcement learning

Training large language models begins with pretraining the model to predict the next word in a sequence based on finding patterns in massive amounts of text. Patterns are then fine-tuned to model human-written dialogue and to align responses with user preferences.

LLMs are the backbone of AI tools that process and generate natural language

Large language models identify patterns in massive datasets of books, code, and unlabeled text from which to generate coherent responses. GPT-3 was trained on about 45 terabytes of data and uses 175 billion parameters—each parameter being a value the model adjusts as it learns.

Large language models struggle to play video games, despite coding them easily

As of 2025, given clear tasks, specific feedback, and test results to learn from, LLMs have been successful at coding simple video games that resemble pre-existing ones. However, given the variety of games, the lack of training datasets, and the need for spatial awareness, which LLMs find challenging, some of these models experience greater difficulty navigating and "winning" games.

LLMs explained simply and quickly

Large language models generate responses by predicting one token at a time, based on patterns they’ve learned from massive datasets. This explainer covers their core components: data, architecture, training, and how transformer models use context to simulate conversation.

Large language models are examples of foundation models

Such models are trained on massive amounts of data, which enable them to perform a variety of tasks, rather than being provided task-specific data to complete a narrow function. Such models come with high compute costs and potential trust issues due to the nature of their unorganized training data.

Trick a large language model into revealing secret passwords over multiple rounds, each with increasing safeguards.

Anthropic's interpretability team discusses how AI models' thinking mimics biology

Just as humans engage in complex behaviors to survive and reproduce, teams of experts in neuroscience, virology, mathematics, and other disciplines argue that large language models develop complex mechanisms to achieve their goals. These mechanisms can be manipulated to assess how they modify outputs—much like stimulating individual neurons—to uncover how LLMs "think."

Understanding how Claude Code and other AI coding agents function

Under human oversight, a supervising large language model interprets user tasks and delegates work to subordinate LLMs, which can generate code, fix bugs, and run tests, often most effectively for proofs of concept. Incremental backups and versioning are crucial when using such agents, which can lose details during their work as a result of compressing context history to work around memory limitations.

Transformer architecture can recognize and predict patterns in language

The underlying software powering text generation in AI tools associates each word or subword—called a token—with a set of values corresponding to how often it appears near other tokens. By recognizing these associations in prompts, the LLM can infer meaning.

EOS and maximum length tokens tell an LLM when to stop generating a response

Tokens are words or subwords (e.g., prefixes) that a large language model processes to generate a response. During training, the model is taught that if it ever generates a special token called end-of-sequence, its response is complete.

How ChatGPT learns to read

The introduction of ChatGPT last fall jumpstarted public interest in the future capabilities of artificial intelligence and what it means for society. Of many somewhat "jargony" terms thrown around, the phrase "Large Language Model" is commonly seen—but also incredibly unclear as to what it means. In the simplest sense, the programs comb through mountains of internet data in response to a question and guess at what should come next (subscription needed).

View a timeline of AI deployments from 2017 to 2025

The development of transformer architecture and the release of GPTs across AI labs led to rapid changes in LLM scale and power. Companies like OpenAI, Google, Meta, Microsoft, and Anthropic have invested billions of dollars to develop reliable, versatile models.

Temperature is a value that controls how predictable or creative an LLM's output is

In large language models, temperature adjusts the randomness in text generation by modifying how often words with lower probabilities are selected next in the sequence. Lower temperatures produce more obvious outputs while higher temperatures produce more creative responses.

Visualize how transformer architecture generates a large language model's outputs by running words and subwords assigned to specific numbers through multiple rounds of mathematical processing.

Model context protocol allows large language models to connect to external data

Using a unified interface, MCP simplifies how AI systems securely connect to external data, tools, and services. The protocol enables AI agents to perform tasks without ongoing human refinement or updates to manually integrate new data.

Generative AI tools excel at pattern recognition, not contextual accuracy

Large language models are trained on vast amounts of unstructured data, from which they develop parameters for grammar and associations between words. These connections can introduce errors due to inapplicable reasoning when used on new data in unfamiliar contexts.

