Claude Model API Guide

from openai import OpenAI

client = OpenAI(
    api_key="your-apiyi-key",
    base_url="https://api.apiyi.com/v1"
)

response = client.chat.completions.create(
    model="claude-opus-4-5-20251101",
    messages=[
        {
            "role": "user",
            "content": "Implement a quicksort algorithm in Python and explain its time complexity."
        }
    ],
    # Optional: Set reasoning depth (Opus 4.5 exclusive)
    extra_body={
        "anthropic_effort": "medium"  # low/medium/high
    }
)

print(response.choices[0].message.content)

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'your-apiyi-key',
  baseURL: 'https://api.apiyi.com/v1'
});

const response = await client.chat.completions.create({
  model: 'claude-sonnet-4-5-20250929',
  messages: [
    {
      role: 'user',
      content: 'Analyze the performance bottlenecks in this code...'
    }
  ]
});

console.log(response.choices[0].message.content);

curl https://api.apiyi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-apiyi-key" \
  -d '{
    "model": "claude-haiku-4-5-20251001",
    "messages": [
      {
        "role": "user",
        "content": "Hello, introduce yourself."
      }
    ]
  }'

import anthropic

client = anthropic.Anthropic(
    api_key="your-apiyi-key",
    base_url="https://api.apiyi.com"
)

message = client.messages.create(
    model="claude-opus-4-5-20251101",
    max_tokens=8192,
    messages=[
        {
            "role": "user",
            "content": "Design a highly available microservices architecture and explain the core design principles."
        }
    ],
    # Opus 4.5 exclusive: Set reasoning depth
    extra_headers={
        "anthropic-effort": "high"  # low/medium/high
    }
)

print(message.content[0].text)

import anthropic

client = anthropic.Anthropic(
    api_key="your-apiyi-key",
    base_url="https://api.apiyi.com"
)

with client.messages.stream(
    model="claude-sonnet-4-5-20250929",
    max_tokens=4096,
    messages=[
        {"role": "user", "content": "Write an article about AI development trends..."}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

import anthropic
import base64

client = anthropic.Anthropic(
    api_key="your-apiyi-key",
    base_url="https://api.apiyi.com"
)

# Read and encode image to base64
with open("screenshot.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode('utf-8')

message = client.messages.create(
    model="claude-opus-4-5-20251101",
    max_tokens=4096,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/png",
                        "data": image_data
                    }
                },
                {
                    "type": "text",
                    "text": "This is a webpage screenshot. Please implement the same UI using React + Tailwind CSS."
                }
            ]
        }
    ]
)

print(message.content[0].text)

{
  "model": "claude-opus-4-5-20251101",
  "apiKey": "sk-xxxxxxxxxxxxxxxx",
  "baseURL": "https://api.apiyi.com",
  "maxTokens": 8192,
  "temperature": 0.7
}

response = client.chat.completions.create(
    model="claude-opus-4-5-20251101",
    messages=[{"role": "user", "content": "Design a distributed caching system..."}],
    extra_body={
        "anthropic_effort": "high"  # low/medium/high
    }
)

message = client.messages.create(
    model="claude-opus-4-5-20251101",
    messages=[{"role": "user", "content": "Optimize this SQL query..."}],
    extra_headers={
        "anthropic-effort": "medium"
    }
)

# Recommended: Clear task description
prompt = """
Task: Refactor the following Python code
Requirements:
1. Improve performance (reduce time complexity from O(n²) to O(n log n))
2. Add error handling and type hints
3. Add unit tests

Code:
[paste code]
"""

# Not recommended: Vague instructions
prompt = "Help me fix this code"

# Example: Analyze entire codebase
files_content = ""
for file in ["main.py", "utils.py", "models.py"]:
    with open(file) as f:
        files_content += f"\n\n# {file}\n{f.read()}"

response = client.chat.completions.create(
    model="claude-opus-4-5-20251101",
    messages=[{
        "role": "user",
        "content": f"Analyze the architecture of this codebase and suggest optimizations:\n{files_content}"
    }]
)