OpenAI has models for many use cases, including natural language understanding, code generation, image creation, etc. Here’s a quick rundown:
1. GPT Models (Language Models):
These are the state of the art large language models for text tasks.
- GPT-4: The most advanced, for complex problem solving, long form content and nuanced conversations.
- GPT-3.5: A great predecessor, for general text tasks like summarization, Q&A and conversational support.
2. Codex Models:
For code understanding and generation.
- Codex (e.g., GPT-4 Turbo Codex): Powers GitHub Copilot and can write, debug and explain code in many programming languages. Supports Python, JavaScript, C++ and more.
3. DALL·E Models (Image Generation):
For generating images from text.
- DALL·E 2: Can generate high quality images with inpainting (editing parts of an image) and customization of visuals based on detailed prompts.
4. Whisper Models (Speech Recognition):
For transcribing and understanding speech.
- Whisper: Transcribes and translates audio into multiple languages. Great for captions, meeting notes and accessibility.
5. Embedding Models:
For semantic understanding and clustering of text.
- Text Embedding Models: Generate text vectors for search, recommendation and classification.
6. Fine-tuned Models:
Custom versions of GPT or other base models trained on specific datasets for niche applications like customer support, medical Q&A or legal analysis.
7. API Tools Integration:
OpenAI models can be integrated with external tools and systems to do hybrid tasks like web browsing or image interpretation (via plugins or APIs).
