GPT-5.5 vs DeepSeek V4 终极对比：OpenAI 旗舰与中国开源先锋的正面交锋

import os
from openai import OpenAI

# GPT-5.5 客户端（OpenAI 官方 API）
gpt_client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# DeepSeek V4-Pro 客户端（OpenAI 兼容接口）
deepseek_client = OpenAI(
    api_key=os.environ["DEEPSEEK_API_KEY"],
    base_url="https://api.deepseek.com/v1"
)

def compare_models(prompt: str) -> dict:
    """对比 GPT-5.5 和 DeepSeek V4-Pro 的输出"""
    
    # GPT-5.5 调用（使用 verbosity 参数）
    gpt_resp = gpt_client.chat.completions.create(
        model="gpt-5.5",
        messages=[{"role": "user", "content": prompt}],
        extra_body={"verbosity": "medium"},
        temperature=0.3
    )
    
    # DeepSeek V4-Pro 调用
    ds_resp = deepseek_client.chat.completions.create(
        model="deepseek-chat-v4-pro",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.3
    )
    
    return {
        "gpt55": {
            "content": gpt_resp.choices[0].message.content,
            "tokens": gpt_resp.usage.total_tokens,
            "cost": estimate_gpt55_cost(gpt_resp.usage),
        },
        "v4pro": {
            "content": ds_resp.choices[0].message.content,
            "tokens": ds_resp.usage.total_tokens,
            "cost": estimate_deepseek_cost(ds_resp.usage, "pro"),
        }
    }

def estimate_gpt55_cost(usage) -> float:
    """GPT-5.5: $5/M input, $30/M output"""
    input_cost = usage.prompt_tokens * 5 / 1_000_000
    output_cost = usage.completion_tokens * 30 / 1_000_000
    return round(input_cost + output_cost, 6)

def estimate_deepseek_cost(usage, tier: str) -> float:
    """V4-Pro: ~$0.35/M input, $1.40/M output"""
    """V4-Flash: ~$0.02/M input, $0.08/M output"""
    rates = {
        "pro": (0.35, 1.40),
        "flash": (0.02, 0.08),
    }
    in_rate, out_rate = rates[tier]
    input_cost = usage.prompt_tokens * in_rate / 1_000_000
    output_cost = usage.completion_tokens * out_rate / 1_000_000
    return round(input_cost + output_cost, 6)

# 实战测试
result = compare_models(
    "用 Python 实现一个 LRUCache，要求支持 O(1) 的 get 和 put 操作，"
    "包含类型注解、docstring 和单元测试。"
)
print(f"GPT-5.5: {result['gpt55']['tokens']} tokens, ${result['gpt55']['cost']}")
print(f"V4-Pro:  {result['v4pro']['tokens']} tokens, ${result['v4pro']['cost']}")

# 混合策略：简单任务用 Flash，复杂任务用 Pro，编程用 GPT-5.5

class SmartRouter:
    """智能路由：根据任务复杂度选择最优模型"""
    
    def __init__(self, gpt_client, ds_client):
        self.gpt = gpt_client
        self.ds = ds_client
    
    def route(self, prompt: str) -> OpenAI:
        """根据提示词特征选择模型"""
        # 中文任务 -> DeepSeek
        if self._is_chinese(prompt):
            if self._is_complex(prompt):
                return self.ds, "deepseek-chat-v4-pro"
            return self.ds, "deepseek-chat-v4-flash"
        # 编程任务 -> GPT-5.5（Agentic Coding 优化）
        if self._is_coding(prompt):
            return self.gpt, "gpt-5.5"
        # 简单通用任务 -> Flash（极致性价比）
        if len(prompt) < 200:
            return self.ds, "deepseek-chat-v4-flash"
        # 其他 -> Pro（均衡选择）
        return self.ds, "deepseek-chat-v4-pro"
    
    def _is_chinese(self, text: str) -> bool:
        return sum(1 for c in text if '\u4e00' <= c <= '\u9fff') > len(text) * 0.3
    
    def _is_coding(self, text: str) -> bool:
        coding_keywords = ["代码", "实现", "函数", "class", "def ", "import ", "algorithm"]
        return any(kw in text.lower() for kw in coding_keywords)
    
    def _is_complex(self, text: str) -> bool:
        return len(text) > 500 or text.count("？") + text.count("?") > 3