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OpenAI
Frontier LLM APIs — GPT-4o, o1, embeddings, vision, and DALL-E.
0Licensed
LLM Platforms
Overview
OpenAI provides frontier AI models including GPT-4o, o1, and DALL-E via a REST API, enabling developers to build AI-powered applications at scale.
Available Models
— may change as new versions release| Model | Context | Best For | Cost Tier |
|---|---|---|---|
| GPT-4.1 | 1M | Long-context reasoning, large codebases | High $$$ |
| GPT-4o | 128K | Multimodal tasks, vision, general reasoning | Mid $$ |
| GPT-4o mini | 128K | Fast, cost-efficient everyday tasks | Low $ |
| o3 | 200K | Deep reasoning, math, science, hard coding | Premium $$$$ |
| o4-mini | 200K | Fast chain-of-thought, agentic workflows | Mid $$ |
| o1 | 200K | Complex reasoning with extended thinking | High $$$ |
Key Features
- GPT-4o and o1 reasoning models via API
- Structured outputs with JSON schema enforcement
- Function calling for tool use in agents
- Embeddings API for semantic search
- Vision API — analyze images in prompts
- Fine-tuning on custom datasets
- Assistants API with code interpreter and file search
Real-World Workflows
Build a RAG-powered chatbot
- 1Chunk and embed your documents using the Embeddings API
- 2Store vectors in a vector database like Pinecone or Chroma
- 3At query time, retrieve top-k relevant chunks via similarity search
- 4Pass chunks as context to GPT-4o and return a grounded answer
Structured data extraction from unstructured text
- 1Define a JSON schema for the fields you want to extract
- 2Call GPT-4o with response_format: json_schema
- 3Parse the guaranteed-valid JSON response directly
- 4Integrate into your data pipeline or database
Getting Started
pip install openai
from openai import OpenAI
client = OpenAI() # uses OPENAI_API_KEY env var
response = client.chat.completions.create(
model='gpt-4o',
messages=[{'role': 'user', 'content': 'Hello!'}]
)
print(response.choices[0].message.content)Compare Alternatives
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