Openai

Prompt caching is the single highest-ROI optimization available for production LLM applications. If you run 10,000 requests per day with an 8K-token cached system prompt on Anthropic Claude, you save roughly $576/month — with a few lines of code change. OpenAI’s automatic caching requires zero code changes and gives you a 50% discount on repeated input tokens. Anthropic’s explicit caching offers up to 90% savings. This guide covers both, plus Gemini, with production code examples, real cost numbers, and the anti-patterns that silently destroy your cache hit rate. ...

The OpenAI Responses API is the new primary interface for building stateful, agentic AI applications — replacing the Assistants API (being sunset H1 2026) and extending beyond what Chat Completions can do. This tutorial walks through everything from your first API call to building multi-step agents with built-in tools like web search and file retrieval. What Is the OpenAI Responses API? The OpenAI Responses API is a stateful, tool-native interface for building AI agents and multi-turn applications — launched in March 2025 as OpenAI’s replacement for the Assistants API and a significant evolution beyond Chat Completions. Unlike Chat Completions, which is stateless (every request requires you to resend the full conversation history), Responses API maintains conversation state server-side using previous_response_id. A 10-turn conversation with Chat Completions resends your entire history on turn 10, making it up to 5x more expensive for long dialogues. Responses API sends only the new message each turn — the server already holds context. Built-in tools (web search at $25–50/1K queries, file search at $2.50/1K queries) are first-class citizens rather than custom function definitions, and reasoning tokens from o3 and o4-mini are preserved between turns instead of being discarded. OpenAI has moved all example code in the openai-python repository to Responses API patterns — it is where the platform is going. ...