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AI API Guides

Choose, price and integrate leading model APIs with practical examples for OpenAI, Claude, Gemini, DeepSeek and Qwen.

Go from model shortlist to a working production request.

Learn in the order developers build.

Start with the decision, move into implementation and finish with production checks.

Structured learning path

Choose a model

Match quality, context, modality and latency to the workload.

Understand pricing

Estimate token, cache, tool and request-level costs.

Integrate the API

Use OpenAI-compatible or provider-native SDK patterns.

Ship safely

Add retries, limits, observability and fallback routes.

Build topical authority, not article islands.

Core guides

5 guided starting points

Provider guide01

OpenAI API Guide

Models, Responses API patterns and migration considerations.

Open guide

Provider guide02

Claude API Guide

Messages API, prompt caching and long-context workflows.

Open guide

Provider guide03

Gemini API Guide

Multimodal requests, context and production integration.

Open guide

Provider guide04

DeepSeek API Guide

Reasoning workloads, pricing and OpenAI-compatible calls.

Open guide

Provider guide05

Qwen API Guide

Qwen model selection, parameters and code examples.

Open guide

Launch a multi-model feature

Start with one request contract, test two compatible models and keep the route configurable.

Define the output contract

Benchmark with real prompts

Estimate cost per successful task

Add a fallback before launch

Turn the guide into a request.

Try a model in the Playground, then use the Quickstart to move the same pattern into Python or Node.js.

Try without coding Open Quickstart

Explore every growth topic.

AI API Guides

Go from model shortlist to a working production request.

LLM Infrastructure

Build an AI request layer that survives provider and model changes.

AI Agent

Move from a prompt demo to an observable agent workflow.

Cost Optimization

Lower cost without hiding quality or reliability trade-offs.

Model Comparison

Select a model based on the job, not the leaderboard headline.

Tutorials

Copy a working pattern, understand it and adapt it to your application.

Frequently asked questions

AEO-ready answers

What is an AI model API?

It is a programmatic interface for sending input to a model and receiving generated text, images, audio, video or structured data.

How should developers compare AI APIs?

Use the same real workload and compare task quality, total cost, latency, reliability, modalities, tooling and migration effort.