Claude Opus 4.7 Tokenizer 变革全解析：成本暴涨 46% 背后的技术原理、多模型对比与智能路由实战

from collections import Counter
from typing import Dict, List, Tuple

class BPETokenizer:
    """简化版 BPE Tokenizer 实现"""
    
    def __init__(self, merges: int = 1000):
        self.merges = merges
        self.vocab: Dict[str, int] = {}
        self.merge_rules: List[Tuple[str, str]] = []
        
    def get_stats(self, word_freq: Dict[str, int]) -> Dict[Tuple[str, str], int]:
        """统计相邻符号对的频率"""
        pairs = Counter()
        for word, freq in word_freq.items():
            symbols = word.split()
            for i in range(len(symbols) - 1):
                pairs[(symbols[i], symbols[i + 1])] += freq
        return pairs
    
    def merge_vocab(self, pair: Tuple[str, str], 
                    word_freq: Dict[str, int]) -> Dict[str, int]:
        """合并最高频的符号对"""
        bigram = ' '.join(pair)
        replacement = ''.join(pair)
        new_vocab = {}
        
        for word in word_freq:
            new_word = word.replace(bigram, replacement)
            new_vocab[new_word] = word_freq[word]
        
        return new_vocab
    
    def train(self, corpus: List[str]) -> Dict[str, int]:
        """训练 BPE 分词器"""
        # 初始化：将每个字符视为独立 token
        word_freq: Dict[str, int] = Counter(corpus)
        word_freq = {
            ' '.join(list(word)) + ' </w>': freq 
            for word, freq in word_freq.items()
        }
        
        for _ in range(self.merges):
            pairs = self.get_stats(word_freq)
            if not pairs:
                break
            best_pair = max(pairs, key=pairs.get)
            word_freq = self.merge_vocab(best_pair, word_freq)
            self.merge_rules.append(best_pair)
        
        # 构建最终词汇表
        for word in word_freq:
            for token in word.split():
                self.vocab[token] = len(self.vocab)
        
        return self.vocab
    
    def encode(self, text: str) -> List[int]:
        """将文本编码为 token IDs"""
        word = ' '.join(list(text)) + ' </w>'
        for a, b in self.merge_rules:
            word = word.replace(f'{a} {b}', f'{a}{b}')
        return [self.vocab.get(t, 0) for t in word.split()]

# === 使用示例 ===
corpus = ["hello", "world", "helloworld", "hellohello", "worldwide"]
tokenizer = BPETokenizer(merges=50)
vocab = tokenizer.train(corpus)

tokens = tokenizer.encode("hello world")
print(f"Token IDs: {tokens}")
print(f"Vocabulary size: {len(vocab)}")

import anthropic
from typing import Dict, List
from dataclasses import dataclass
import json

@dataclass
class TokenCount:
    """单个模型的 token 计数结果"""
    model: str
    input_tokens: int
    cache_creation_tokens: int = 0
    cache_read_tokens: int = 0

def count_tokens_multi_model(
    text: str,
    models: List[str],
    api_key: str
) -> Dict[str, TokenCount]:
    """
    对比多个 Claude 模型的 token 消耗
    
    Args:
        text: 要计数的文本
        models: Claude 模型 ID 列表
        api_key: Anthropic API Key
    
    Returns:
        模型 -> TokenCount 的映射
    """
    client = anthropic.Anthropic(api_key=api_key)
    results = {}
    
    for model_id in models:
        response = client.messages.count_tokens(
            model=model_id,
            messages=[{"role": "user", "content": text}],
        )
        results[model_id] = TokenCount(
            model=model_id,
            input_tokens=response.input_tokens,
        )
    
    return results

def compare_and_report(
    text: str, 
    api_key: str,
    models: List[str] = None
) -> str:
    """生成 token 对比报告"""
    if models is None:
        models = [
            "claude-opus-4-7-20260416",
            "claude-opus-4-6-20251022",
            "claude-sonnet-4-6-20251022",
            "claude-haiku-4-5-20251022",
        ]
    
    results = count_tokens_multi_model(text, models, api_key)
    
    # 以第一个模型为基准计算相对倍数
    baseline = list(results.values())[0].input_tokens
    
    report_lines = [
        "📊 Token 对比报告",
        "=" * 50,
        f"输入文本长度: {len(text)} 字符",
        f"输入文本 token 估算: {baseline} (以 Opus 4.7 为基准)",
        "",
        f"{'模型':<35} {'Token 数':>10} {'相对倍数':>10}",
        "-" * 55,
    ]
    
    for model_id, count in results.items():
        ratio = count.input_tokens / baseline if baseline else 0
        report_lines.append(
            f"{model_id:<35} {count.input_tokens:>10,} {ratio:>10.2f}×"
        )
    
    return "\n".join(report_lines)

