Claude Opus 4.7/4.8 深度解析：Tokenizer 变革、Super-Agent 基准与动态工作流编排指南

#!/usr/bin/env python3
"""
Claude Opus 4.7 Token 成本分析工具
基于 Anthropic Token Counting API 实现
"""
import json
import os
from typing import Optional

try:
    from anthropic import Anthropic
except ImportError:
    print("请先安装: pip install anthropic")
    exit(1)


class TokenCostAnalyzer:
    """Token 计数与成本分析器"""
    
    # Claude 模型定价（美元/百万 Token）
    PRICING = {
        "claude-opus-4-6": {"input": 5.0, "output": 25.0},
        "claude-opus-4-7": {"input": 5.0, "output": 25.0},
        "claude-sonnet-4-6": {"input": 3.0, "output": 15.0},
        "claude-haiku-4-5": {"input": 0.8, "output": 4.0},
    }
    
    def __init__(self, api_key: Optional[str] = None):
        self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY")
        if not self.api_key:
            raise ValueError("需要设置 ANTHROPIC_API_KEY 环境变量")
        self.client = Anthropic(api_key=self.api_key)
    
    def count_tokens(self, text: str, model: str = "claude-opus-4-7") -> int:
        """使用 Anthropic API 计算 Token 数量"""
        response = self.client.messages.count_tokens(
            model=model,
            messages=[{"role": "user", "content": text}]
        )
        return response.input_tokens
    
    def count_multi_model(self, text: str) -> dict:
        """在多个模型上对比 Token 计数"""
        results = {}
        for model in ["claude-opus-4-6", "claude-opus-4-7", 
                      "claude-sonnet-4-6", "claude-haiku-4-5"]:
            try:
                tokens = self.count_tokens(text, model)
                results[model] = tokens
            except Exception as e:
                results[model] = f"Error: {e}"
        return results
    
    def calculate_cost(self, input_tokens: int, output_tokens: int, 
                       model: str) -> dict:
        """计算 API 调用成本"""
        pricing = self.PRICING.get(model, {})
        input_cost = (input_tokens / 1_000_000) * pricing.get("input", 0)
        output_cost = (output_tokens / 1_000_000) * pricing.get("output", 0)
        total = input_cost + output_cost
        return {
            "model": model,
            "input_tokens": input_tokens,
            "output_tokens": output_tokens,
            "input_cost_usd": round(input_cost, 6),
            "output_cost_usd": round(output_cost, 6),
            "total_cost_usd": round(total, 6),
        }
    
    def compare_migration_cost(self, text: str, 
                                output_tokens: int = 500,
                                daily_calls: int = 1_000_000) -> dict:
        """对比从 Opus 4.6 迁移到 4.7 的成本变化"""
        tokens_46 = self.count_tokens(text, "claude-opus-4-6")
        tokens_47 = self.count_tokens(text, "claude-opus-4-7")
        
        cost_46 = self.calculate_cost(tokens_46, output_tokens, "claude-opus-4-6")
        cost_47 = self.calculate_cost(tokens_47, output_tokens, "claude-opus-4-7")
        
        daily_cost_46 = cost_46["total_cost_usd"] * daily_calls
        daily_cost_47 = cost_47["total_cost_usd"] * daily_calls
        
        return {
            "input_text_length": len(text),
            "tokens_opus_46": tokens_46,
            "tokens_opus_47": tokens_47,
            "inflation_ratio": round(tokens_47 / tokens_46, 3),
            "cost_per_call_46": cost_46,
            "cost_per_call_47": cost_47,
            "daily_cost_46": round(daily_cost_46, 2),
            "daily_cost_47": round(daily_cost_47, 2),
            "daily_increase": round(daily_cost_47 - daily_cost_46, 2),
            "monthly_increase": round((daily_cost_47 - daily_cost_46) * 30, 2),
        }


# 使用示例
if __name__ == "__main__":
    analyzer = TokenCostAnalyzer()
    
    # 示例系统提示词
    system_prompt = """你是一位经验丰富的 Python 工程师，擅长构建高并发的分布式系统。
你的任务是审查用户提供的代码，找出潜在的性能瓶颈、安全漏洞和架构问题。
审查时需要关注：时间复杂度、内存使用、线程安全、异常处理、日志记录。
请用简洁专业的语言给出审查意见，包含具体修改建议和代码示例。"""
    
    # 对比分析
    result = analyzer.compare_migration_cost(
        text=system_prompt,
        output_tokens=500,
        daily_calls=1000000
    )
    
    print(f"系统提示词长度: {result['input_text_length']} 字符")
    print(f"Opus 4.6 Token 数: {result['tokens_opus_46']}")
    print(f"Opus 4.7 Token 数: {result['tokens_opus_47']}")
    print(f"Token 膨胀率: {result['inflation_ratio']}x")
    print(f"日成本增加: ${result['daily_increase']}")
    print(f"月成本增加: ${result['monthly_increase']}")

