OpenAI 与微软的 380 亿美元收入分成上限：从独家绑定走向多云格局

#!/usr/bin/env python3
"""
openai_cost_tracker.py — 估算 OpenAI API 月度调用成本
依赖: pip install openai
运行前设置环境变量: export OPENAI_API_KEY="sk-..."
"""

import os
from datetime import datetime
from collections import defaultdict
from openai import OpenAI

# ---- 模型定价表 (USD per 1M tokens, 2024-07 参考价) ----
PRICING = {
    "gpt-4o":        {"input": 5.00,   "output": 15.00},
    "gpt-4o-mini":   {"input": 0.15,   "output": 0.60},
    "gpt-4-turbo":   {"input": 10.00,  "output": 30.00},
    "gpt-3.5-turbo": {"input": 0.50,   "output": 1.50},
    "o1-preview":    {"input": 15.00,  "output": 60.00},
    "o1-mini":       {"input": 3.00,   "output": 12.00},
}

def estimate_cost(model: str, input_tokens: int, output_tokens: int) -> float:
    """根据模型和 token 数估算单次调用成本"""
    if model not in PRICING:
        print(f"[警告] 模型 {model} 未在定价表中，使用 gpt-4o-mini 作为参考")
        model = "gpt-4o-mini"
    p = PRICING[model]
    return (input_tokens / 1_000_000) * p["input"] + (output_tokens / 1_000_000) * p["output"]

def track_api_call(client: OpenAI, model: str, prompt: str, max_tokens: int = 512) -> dict:
    """执行一次 API 调用并记录 token 使用与成本"""
    resp = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        max_tokens=max_tokens,
    )
    usage = resp.usage
    cost = estimate_cost(model, usage.prompt_tokens, usage.completion_tokens)
    return {
        "model": model,
        "input_tokens": usage.prompt_tokens,
        "output_tokens": usage.completion_tokens,
        "cost_usd": cost,
        "timestamp": datetime.now().isoformat(),
    }

def main():
    client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

    # ---- 模拟一批调用，实际使用时替换为你的业务逻辑 ----
    calls = [
        ("gpt-4o-mini", "用一句话解释什么是收入分成上限"),
        ("gpt-4o",      "分析云厂商独家协议对 AI 公司 IPO 估值的影响，限 200 字"),
        ("gpt-4o-mini", "列举三个开发者选择多云 API 渠道的好处"),
    ]

    log = defaultdict(list)
    total_cost = 0.0

    for model, prompt in calls:
        record = track_api_call(client, model, prompt)
        log[model].append(record)
        total_cost += record["cost_usd"]
        print(f"  [{record['timestamp']}] {model} | "
              f"in={record['input_tokens']} out={record['output_tokens']} | "
              f"cost=${record['cost_usd']:.4f}")

    print(f"\n===== 月度估算摘要 =====")
    for model, records in log.items():
        model_cost = sum(r["cost_usd"] for r in records)
        total_tokens = sum(r["input_tokens"] + r["output_tokens"] for r in records)
        print(f"  {model}: {len(records)} 次调用, {total_tokens} tokens, $${model_cost:.4f}")
    print(f"  总计: ${total_cost:.4f}")

if __name__ == "__main__":
    main()

pip install openai
export OPENAI_API_KEY="sk-你的密钥"
python openai_cost_tracker.py