# === 使用示例 ===
if __name__ == "__main__":
    import os
    
    # 测试文本：一个典型的系统 prompt
    test_prompt = """
    You are an expert software engineer. 
    Review the following code and provide:
    1. Bug analysis
    2. Performance suggestions  
    3. Security vulnerabilities
    4. Refactored code
    """ * 50
    
    # 需要先设置环境变量 ANTHROPIC_API_KEY
    api_key = os.environ.get("ANTHROPIC_API_KEY", "your-api-key")
    
    report = compare_and_report(test_prompt, api_key)
    print(report)

import openai
from typing import Dict, List
import tiktoken

class MultiModelTokenAnalyzer:
    """多模型 Token 分析器 —— 支持 OpenAI 和 Anthropic 模型"""
    
    # 模型到 tiktoken 编码的映射
    ENCODING_MAP = {
        "gpt-4o": "o200k_base",
        "gpt-4o-mini": "o200k_base", 
        "gpt-4-turbo": "cl100k_base",
        "gpt-3.5-turbo": "cl100k_base",
        "o1": "o200k_base",
        "o3": "o200k_base",
        "o3-mini": "o200k_base",
    }
    
    def __init__(self, openai_key: str, anthropic_key: str):
        self.openai_client = openai.OpenAI(api_key=openai_key)
        self.anthropic_client = openai.Anthropic(api_key=anthropic_key)
    
    def count_tiktoken(self, text: str, model: str) -> int:
        """使用 tiktoken 库估算 OpenAI 模型 token 数"""
        encoding_name = self.ENCODING_MAP.get(model, "cl100k_base")
        enc = tiktoken.get_encoding(encoding_name)
        return len(enc.encode(text))
    
    def analyze_prompt_efficiency(
        self, 
        prompt: str,
        models_to_compare: List[str]
    ) -> Dict:
        """分析多个模型对同一 prompt 的 token 效率"""
        results = {}
        
        for model in models_to_compare:
            if model.startswith("gpt") or model.startswith("o"):
                tokens = self.count_tiktoken(prompt, model)
                cost_per_m = self._get_openai_price(model)
            else:
                # Anthropic 模型需要 API 调用
                tokens = None  # 需要 API 调用
                cost_per_m = self._get_anthropic_price(model)
            
            results[model] = {
                "tokens": tokens,
                "estimated_cost_per_1k": round(
                    (cost_per_m / 1_000_000) * 1000, 4
                ) if cost_per_m else None,
            }
        
        return results
    
    def _get_openai_price(self, model: str) -> float:
        """2026 年 Q2 OpenAI 模型价格（输入 $/M token）"""
        prices = {
            "gpt-4o": 2.50,
            "gpt-4o-mini": 0.15,
            "gpt-4-turbo": 10.00,
            "o1": 15.00,
            "o3": 10.00,
            "o3-mini": 1.10,
        }
        return prices.get(model, 0)
    
    def _get_anthropic_price(self, model: str) -> float:
        """2026 年 Q2 Anthropic 模型价格（输入 $/M token）"""
        prices = {
            "claude-opus-4-7": 5.00,
            "claude-opus-4-6": 5.00,
            "claude-sonnet-4-6": 3.00,
            "claude-haiku-4-5": 0.25,
        }
        return prices.get(model, 0)

# === 使用示例 ===
if __name__ == "__main__":
    analyzer = MultiModelTokenAnalyzer(
        openai_key="sk-...",
        anthropic_key="sk-ant-..."
    )
    
    test_prompt = "Analyze this Python code for bugs..." * 100
    
    results = analyzer.analyze_prompt_efficiency(
        test_prompt,
        models_to_compare=[
            "gpt-4o",
            "gpt-4o-mini", 
            "o3-mini",
            "claude-opus-4-7",
            "claude-sonnet-4-6",
        ]
    )
    
    print(f"{'模型':<25} {'估算 Token':>12} {'$/1K tokens':>14}")
    print("-" * 51)
    for model, data in results.items():
        tokens = data["tokens"] or "N/A"
        cost = data["estimated_cost_per_1k"] or "N/A"
        print(f"{model:<25} {str(tokens):>12} {str(cost):>14}")

from enum import Enum
from dataclasses import dataclass
from typing import Dict, Optional, Callable
import re

class RequestType(Enum):
    CODE = "code"
    REASONING = "reasoning"
    SUMMARIZATION = "summarization"
    CREATIVE = "creative"
    EXTRACTION = "extraction"
    VISION = "vision"