B2 models the interactions of air, water, soil, plants and microbes at the LEO

The Landscape Evolution Observatory at Biosphere 2 examines how factors such as climate change, water flow, weathering and community development impact landscape structures comprising 500 metric tons of crushed rock. Research is also conducted at biomes representing savannas, mangroves, coastal fog deserts, oceans and rainforests.

Find out how important you are to AI tools when they generate text responses with this tool, which estimates the weights of people's names.

League tables show the number of deals top investment banks do in a time period

These tables provide a visual to help keep track of the leading companies in investment banking. Companies such as JPMorgan and Goldman Sachs typically sit up top.

AI scraping for LLM training data has significantly strained Wikipedia's infrastructure

From January 2024 to April 2025, the site's bandwidth increased by 50% as automated bots downloaded terabytes of data for the large language models powering AI tools. The Wikimedia Foundation found that bots accounted for 65% of the highest demand requests (e.g., videos) despite representing just 35% of page views.

Anthropic's Frontier Red Team assesses the dangers of its AI tools through 'evals'—safety tests that probe models to disclose sensitive information.

Why Google Search's ranking system combines filters, signals, and machine learning layers

Rather than static rankings, dynamic tools continually shift search results in real time. These include Twiddlers, which adjust ranking positions based on document traits and search context, and FreshnessNode, which reshuffles top results to prioritize timely informational content.

Breaking down Anthropic's products and why their models are named after literary forms

Since March 2024, the Claude series of large language models includes three varieties—Haiku, Sonnet, and Opus—named to metaphorically illustrate increasing complexity and computing power. Claude Code can perform software engineering tasks for developers and, as of January 2026, writes nearly all of Anthropic's code alongside Opus 4.5.

Linguistics professors believe memes are a language

It's generally agreed upon that languages exhibit at least some of the following characteristics: compositionality, social transmission, arbitrariness of form and meaning, and accomplishment of linguistic functions like asking questions and expressing emotions, among other qualities. Memes fulfill these basic requirements, giving them linguistic legitimacy.

Governments around the world are struggling to measure loneliness

Funding for policy resources often hinges on measuring the scale of a problem and then being able to measure the effectiveness of interventions, requiring data. Yet defining and measuring loneliness at a country-scale remains challenging. Typically, the University of California Los Angeles Loneliness Scale is sometimes used worldwide or adapted to measure loneliness.

There’s an artificial intelligence model trained on bankruptcy law

Although it's designed for lawyers, the tool is able to help everyday browsers synthesize important cases and point users to relevant documents about them. One sample using it asks Chatlaw to explain the significance of a recent case and then to define relevant terminology.

Explore more than 7,000 of the world's languages

Google collects open-source data on languages, detailing how many known speakers each language has, whether its endangered, and where in the world it is spoken.

IBM developed a benchmark to measure the usable performance of a quantum computer

Dubbed "quantum volume," the benchmark tests how well a quantum computer runs complex tasks. It stops when the computer can no longer correctly select the most likely answers, called "heavy outputs," more than two-thirds of the time.

Explore persistent 3D environments with Marble

Marble is a spatial AI system built by World Labs, a San Francisco startup focusing on world models (rather than large language models). Using a text prompt (or a single image or video), users can generate a "world" and move through it, edit it, or export it. The company and project are part of the broader spatial intelligence area of AI, where AI can simulate environments.

A comprehensive state-by-state report of LGBTQ+ rights in America

The State Equality Index (SEI) is a comprehensive state-by-state report that provides a review of statewide laws and policies that affect LGBTQ+ people and their families. The SEI rates all 50 states plus Washington, D.C. in six areas of law. Each...

Tokens—numbers assigned to words and subwords—are the basic units of data that AI tools process to generate outputs that mimic language without understanding the text itself.

A map of the moon, using lidar

When planning to land on the moon, knowing the topography matters. This map of the lunar surface was created by using 6 billion measurements from NASA's Lunar Orbiter Laser Altimeter, a type of lidar. Check out the technically accurate depths of craters and ancient oceans on our beloved lunar neighbor with this fun, impressive map.

Machine learning and deep learning are successive subsets of artificial intelligence

ML allows systems to learn from data, while DL uses complex neural networks to analyze large amounts of unstructured data. Neural networks enable capabilities like image and speech recognition through their use of algorithms to identify patterns in data.

