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  1. openai.com › product › gpt-4GPT-4 | OpenAI

    GPT-4 is a deep learning system that produces safer and more useful responses than previous versions. It can generate, edit, and iterate with users on various writing tasks, and is available on ChatGPT Plus and as an API.

    • Hello GPT-4o

      As measured on traditional benchmarks, GPT-4o achieves GPT-4...

  2. openai.com › research › gpt-4GPT-4 | OpenAI

    Mar 14, 2023 · GPT-4 is a large model that can process image and text inputs and generate text outputs. It exhibits human-level performance on various benchmarks, such as simulated exams, and is more reliable, creative, and aligned than GPT-3.5.

  3. openai.com › index › hello-gpt-4oHello GPT-4o | OpenAI

    May 13, 2024 · GPT-4o is a new model that can reason across audio, vision, and text in real time. It can respond to audio inputs in 232 milliseconds, match GPT-4 Turbo performance on text, and improve on vision and audio understanding.

  4. platform.openai.com › docs › modelsOpenAI Platform

    Mar 14, 2023 · Learn about the latest and most advanced models from OpenAI, including GPT-4 and GPT-4o, which can generate text and images from text or image inputs. Compare their features, prices, and performance across different languages and tasks.

  5. en.wikipedia.org › wiki › GPT-4GPT-4 - Wikipedia

    Generative Pre-trained Transformer 4 ( GPT-4) is a multimodal large language model created by OpenAI, and the fourth in its series of GPT foundation models. [1] . It was launched on March 14, 2023, [1] and made publicly available via the paid chatbot product ChatGPT Plus, via OpenAI's API, and via the free chatbot Microsoft Copilot. [2] .

  6. Mar 16, 2023 · OpenAI unveils GPT-4, a powerful AI model that can generate text, images and code from prompts. Researchers praise its potential, but also worry about its secrecy, safety and bias.

  7. Mar 15, 2023 · A 100-page paper by OpenAI on the development and performance of GPT-4, a large-scale, multimodal model that can process image and text inputs and outputs. The paper reports human-level performance on various benchmarks, such as passing a simulated bar exam, and provides optimization methods for scaling up the model.

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