OpenAI has released a variety of products and applications since its founding in 2015.

Reinforcement learning At its beginning, OpenAI's research included many projects focused on reinforcement learning (RL). OpenAI has been viewed as an important competitor to DeepMind.

Gym Announced in 2016, Gym was an open-source Python library designed to facilitate the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research, making published research more easily reproducible while providing users with a simple interface for interacting with these environments. In 2022, new developments of Gym have been moved to the library Gymnasium.

Gym Retro Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research on video games using RL algorithms and study generalization. Prior RL research focused mainly on optimizing agents to solve single tasks. Gym Retro gives the ability to generalize between games with similar concepts but different appearances.

RoboSumo Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially lack knowledge of how to even walk, but are given the goals of learning to move and to push the opposing agent out of the ring. Through this adversarial learning process, the agents learn how to adapt to changing conditions. When an agent is then removed from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had learned how to balance in a generalized way. OpenAI's Igor Mordatch argued that competition between agents could create an intelligence "arms race" that could increase an agent's ability to function even outside the context of the competition.

OpenAI Five

OpenAI Five is a team of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that learn to play against human players at a high skill level entirely through trial-and-error algorithms. Before becoming a team of five, the first public demonstration occurred at The International 2017, the annual premiere championship tournament for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live one-on-one matchup. After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of real time, and that the learning software was a step in the direction of creating software that can handle complex tasks like a surgeon. The system uses a form of reinforcement learning, as the bots learn over time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map objectives.

By June 2018, the ability of the bots expanded to play together as a full team of five, and they were able to defeat teams of amateur and semi-professional players. At The International 2018, OpenAI Five played in two exhibition matches against professional players, but ended up losing both games. In April 2019, OpenAI Five defeated OG, the reigning world champions of the game at the time, 2:0 in a live exhibition match in San Francisco. The bots' final public appearance came later that month, where they played in 42,729 total games in a four-day open online competition, winning 99.4% of those games.

OpenAI Five's mechanisms in Dota 2's bot player show the challenges of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated the use of deep reinforcement learning (DRL) agents to achieve superhuman competence in Dota 2 matches.

Dactyl Developed in 2018, Dactyl uses machine learning to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. It learns entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation problem by using domain randomization, a simulation approach which exposes the learner to a variety of experiences rather than trying to fit to reality. The setup for Dactyl, aside from having motion tracking cameras, also has RGB cameras to allow the robot to manipulate an arbitrary object by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism.

In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of generating progressively more difficult environments. ADR differs from manual domain randomization by not needing a human to specify randomization ranges.

API In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new AI models developed by OpenAI" to let developers call on it for "any English language AI task".

AgentKit On October 6, 2025, Sam Altman announced OpenAI's new AgentKit at the 2025 Dev Day opening keynote. AgentKit is a new integrated suite of tools for building, deploying and optimizing AI agents.

According to OpenAI, AgentKit builds upon its Responses API released in March, offering a more streamlined approach to agent creation. Several early adopters report significant time savings and efficiency gains when using the new agentic tools. +CapabilityDescriptionAgent BuilderA visual canvas for creating and versioning multi-agent workflowsChatKitA toolkit for embedding customizable chat-based agent experiences in your productConnector RegistryCentral admin panel for managing data sources across OpenAI productsEnhanced EvalsNew Evals capabilities, including datasets, trace grading, automated prompt optimization and third-party model supportGuardrailsCustom guardrail configurations Reinforcement Fine-TuningAbility to customize OpenAI's reasoning models, including custom tool calls and custom graders

Text generation OpenAI debuted its GPT series of large language models (LLMs) in 2018, and has since released multiple versions of its models. GPT is an acronym for generative pre-trained transformer, a term coined by OpenAI researchers that refers to a type of LLM that is not exclusively developed by OpenAI.

+ OpenAI's GPT-n seriesModelArchitectureParameter countTraining dataRelease dateTraining costActive parametersGPT-112-level, 12-headed transformer decoder (no encoder), followed by linear-softmax117 millionBookCorpus: 4.5 GB of text, from 7,000 unpublished books of various genres30 days on 8 P600 graphics cards, or 1 petaFLOPS-day117 millionGPT-2GPT-1, with modified normalization1.5 billionWebText: 40 GB of text, 8 million documents, from 45 million webpages upvoted on Reddit (initial limited version) and (full version)"tens of petaFLOPS-days", or 1.5 × 1021 FLOPS1.5 billionGPT-3GPT-2, with modifications to allow larger scaling175 billion 499 billion tokens consisting of CommonCrawl (570 GB), WebText, English Wikipedia, and two books corpora (Books1 and Books2)3640 petaFLOPS-days (Table D.1), or 3.1 × 1023 FLOPS175 billionGPT-3.5Undisclosed175 billionUndisclosedMarch 15, 2022UndisclosedGPT-4Also trained with both text prediction and RLHF; accepts both text and images as input. Further details are not public.UndisclosedGPT-4oo1o3GPT-4.5GPT-4.1GPT-5August 7, 2025GPT-5.6 SolGPT-5.6 TerraGPT-5.6 LunaGPT-5.6 CyberGPT-6 AstraGPT-6.1 Sol???September 29, 2026?

OpenAI's original GPT model ("GPT-1")

The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and published as a preprint on OpenAI's website on June 11, 2018. It showed how a generative model of language could acquire world knowledge and process long-range dependencies by pre-training on a diverse corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative versions initially released to the public. The full version of GPT-2 was not immediately released due to concerns about potential misuse, including applications for writing fake news. Some experts expressed skepticism that GPT-2 posed a significant threat.

In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural fake news". Other researchers, such as Jeremy Howard, warned of "the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". In November 2019, OpenAI released the complete version of the GPT-2 language model. Several websites host interactive demonstrations of different instances of GPT-2 and other transformer models.

GPT-2's authors argue that unsupervised language models are general-purpose learners, illustrated by GPT-2 achieving state-of-the-art accuracy and perplexity on 7 of 8 zero-shot tasks (i.e., the model was not further trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by using byte pair encoding. This permits representing any string of characters by encoding both individual characters and multiple-character tokens.

GPT-3

First described in May 2020, Generative Pre-trained Transformer 3 (GPT-3) is an unsupervised transformer language model and the successor to GPT-2. OpenAI stated that the full version of GPT-3 contained 175 billion parameters, two orders of magnitude larger than the 1.5 billion in the full version of GPT-2 (although GPT-3 models with as few as 125 million parameters were also trained).

OpenAI stated that GPT-3 succeeded at certain "meta-learning" tasks and could generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German.

GPT-3 dramatically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or encountering the fundamental capability limitations of predictive language models. Pre-training GPT-3 required several thousand petaflop/s-days of compute, compared to tens of petaflop/s-days for the full GPT-2 model. Like its predecessor, the GPT-3 trained model was not immediately released to the public for concerns of possible abuse, although OpenAI planned to allow access through a paid cloud API after a two-month free private beta that began in June 2020.

On September 23, 2020, GPT-3 was licensed exclusively to Microsoft.

Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has additionally been trained on code from 54 million GitHub repositories, and is the AI powering the code autocompletion tool GitHub Copilot. In August 2021, an API was released in private beta. According to OpenAI, the model can create working code in over a dozen programming languages, most effectively in Python.

Several issues with glitches, design flaws and security vulnerabilities were cited.

OpenAI announced that they would discontinue support for the Codex API on March 23, 2023.

GPT-4

On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. They announced that the updated technology passed a simulated law school bar exam with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, analyze or generate up to 25,000 words of text, and write code in all major programming languages.