See top-down views of Washington state's geology with cutting-edge lidar

Lidar is a revolutionary tool that creates visual models of landscapes using rapid light pulse measurements. Geologists in Washington state have deployed the technology to study the state's fault lines, landslides, tsunami inundation zones, and much more.

See a list of 2024’s top 10 VC firms

A list of the top 10 VC firms, like Sequoia Capital and Andreessen Horowitz, ranked by assets under management shows how those two firms stack up against their competitors—and what kinds of companies those competitors invest in.

See the UCLA Loneliness Scale, often used to measure global loneliness

This 20-question survey, first established in the late 1970s and subsequently revised and often adapted, aims to measure the subjective experience of loneliness. Respondents rate various items on a scale of 1 (Never) to 4 (Often). One revision of the test (not included here) offers an abbreviated three-question version that focuses on how often you feel companionship, left out, and isolated.

Artificial general intelligence equals or surpasses human intelligence

AGI incorporates sensory perception, memory, and advanced logical inference systems to move beyond the narrow tasks seen in large language models and chatbots. One of the most significant hurdles in developing AGI is designing systems that learn and can flexibly apply learning across domains.

Google Search trends reveal truths about society that traditional surveys miss

Studies of human behavior show that survey respondents may lie to present themselves more favorably. By studying anonymous search data, researchers have uncovered hidden racism, mental health struggles, and interest in taboo or stigmatized topics.

DeepSeek is a Chinese AI startup that rattled US competitors when it made an AI model much faster and cheaper than they did

DeepSeek unveiled an AI model in 2025 that it said was made in two months for roughly $6M. For context, OpenAI has suggested it took over $100M to make its AI model GPT-4. The news rattled the stocks of American tech companies, including leading chipmaker Nvidia.

Visualizing the 75 largest underwater animals

With so many fish in the sea, which are the largest? This infographic shows fantastic illustrations of the 75 largest sea animals, ranked by average size. The detailed visual is organized in descending order of size, starting with the blue whale,...

Experiments involving AI agents operating in virtual societies for 15 days produced diverse outcomes, ranging from every agent dying to the creation of a functioning democracy, depending on the AI model used.

Just as large language models are trained on text to enable generative AI systems to mimic natural language, automated AI robotic systems are trained in virtual environments to help them 'learn' about the properties of reality.

See every brand that LVMH—the world's largest luxury goods holding company—owns, from luxury fashion and jewelry brands like Tiffany & Co., Fendi, and Louis Vuitton to beauty supply store Sephora and the hotel group Belmond.

Sam Altman acknowledges the fears around creativity, copyright, and misuse of AI

In 2022, OpenAI's CEO, Sam Altman, popularized the use of large language models with the release of ChatGPT. He has emphasized the importance of involving society in shaping AI’s safety frameworks before models begin to act independently online.

How Model Context Protocol allows AI systems to communicate with databases

Developed by Anthropic, the open-source software serves as an integration layer that standardizes how large language models connect to real-time information sources or tools. Just as a USB-C port serves as a universal standard for many peripherals, MCP streamlines these integrations and provides context about external sources to LLMs in a way that AI can understand.

Researchers found that by mid-2025, around 35% of newly published websites utilized AI for language generation

The study also found that pages using AI narrow the range of ideas and perspectives and skew more positively, though the measurable impacts on factual accuracy and writing style variation remain unclear.

In 2023, there were nearly as many payloads in LOE as trackable pieces of debris

In 2021, the European Space Agency estimated that more than 130 million space debris objects larger than 1 millimeter were in low Earth orbit, though only about 22,000 were being tracked. As of late 2025, the tracked total exceeds 49,000.

Unlike the models made by large AI companies, open-weight models can have their safeguards removed or weakened, a process that has become increasingly easy and may expose users to harmful information.

View a ranking of research institutions, filterable by region, country, research area, or journal group.

Compare how justice ministries and courts are structured around the world

The Federal Judicial Center's "Judiciaries Worldwide" series provides clear and accessible comparisons of how various countries organize their justice ministries, prosecutors, courts, and judicial councils—emphasizing global models of independence, oversight, and institutional structure.