OpenAI

private

usa · Founded 2015 · Valuation: $852B as of 2026-03

OpenAI was founded in December 2015 as a nonprofit AI research lab by Sam Altman, Ilya Sutskever, Greg Brockman, and others. It restructured to a capped-profit entity in 2019 and completed conversion to a Public Benefit Corporation in October 2025, with Microsoft holding ~27% equity. In March 2026 OpenAI raised $122B at an $852B valuation — the largest private funding round in history — from SoftBank, Amazon, NVIDIA, and others. Revenue reached ~$2B/month by early 2026 with 900M+ weekly ChatGPT users.

OpenAI's GPT series defined the modern LLM era: GPT-2 (2019, 1.5B) demonstrated emergent text generation, GPT-3 (2020, 175B) introduced few-shot learning, and GPT-4 (2023) set the frontier for multimodal reasoning. The o-series reasoning models (o1, o3) pioneered test-time compute scaling, and GPT-5.4 (2026, AA index 57, 1M context) is the current flagship. Beyond language, OpenAI created DALL-E (text-to-image), Whisper (speech recognition), Codex (powering GitHub Copilot), and Sora (video generation).

The lab's foundational technique papers reshaped the entire field: PPO (2017) became the default RL algorithm for alignment, Scaling Laws (2020) established power-law relationships driving compute investment, CLIP (2021) enabled text-image alignment used by virtually all image generation models, and InstructGPT/RLHF (2022) created the SFT → Reward Model → PPO pipeline now standard across the industry. OpenAI also released influential open-source tools including Gym (37K stars) and Triton (19K stars).

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Outputs (26)

GPT-5.5

model

OpenAI's most capable model. AA Intelligence Index: 60 (#1 globally). Described as "a new class of intelligence" that understands intent faster and ca…

GPT-5.4

model

Current flagship model. 1M token context. Knowledge cutoff August 31, 2025. Available in thinking, pro, mini, and nano variants. Architecture and para…

GDPval

eval

1,320 real-world professional tasks across 44 occupations in the 9 largest US GDP-contributing industries (real estate, government, manufacturing, hea…

GPT-5

model

Multi-component system combining a fast model, reasoning model, and router into a single interface. 400K token context. Replaced GPT-4o, o3, o4-mini,…

o3

model

Second-generation reasoning model, advancing o1's test-time compute scaling. 200K token context. Released alongside o4-mini, a smaller reasoning varia…

SimpleQA

eval

4,326 short-form factuality questions adversarially collected against GPT-4 to measure factual recall and calibration. Questions are designed to have…

MLE-bench

eval

75 ML engineering competitions from Kaggle testing whether AI agents can train models, prepare datasets, and run experiments autonomously. Each task i…

o1

model

OpenAI's first reasoning model, trained with reinforcement learning to perform extended chain-of-thought reasoning before answering. Uses hidden "thin…

GPT-4o

model

GPT-4o ("omni") — OpenAI's first natively multimodal model, processing text, audio, image, and video inputs and generating text and audio output…

Sora

model

Text-to-video generation model using a diffusion transformer operating on spacetime patches. Generates up to 1080p video, up to 20 seconds long, from…

DALL-E 3

model

"Improving Image Generation with Better Captions" — the third generation of DALL-E, integrated natively into ChatGPT. Key innovation is a captio…

Let's Verify Step by Step

paper

Demonstrated that process reward models (PRMs) that evaluate each step of a reasoning chain significantly outperform outcome reward models (ORMs) that…

GPT-4

model

GPT-4 technical report. Architecture details undisclosed by OpenAI (parameter count, training data, and hardware not published). 8K token context at l…

Whisper

model

"Robust Speech Recognition via Large-Scale Weak Supervision" — an encoder-decoder Transformer trained on 680,000 hours of multilingual audio dat…

InstructGPT (RLHF)

paper

"Training language models to follow instructions with human feedback" — introduced the three-stage RLHF pipeline that became industry standard:…

Codex

model

"Evaluating Large Language Models Trained on Code" — GPT-3 fine-tuned on publicly available code from GitHub. The largest variant, Codex-12B, so…

Triton

library

Open-source GPU programming language and compiler for writing high-performance neural network kernels. Python-like syntax that compiles to optimized G…

HumanEval

eval

164 hand-written Python programming problems with function signatures, docstrings, and unit tests. Each problem tests language comprehension, algorith…

CLIP

paper

"Learning Transferable Visual Models From Natural Language Supervision" — Contrastive Language-Image Pre-training (CLIP) trained on 400M image-t…

DALL-E

model

"Zero-Shot Text-to-Image Generation" — a 12B parameter autoregressive Transformer that generates images from text descriptions. Uses a discrete…

GPT-3

model

"Language Models are Few-Shot Learners" — 175B parameter dense Transformer (96 layers, 12288 hidden, 96 heads) trained on 300B tokens from a fil…

Scaling Laws for Neural Language Models

paper

Established power-law scaling relationships between language model performance and three key factors: model size (N), dataset size (D), and compute bu…

GPT-2

model

"Language Models are Unsupervised Multitask Learners" — 1.5B parameter decoder-only Transformer (48 layers, 1600 hidden, 25 heads) trained on We…

GPT-1

model

"Improving Language Understanding by Generative Pre-Training" — the paper that launched the GPT series. A 117M parameter decoder-only Transforme…

Proximal Policy Optimization (PPO)

paper

Introduced Proximal Policy Optimization, a family of policy gradient methods that alternate between sampling data through interaction with the environ…

OpenAI Gym

library

Toolkit for developing and comparing reinforcement learning algorithms. Provides a standard API for RL environments (CartPole, Atari, MuJoCo, etc.) th…