1. Overview of OpenAI Models

OpenAI has released a series of artificial intelligence models aimed at handling various levels of complexity in problem-solving. From the GPT series for understanding and generating natural language or code, to DALL·E for creating and editing images, as well as TTS and Whisper for transforming text and speech, these models each have their own strengths and cover a wide range of application scenarios.

  • GPT-4 and GPT-4 Turbo: Representing the latest in natural language processing technology, capable of accurately performing complex tasks and providing deep understanding of natural language.
  • GPT-3.5: Further improvement upon GPT-3, emphasizing high cost-effectiveness while possessing powerful natural language and code generation capabilities.
  • DALL·E: Utilizes advanced deep learning techniques to create lifelike images.
  • TTS: Transforms text into speech, suitable for various applications seeking speech output.
  • Whisper: A versatile speech recognition and translation model (speech to text), supporting multiple languages.
  • Embeddings: Converts text into numerical representations, widely used in search, clustering, recommendation systems, and more.
  • Moderation: Capable of detecting sensitive content in text, aiding in compliance with usage policies.

OpenAI's models are regularly upgraded according to different needs and provide stable old versions for developers to ensure application consistency.

2. GPT-4 and GPT-4 Turbo

GPT-4 is a large multimodal model that not only accepts text input but also processes input from images and outputs text. GPT-4 excels in a wide range of common knowledge and deep reasoning, with higher accuracy than any previous model.

GPT-4 Turbo has made improvements in handling "lazy" behaviors, i.e., when the model fails to complete a task. Additionally, GPT-4 supports more advanced features such as:

  • Enhanced instruction following capability
  • JSON mode
  • Reproducible outputs
  • Parallel function calls

For applications requiring processing of large amounts of data and complex instructions, GPT-4 provides a huge context window of 128,000 tokens, giving it a natural advantage in processing long coherent texts.

3. GPT-3.5 Model

The GPT-3.5 model is a significantly cost-effective model with the ability to understand and generate natural language or code. GPT-3.5 Turbo is an optimized version of GPT-3.5, specially designed for chatting optimization, while also performing well in traditional task completion.

For most fundamental tasks, the difference between GPT-4 and GPT-3.5 models is not significant. However, in more complex reasoning scenarios, the capabilities of GPT-4 far exceed those of GPT-3.5 and its predecessors.

4. DALL·E Image Generation Model

DALL·E is another innovative technology by OpenAI that can generate realistic images based on natural language descriptions. For example, users can ask it to create "an octopus wearing a spacesuit," and DALL·E will generate an image that matches the description.

5. Text-to-Speech (TTS) Models

Text-to-Speech (TTS) is a technology that converts text information into spoken language, with significant applications in various scenarios such as aiding visually impaired individuals with reading, enabling intelligent assistant responses, and automatic voice notifications.

OpenAI offers two variants of TTS models — tts-1 and tts-1-hd. Among them, tts-1 is optimized for real-time text-to-speech scenarios, with faster speed, while tts-1-hd is optimized for higher quality and is more suitable for scenarios with high demands for sound quality.

6. Whisper Speech Recognition Model

Whisper is a versatile speech recognition model (speech-to-text) trained to recognize speech in multiple languages, with capabilities for speech translation and language recognition. Whisper is trained using large-scale diverse speech datasets to achieve wide-ranging applications.

Whisper Model Features

Whisper can recognize speech in multiple languages and has the following capabilities:

  1. High-accuracy speech recognition.
  2. Support for speech translation in multiple languages.
  3. Language recognition capabilities.

7. Embeddings Text Embedding Model

The text embedding model can convert text into numerical vector forms, enabling the computation of the correlation between texts. It is widely used in search, clustering, recommendation systems, anomaly detection, and classification tasks, among others.

8. Moderation Content Review Model

The content review model can check whether content complies with OpenAI's usage policies, automatically identifying various sensitive content types, and assisting in maintaining community standards.