#!/usr/bin/env python3
"""
Token 膨胀实验：对比不同内容类型在 Opus 4.6 vs 4.7 的 Token 差异
"""
import csv
from typing import List, Tuple

# 测试内容样本（模拟不同内容类型）
TEST_CASES = [
    ("系统提示词", """你是一个专业的代码审查助手。请仔细分析以下代码：
1. 检查时间复杂度和空间复杂度
2. 识别潜在的 bug 和安全漏洞
3. 提出具体的改进建议
4. 用清晰的中文解释每个问题"""),
    
    ("普通对话", "你好，请帮我解释一下什么是机器学习中的过拟合现象？"),
    
    ("Python 代码", '''def quicksort(arr: list[int]) -> list[int]:
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)'''),
    
    ("中文文本", "人工智能正在深刻改变软件开发的方式。从代码生成到自动化测试，从智能审查到架构设计，AI 已经渗透到软件工程的每一个环节。"),
    
    ("JSON 数据", '{"model": "claude-opus-4.7", "max_tokens": 4096, "temperature": 0.7, "top_p": 0.9, "stop_sequences": ["\n\n"], "system": "你是一个有用的助手"}'),
]


def simulate_token_count(text: str, model_version: str) -> int:
    """
    模拟 Token 计数（基于 Simon Willison 实测数据推算）
    实际项目中请替换为真实的 API 调用
    """
    base_tokens = len(text.encode('utf-8')) // 4  # 粗略估算
    
    if model_version == "opus-4.7":
        # 根据内容类型应用不同的膨胀系数
        if "系统提示" in text or "system" in text.lower():
            return int(base_tokens * 1.46)  # 系统提示词膨胀最明显
        elif "def " in text or "function" in text:
            return int(base_tokens * 1.2)   # 代码适度膨胀
        else:
            return int(base_tokens * 1.15)  # 普通文本适度膨胀
    else:
        return base_tokens


def run_experiment() -> List[Tuple[str, int, int, float]]:
    """运行对比实验"""
    results = []
    for name, text in TEST_CASES:
        tokens_46 = simulate_token_count(text, "opus-4.6")
        tokens_47 = simulate_token_count(text, "opus-4.7")
        ratio = tokens_47 / tokens_46 if tokens_46 > 0 else 0
        results.append((name, tokens_46, tokens_47, ratio))
    return results


def export_csv(results: List[Tuple[str, int, int, float]], 
               filename: str = "token_inflation_results.csv"):
    """导出结果为 CSV 文件"""
    with open(filename, 'w', encoding='utf-8', newline='') as f:
        writer = csv.writer(f)
        writer.writerow(["内容类型", "Opus 4.6 Token", "Opus 4.7 Token", "膨胀倍数"])
        for name, t46, t47, ratio in results:
            writer.writerow([name, t46, t47, f"{ratio:.2f}x"])
    print(f"结果已导出到 {filename}")


def print_report(results: List[Tuple[str, int, int, float]]):
    """打印分析报告"""
    print("=" * 60)
    print("📊 Claude Opus Token 膨胀分析报告")
    print("=" * 60)
    
    total_46 = sum(r[1] for r in results)
    total_47 = sum(r[2] for r in results)
    avg_ratio = total_47 / total_46 if total_46 > 0 else 0
    
    print(f"\n📈 综合数据:")
    print(f"   总 Token (4.6): {total_46}")
    print(f"   总 Token (4.7): {total_47}")
    print(f"   平均膨胀率: {avg_ratio:.2f}x")
    print(f"\n📋 详细对比:")
    print(f"{'内容类型':<12} {'4.6':>8} {'4.7':>8} {'膨胀':>8}")
    print("-" * 40)
    for name, t46, t47, ratio in results:
        print(f"{name:<12} {t46:>8} {t47:>8} {ratio:>7.2f}x")
    
    # 识别膨胀最严重的内容类型
    worst = max(results, key=lambda x: x[3])
    print(f"\n⚠️  膨胀最严重: {worst[0]} ({worst[3]:.2f}x)")
    print(f"💡 建议: 精简 {worst[0]} 内容以降低迁移成本")


if __name__ == "__main__":
    results = run_experiment()
    print_report(results)
    export_csv(results)

# 每日 Token 消耗监控
def monitor_daily_usage(api_client, threshold_usd=1000):
    today_usage = api_client.get_usage(period="today")
    if today_usage.total_cost > threshold_usd:
        send_alert(f"⚠️ 今日 API 成本 ${today_usage.total_cost}，超过阈值 ${threshold_usd}")
        # 自动降级到更便宜的模型
        switch_to_model("claude-sonnet-4-6")