AI芯片军备竞赛升温：Anthropic自研芯片，微软豪赌GPT-5.6 Sol

"""
AI模型成本对比计算器
用于计算不同模型在不同使用场景下的月度成本
"""

from dataclasses import dataclass
from typing import List

@dataclass
class ModelPricing:
    name: str
    input_price: float  # 美元/百万tokens
    output_price: float  # 美元/百万tokens
    tokenizer_multiplier: float  # tokenizer变化系数，1.0表示无变化

# 定义模型定价（基于2026年8月数据）
MODELS = {
    "claude_sonnet_5_new": ModelPricing(
        name="Claude Sonnet 5（9月调价后）",
        input_price=3.00,
        output_price=15.00,
        tokenizer_multiplier=1.35  # 新tokenizer增加35%tokens
    ),
    "claude_sonnet_5_old": ModelPricing(
        name="Claude Sonnet 5（调价前）",
        input_price=2.00,
        output_price=10.00,
        tokenizer_multiplier=1.0
    ),
    "deepseek_v4_flash": ModelPricing(
        name="DeepSeek V4 Flash",
        input_price=0.14,
        output_price=0.28,
        tokenizer_multiplier=1.0
    ),
    "gpt5_6_sol": ModelPricing(
        name="GPT-5.6 Sol（预估）",
        input_price=5.00,
        output_price=15.00,
        tokenizer_multiplier=1.0
    ),
}

@dataclass
class UsageProfile:
    name: str
    daily_input_tokens: int  # 每日输入tokens（调整前）
    daily_output_tokens: int  # 每日输出tokens
    days_per_month: int

# 定义使用场景
PROFILES = {
    "developer_daily": UsageProfile(
        name="个人开发者日常使用",
        daily_input_tokens=500_000,  # 50万tokens/天
        daily_output_tokens=100_000,  # 10万tokens/天
        days_per_month=22
    ),
    "enterprise_team": UsageProfile(
        name="企业团队（50人）",
        daily_input_tokens=10_000_000,  # 1000万tokens/天
        daily_output_tokens=2_000_000,  # 200万tokens/天
        days_per_month=22
    ),
}

def calculate_monthly_cost(model: ModelPricing, profile: UsageProfile) -> dict:
    """计算月度成本"""
    # 应用tokenizer乘数
    actual_input = profile.daily_input_tokens * model.tokenizer_multiplier
    actual_output = profile.daily_output_tokens * model.tokenizer_multiplier
    
    # 计算月度成本
    monthly_input_cost = (actual_input / 1_000_000) * model.input_price * profile.days_per_month
    monthly_output_cost = (actual_output / 1_000_000) * model.output_price * profile.days_per_month
    total_cost = monthly_input_cost + monthly_output_cost
    
    return {
        "model": model.name,
        "profile": profile.name,
        "monthly_input_cost": round(monthly_input_cost, 2),
        "monthly_output_cost": round(monthly_output_cost, 2),
        "total_monthly_cost": round(total_cost, 2),
    }

def compare_models(profile: UsageProfile) -> List[dict]:
    """对比所有模型在特定场景下的成本"""
    results = []
    for model_key, model in MODELS.items():
        result = calculate_monthly_cost(model, profile)
        results.append(result)
    return results

def print_comparison(profile: UsageProfile):
    """打印对比结果"""
    print(f"
{'='*70}")
    print(f"使用场景: {profile.name}")
    print(f"日均输入: {profile.daily_input_tokens:,} tokens | 日均输出: {profile.daily_output_tokens:,} tokens")
    print(f"{'='*70}")
    
    results = compare_models(profile)
    
    # 按成本排序
    results.sort(key=lambda x: x["total_monthly_cost"])
    
    print(f"{'模型':<30} {'输入成本':>12} {'输出成本':>12} {'月度总成本':>14}")
    print(f"{'-'*70}")
    
    for r in results:
        print(f"{r['model']:<30} ${r['monthly_input_cost']:>10.2f} ${r['monthly_output_cost']:>10.2f} ${r['total_monthly_cost']:>12.2f}")
    
    # 计算最便宜和最贵的差距
    cheapest = results[0]
    most_expensive = results[-1]
    ratio = most_expensive["total_monthly_cost"] / cheapest["total_monthly_cost"]
    
    print(f"
最低成本: {cheapest['model']} = ${cheapest['total_monthly_cost']:.2f}/月")
    print(f"最高成本: {most_expensive['model']} = ${most_expensive['total_monthly_cost']:.2f}/月")
    print(f"价差倍数: {ratio:.1f}x")

# 运行对比
if __name__ == "__main__":
    for profile in PROFILES.values():
        print_comparison(profile)
    
    # 计算Claude涨价对开发者的影响
    print(f"
{'='*70}")
    print("Claude Sonnet 5 涨价影响分析")
    print(f"{'='*70}")
    
    dev_profile = PROFILES["developer_daily"]
    old_cost = calculate_monthly_cost(MODELS["claude_sonnet_5_old"], dev_profile)
    new_cost = calculate_monthly_cost(MODELS["claude_sonnet_5_new"], dev_profile)
    
    increase = new_cost["total_monthly_cost"] - old_cost["total_monthly_cost"]
    increase_pct = (increase / old_cost["total_monthly_cost"]) * 100
    
    print(f"调价前月度成本: ${old_cost['total_monthly_cost']:.2f}")
    print(f"调价后月度成本: ${new_cost['total_monthly_cost']:.2f}")
    print(f"月度增加支出: ${increase:.2f} ({increase_pct:.1f}%)")
    print(f"
DeepSeek V4 Flash同等场景成本: "
          f"${calculate_monthly_cost(MODELS['deepseek_v4_flash'], dev_profile)['total_monthly_cost']:.2f}")

======================================================================
使用场景: 个人开发者日常使用
日均输入: 500,000 tokens | 日均输出: 100,000 tokens
======================================================================
模型                              输入成本       输出成本       月度总成本
----------------------------------------------------------------------
DeepSeek V4 Flash                 $      1.54 $      0.62 $         2.15
Claude Sonnet 5（调价前）          $     22.00 $     22.00 $        44.00
Claude Sonnet 5（9月调价后）       $     44.55 $     44.55 $        89.10
GPT-5.6 Sol（预估）               $     55.00 $     33.00 $        88.00

最低成本: DeepSeek V4 Flash = $2.15/月
最高成本: GPT-5.6 Sol（预估） = $88.00/月
价差倍数: 40.9x