使用Python调用Claude API的三种方案实测

pip install anthropic openai

import anthropic

client = anthropic.Anthropic(api_key="sk-ant-api03-xxxxx")

message = client.messages.create(
 model="claude-sonnet-4-6-20260414",
 max_tokens=1024,
 messages=[
 {"role": "user", "content": "用 Python 写一个快速排序，要有注释"}
 ]
)

print(message.content[0].text)

anthropic.AuthenticationError: Error code: 401 - {'type': 'error', 'error': {'type': 'authentication_error', 'message': 'invalid x-api-key'}}

with client.messages.stream(
 model="claude-sonnet-4-6-20260414",
 max_tokens=1024,
 messages=[{"role": "user", "content": "解释一下 RLHF 是什么"}]
) as stream:
 for text in stream.text_stream:
 print(text, end="", flush=True)

from openai import OpenAI

client = OpenAI(
 api_key="sk-ant-api03-xxxxx",
 base_url="https://api.ofox.ai/v1"
)

response = client.chat.completions.create(
 model="claude-sonnet-4-6-20260414",
 max_tokens=1024,
 messages=[
 {"role": "system", "content": "你是一个资深 Python 开发者"},
 {"role": "user", "content": "帮我写一个 Redis 缓存装饰器"}
 ]
)

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

stream = client.chat.completions.create(
 model="claude-sonnet-4-6-20260414",
 max_tokens=1024,
 messages=[{"role": "user", "content": "写一篇 500 字的技术博客大纲"}],
 stream=True
)

for chunk in stream:
 if chunk.choices[0].delta.content:
 print(chunk.choices[0].delta.content, end="", flush=True)

import requests
import json

url = "https://api.anthropic.com/v1/messages"
headers = {
 "x-api-key": "sk-ant-api03-xxxxx",
 "anthropic-version": "2023-06-01",
 "content-type": "application/json"
}

payload = {
 "model": "claude-sonnet-4-6-20260414",
 "max_tokens": 1024,
 "messages": [
 {"role": "user", "content": "什么是向量数据库？简单解释"}
 ]
}

response = requests.post(url, headers=headers, json=payload, timeout=30)
result = response.json()
print(result["content"][0]["text"])

{"type":"error","error":{"type":"invalid_request_error","message":"Invalid API version: 2026-01-01. Valid versions: 2023-06-01, 2024-10-22, ..."}}

graph LR
 A[你的 Python 代码] --> B{选择调用方式}
 B -->|方案一| C[Anthropic SDK]
 B -->|方案二| D[OpenAI SDK + 聚合网关]
 B -->|方案三| E[HTTP requests]
 C --> F[Anthropic API]
 D --> G[聚合网关 API]
 G --> F
 E --> F
 F --> H[Claude Sonnet 4.6 / Opus 4.7]

message = client.messages.create(
 model="claude-sonnet-4-6-20260414",
 max_tokens=1024,
 messages=[{
 "role": "user",
 "content": [
 {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": base64_string}},
 {"type": "text", "text": "这张图里画的是什么？"}
 ]
 }]
)

anthropic.RateLimitError: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of request tokens has exceeded your per-minute rate limit'}}

from tenacity import retry, wait_exponential, stop_after_attempt

@retry(wait=wait_exponential(min=1, max=60), stop=stop_after_attempt(5))
def call_claude(prompt):
 return client.messages.create(
 model="claude-sonnet-4-6-20260414",
 max_tokens=1024,
 messages=[{"role": "user", "content": prompt}]
 )