GPT-4 Powers a New Generation of Applications

We’ve created GPT‑4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT‑4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT‑3.5’s score was around the bottom 10%. We’ve spent 6 months iteratively aligning ⁠ GPT‑4 using lessons from our adversarial testing program as well as ChatGPT, resulting in our best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.

Over the past two years, we rebuilt our entire deep learning stack and, together with Azure, co-designed a supercomputer from the ground up for our workload. A year ago, we trained GPT‑3.5 as a first “test run” of the system. We found and fixed some bugs and improved our theoretical foundations. As a result, our GPT‑4 training run was (for us at least!) unprecedentedly stable, becoming our first large model whose training performance we were able to accurately predict ahead of time. As we continue to focus on reliable scaling, we aim to hone our methodology to help us predict and prepare for future capabilities increasingly far in advance—something we view as critical for safety.

We are releasing GPT‑4’s text input capability via ChatGPT and the API (with a waitlist ⁠ ). To prepare the image input capability for wider availability, we’re collaborating closely with a single partner ⁠ (opens in a new window) to start. We’re also open-sourcing OpenAI Evals ⁠ (opens in a new window) , our framework for automated evaluation of AI model performance, to allow anyone to report shortcomings in our models to help guide further improvements.

So when prompted with a question, the base model can respond in a wide variety of ways that might be far from a user’s intent. To align it with the user’s intent within guardrails, we fine-tune the model’s behavior using reinforcement learning with human feedback ( RLHF ⁠ ).

We’ve also been using GPT‑4 internally, with great impact on functions like support, sales, content moderation, and programming. We also are using it to assist humans in evaluating AI outputs, starting the second phase in our alignment strategy ⁠ .

In a casual conversation, the distinction between GPT‑3.5 and GPT‑4 can be subtle. The difference comes out when the complexity of the task reaches a sufficient threshold—GPT‑4 is more reliable, creative, and able to handle much more nuanced instructions than GPT‑3.5.

To understand the difference between the two models, we tested on a variety of benchmarks, including simulating exams that were originally designed for humans. We proceeded by using the most recent publicly-available tests (in the case of the Olympiads and AP free response questions) or by purchasing 2022–2023 editions of practice exams. We did no specific training for these exams. A minority of the problems in the exams were seen by the model during training, but we believe the results to be representative—see our technical report ⁠ (opens in a new window) for details.

We also evaluated GPT‑4 on traditional benchmarks designed for machine learning models. GPT‑4 considerably outperforms existing large language models, alongside most state-of-the-art (SOTA) models which may include benchmark-specific crafting or additional training protocols: