OpenAI, Anthropic, DeepSeek API: A Guide to Integrating AI Models

## Introduction Integrating artificial intelligence into applications is no longer the prerogative of giants — today, any developer can connect a powerful language model via API. The three market leaders — OpenAI, Anthropic, and DeepSeek — offer different approaches to authentication, streaming, and system prompt management. In this guide, we'll break down the practical nuances of working with each API, compare pricing, and provide code templates for a quick start. ## Authentication and Basic Requests ### OpenAI API OpenAI uses a Bearer token in the `Authorization` header. You can obtain a key in your personal account on platform.openai.com. Example in Python: ```python import openai openai.api_key = "sk-..." response = openai.ChatCompletion.create( model="gpt-4o", messages=[{"role": "user", "content": "Hello!"}] ) print(response.choices[0].message.content) ``` ### Anthropic API Anthropic (Claude) requires specifying `x-api-key` in headers and the API version. Important: Anthropic uses the `anthropic-version` format in requests. Example: ```python import requests headers = { "x-api-key": "sk-ant-...", "anthropic-version": "2023-06-01" } data = { "model": "claude-3-opus-20240229", "messages": [{"role": "user", "content": "Hi"}] } response = requests.post("https://api.anthropic.com/v1/messages", json=data, headers=headers) print(response.json()["content"][0]["text"]) ``` ### DeepSeek API DeepSeek offers simpler authentication — via `Authorization: Bearer `. Endpoint: `https://api.deepseek.com/v1/chat/completions`. DeepSeek supports the DeepSeek-V2 model, known for its low cost and high inference speed. ## Real-Time Response Streaming ### OpenAI OpenAI supports `stream=True`: ```python stream = openai.ChatCompletion.create( model="gpt-4o", messages=[{"role": "user", "content": "Write a short poem"}], stream=True ) for chunk in stream: if chunk.choices[0].delta.get("content"): print(chunk.choices[0].delta.content, end="") ``` ### Anthropic Anthropic implements streaming via Server-Sent Events (SSE): ```python import requests headers = { "x-api-key": "sk-ant-...", "anthropic-version": "2023-06-01" } data = { "model": "claude-3-haiku-20240307", "messages": [{"role": "user", "content": "Tell me about yourself"}], "stream": True } with requests.post("https://api.anthropic.com/v1/messages", json=data, headers=headers, stream=True) as r: for line in r.iter_lines(): if line: print(line.decode()) ``` ### DeepSeek DeepSeek also supports streaming via SSE, similar to OpenAI: ```python import requests headers = {"Authorization": "Bearer "} data = { "model": "deepseek-chat", "messages": [{"role": "user", "content": "Come up with a startup idea"}], "stream": True } with requests.post("https://api.deepseek.com/v1/chat/completions", json=data, headers=headers, stream=True) as r: for line in r.iter_lines(): if line: print(line.decode()) ``` ## System Prompts: Configuring Model Behavior A system prompt is an instruction that sets the style and constraints of the response. All three providers handle it differently: | Provider | Field for System Prompt | Example | |----------|-------------------------|---------| | OpenAI | `messages[0]` with role `"system"` | `{"role": "system", "content": "You are an experienced copywriter"}` | | Anthropic | `system` (outside the messages array) | `"system": "You are a programming assistant"` | | DeepSeek | `messages[0]` with role `"system"` | `{"role": "system", "content": "Answer briefly"}` | ### Practical Tip Use system prompts to control tone, response length, and avoid sensitive topics. For example, for a tech support chatbot: "Answer politely, do not give medical advice, answer only in Russian." ## Pricing and Limits Cost comparison (data as of June 2026): | Model | Input (per 1M tokens) | Output (per 1M tokens) | Speed

OpenAI, Anthropic, DeepSeek API: A Guide to Integrating AI Models

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