OpenAI API

Why It Matters

The OpenAI API set the standard for LLM access. When ChatGPT demonstrated what large language models could do, the API made those capabilities programmable. Most AI applications today either use OpenAI directly or use frameworks designed around OpenAI’s API patterns.

The API’s design influences the entire ecosystem. Concepts like system messages, temperature, max tokens, and function calling originated or were popularized by OpenAI. Competitor APIs (Claude, Gemini, open-source model APIs) often mirror these conventions, making skills transferable across providers.

For AI engineers, understanding the OpenAI API is foundational. Even if you use other providers or open-source models, you’ll encounter OpenAI’s patterns in tutorials, documentation, and frameworks. The API represents the baseline implementation that others build upon.

Current Models (2026)

GPT-5 is OpenAI’s flagship model with 400K+ context window, representing the most capable text generation model. Use for complex reasoning, creative tasks, and production applications requiring the highest quality.

o3 is the reasoning-focused model designed for complex problem-solving, math, and code. It uses extended thinking time to work through multi-step problems. Best for tasks requiring careful analysis rather than quick responses.

o4-mini provides o-series reasoning capabilities at lower cost, making it practical for applications needing reasoning at scale without GPT-5 level complexity.

GPT-4o remains available for applications needing the previous-generation capabilities, and GPT-3.5-turbo serves high-volume, cost-sensitive use cases.

Implementation Basics

The OpenAI API is organized around several key endpoints:

Chat Completions is the primary endpoint for GPT-5, o3, o4-mini and other models. Send an array of messages (system, user, assistant) and receive a generated response. Parameters control randomness (temperature), length (max_tokens), and sampling (top_p).

Embeddings convert text to vectors for similarity search and RAG. The text-embedding-3 models offer different dimension options trading accuracy for speed. Use embeddings for semantic search, clustering, and classification.

Function calling lets the model output structured data matching your schema. Define functions with JSON Schema, and the model will decide when to call them and with what arguments. This enables structured outputs and tool use.

Audio endpoints handle speech-to-text (Whisper) and text-to-speech. Transcribe meetings, generate voice responses, or build audio-based interfaces.

Responses API is OpenAI’s newer interface for agentic applications, supporting stateful conversations and tool use with built-in function execution. Consider this for complex agent workflows.

Start with the Python SDK for the easiest integration. Set your API key as an environment variable. Begin with chat completions using basic parameters, then add function calling for structured outputs. Use tiktoken to estimate token counts before sending requests to avoid surprises with billing.