Claude 5 旗舰三剑客深度横评：Opus 5、Sonnet 5 与 Fable 5 架构机理、Agent 编程与成本级联策略

import anthropic

client = anthropic.Anthropic(
    api_key="sk-gpt345-your-api-key",
    base_url="https://api.gpt345.com"  # 中转端点原生兼容
)

response = client.messages.create(
    model="claude-opus-5",
    max_tokens=4096,
    system=[
        {
            "type": "text",
            "text": "这里是长达 30,000 tokens 的企业微服务架构设计白皮书与安全合规规范...",
            "cache_control": {"type": "ephemeral"} # 显式标记为临时缓存锚点
        }
    ],
    messages=[
        {"role": "user", "content": "根据上述架构白皮书，审计用户鉴权模块的时序图是否存在漏洞？"}
    ]
)

# 打印对账缓存用量
usage = response.usage
print(f"写入缓存 Tokens: {getattr(usage, 'cache_creation_input_tokens', 0)}")
print(f"命中缓存 Tokens: {getattr(usage, 'cache_read_input_tokens', 0)} (享受 90% 折扣!)")

import requests
import json

class ClaudeCascadingEngine:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.gpt345.com/v1"

    def execute_task(self, prompt: str, is_complex_audit: bool = False):
        # 启发式路由：初次尝试或轻量任务默认走 Sonnet 5
        selected_model = "claude-sonnet-5" if not is_complex_audit else "claude-opus-5"
        
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
        
        payload = {
            "model": selected_model,
            "messages": [{"role": "user", "content": prompt}],
            "max_tokens": 2048,
            "temperature": 0.2
        }

        print(f"[调度网关] 正在路由至: {selected_model}")
        resp = requests.post(f"{self.base_url}/chat/completions", json=payload, headers=headers, timeout=60)
        data = resp.json()
        
        content = data["choices"][0]["message"]["content"]
        
        # 质量拦截规则：若 Sonnet 5 识别出边界模糊或显式声明需要更深逻辑推演，自动升配至 Opus 5
        if selected_model == "claude-sonnet-5" and "无法确定潜在边界条件" in content:
            print("[警告] 遇到复杂边界，自动升配触发 Opus 5 深度重新推演...")
            payload["model"] = "claude-opus-5"
            resp = requests.post(f"{self.base_url}/chat/completions", json=payload, headers=headers, timeout=90)
            return resp.json()["choices"][0]["message"]["content"]

        return content

# 实例化引擎
engine = ClaudeCascadingEngine(api_key="sk-gpt345-your-actual-key")
# answer = engine.execute_task("编写一个基础的二分搜索函数")