@dataclass
class ModelProfile:
    """模型画像"""
    id: str
    price_per_m_input: float
    price_per_m_output: float
    token_multiplier: float  # 相对基准的 token 膨胀率
    context_window: int
    max_image_resolution: int  # 0 = 不支持图像
    strengths: list  # 擅长领域
    
    @property
    def effective_input_price(self) -> float:
        return self.price_per_m_input * self.token_multiplier

@dataclass
class Request:
    """AI 请求"""
    content: str
    type: RequestType
    has_image: bool = False
    image_resolution: int = 0
    max_budget_usd: Optional[float] = None
    latency_sensitivity: str = "normal"  # low/normal/high

# === 模型注册 ===
MODELS = {
    "opus-4.7": ModelProfile(
        id="claude-opus-4-7-20260416",
        price_per_m_input=5.00,
        price_per_m_output=25.00,
        token_multiplier=1.46,
        context_window=200_000,
        max_image_resolution=2576,
        strengths=["code", "reasoning", "vision", "long_context"],
    ),
    "opus-4.6": ModelProfile(
        id="claude-opus-4-6-20251022",
        price_per_m_input=5.00,
        price_per_m_output=25.00,
        token_multiplier=1.00,
        context_window=200_000,
        max_image_resolution=1024,
        strengths=["code", "reasoning", "long_context"],
    ),
    "sonnet-4.6": ModelProfile(
        id="claude-sonnet-4-6-20251022",
        price_per_m_input=3.00,
        price_per_m_output=15.00,
        token_multiplier=1.00,
        context_window=200_000,
        max_image_resolution=1024,
        strengths=["code", "general"],
    ),
    "haiku-4.5": ModelProfile(
        id="claude-haiku-4-5-20251022",
        price_per_m_input=0.25,
        price_per_m_output=1.25,
        token_multiplier=1.00,
        context_window=200_000,
        max_image_resolution=0,
        strengths=["speed", "batch", "extraction"],
    ),
    "o3-mini": ModelProfile(
        id="o3-mini-2025-01-31",
        price_per_m_input=1.10,
        price_per_m_output=4.40,
        token_multiplier=1.00,
        context_window=200_000,
        max_image_resolution=0,
        strengths=["reasoning", "math", "code"],
    ),
}

class SmartRouter:
    """智能模型路由器"""
    
    def __init__(self, default_model: str = "sonnet-4.6"):
        self.default_model = default_model
        self.rules: list = []
    
    def classify_request(self, request: Request) -> RequestType:
        """根据内容自动分类请求类型"""
        text = request.content.lower()
        
        code_patterns = [
            r'\bdef\b', r'\bclass\b', r'\bfunction\b',
            r'\bimport\b', r'\bconst\b',  # triple-backtick pattern
            r'\bfix.*bug\b', r'\brefactor\b',
        ]
        
        reasoning_patterns = [
            r'\bsolve\b', r'\bcalculate\b', r'\bprove\b',
            r'\banalyze.*step\b', r'\blogic\b',
            r'\bthink.*carefully\b',
        ]
        
        for pattern in code_patterns:
            if re.search(pattern, text):
                return RequestType.CODE
        
        for pattern in reasoning_patterns:
            if re.search(pattern, text):
                return RequestType.REASONING
        
        return RequestType.CREATIVE
    
    def estimate_cost(
        self, 
        model: ModelProfile, 
        request: Request
    ) -> float:
        """估算请求成本"""
        # 粗略估算：1 token ≈ 4 字符（英文）或 1.5 字符（中文）
        char_count = len(request.content)
        estimated_input_tokens = char_count // 2  # 中英文混合取中间值
        
        # 输出通常为输入的 1-3 倍，取 2 倍估算
        estimated_output_tokens = estimated_input_tokens * 2
        
        input_cost = (estimated_input_tokens / 1_000_000) * model.effective_input_price
        output_cost = (estimated_output_tokens / 1_000_000) * model.price_per_m_output
        
        return input_cost + output_cost
    
    def route(self, request: Request) -> str:
        """路由到最优模型"""
        # 自动分类（如果未指定）
        if request.type == RequestType.CREATIVE and not request.has_image:
            request.type = self.classify_request(request)
        
        candidates = list(MODELS.values())
        best_model = None
        best_score = float('-inf')
        
        for model in candidates:
            score = self._score_model(model, request)
            if score > best_score:
                best_score = score
                best_model = model
        
        return best_model.id
    
    def _score_model(
        self, 
        model: ModelProfile, 
        request: Request
    ) -> float:
        """为模型打分（越高越好）"""
        score = 0.0
        
        # 1. 能力匹配度（权重 40%）
        type_match = request.type.value in model.strengths
        if type_match:
            score += 40
        
        # 2. 图像支持（权重 20%）
        if request.has_image:
            if model.max_image_resolution >= request.image_resolution:
                score += 20
            else:
                score -= 100  # 不支持的模型直接排除
        
        # 3. 上下文窗口（权重 10%）
        estimated_tokens = len(request.content) // 2
        if estimated_tokens > model.context_window:
            score -= 100  # 超出上下文窗口
        elif estimated_tokens > model.context_window * 0.8:
            score += 5  # 接近上限，勉强可用
        else:
            score += 10
        
        # 4. 成本效率（权重 20%）
        cost = self.estimate_cost(model, request)
        if request.max_budget_usd and cost > request.max_budget_usd:
            score -= 100
        else:
            score += 20 / max(cost * 1000, 0.001)  # 成本越低分数越高
        
        # 5. 延迟敏感度（权重 10%）
        if request.latency_sensitivity == "high":
            if "speed" in model.strengths:
                score += 10
            elif "batch" not in model.strengths:
                score -= 5
        
        return score

# === 使用示例 ===
if __name__ == "__main__":
    router = SmartRouter()
    
    # 测试场景 1：代码审查
    code_request = Request(
        content="def bubble_sort(arr):\n    for i in range(len(arr)):\n        for j in range(len(arr)-1):\n            if arr[j] > arr[j+1]:\n                arr[j], arr[j+1] = arr[j+1], arr[j]\n    return arr\n\n请审查这段代码的问题并优化",
        type=RequestType.CODE,
    )
    print(f"代码审查 -> {router.route(code_request)}")
    
    # 测试场景 2：数学推理（预算敏感）
    math_request = Request(
        content="证明费马小定理：如果 p 是质数且 a 不是 p 的倍数，则 a^(p-1) ≡ 1 (mod p)",
        type=RequestType.REASONING,
        max_budget_usd=0.01,
    )
    print(f"数学推理 -> {router.route(math_request)}")
    
    # 测试场景 3：高分辨率图像分析
    vision_request = Request(
        content="分析这张图片中的物体",
        type=RequestType.VISION,
        has_image=True,
        image_resolution=2048,
    )
    print(f"图像分析 -> {router.route(vision_request)}")

"""
生产环境建议：多级降级策略

在实际生产中，建议实现多级降级机制：
1. 优先尝试最便宜的模型
2. 如果结果不满足阈值，自动升级到更强的模型
3. 记录每次升级的决策，用于持续优化路由规则
"""

from typing import Any, Optional
import logging

logger = logging.getLogger(__name__)

class FallbackChain:
    """多级降级模型链"""
    
    def __init__(self, models: list, quality_threshold: float = 0.7):
        """
        Args:
            models: 按成本从低到高排列的模型列表
            quality_threshold: 质量阈值（低于此值触发升级）
        """
        self.models = models
        self.quality_threshold = quality_threshold
    
    def execute_with_fallback(
        self, 
        prompt: str,
        quality_checker: Callable[[str], float]
    ) -> tuple[str, str]:
        """
        执行请求，必要时自动升级模型
        
        Returns:
            (使用的模型 ID, 结果内容)
        """
        for model in self.models:
            logger.info(f"尝试使用模型: {model.id}")
            result = self._call_model(model, prompt)
            quality = quality_checker(result)
            
            logger.info(
                f"模型 {model.id} 质量得分: {quality:.2f} "
                f"(阈值: {self.quality_threshold})"
            )
            
            if quality >= self.quality_threshold:
                logger.info(f"✅ {model.id} 满足质量要求")
                return model.id, result
            else:
                logger.warning(
                    f"❌ {model.id} 质量不足，升级到下一个模型"
                )
        
        logger.error("所有模型均不满足质量要求")
        return self.models[-1].id, result
    
    def _call_model(self, model: ModelProfile, prompt: str) -> str:
        """调用模型的占位实现"""
        # 实际实现中调用对应的 API
        pass

# 使用示例：代码生成场景的降级链
code_generation_chain = FallbackChain(
    models=[
        MODELS["haiku-4.5"],     # 先试最便宜的
        MODELS["sonnet-4.6"],    # 不够好则升级
        MODELS["opus-4.7"],      # 最后用最强大的
    ],
    quality_threshold=0.8,       # 代码质量要求较高
)