哪个大模型好用？2026 按场景选型实战：编程、逆向、写作、翻译与长文档决策矩阵

import os
from openai import OpenAI

API_KEY = os.getenv("GPT345_API_KEY", "sk-your-gpt345-key")
BASE_URL = "https://api.gpt345.com/v1"

client = OpenAI(api_key=API_KEY, base_url=BASE_URL)

class UniversalScenarioRouter:
    SCENARIO_MAPPING = {
        "coding_fast": "qwen-2.5-coder-32b",
        "coding_complex": "deepseek-v4",
        "coding_architect": "claude-3-7-sonnet",
        "reverse_security": "deepseek-v4",
        "chinese_writing": "glm-5.3",
        "translation": "gemini-3-8-flash",
        "long_document": "kimi-k3"
    }

    @classmethod
    def dispatch(cls, scenario: str, prompt: str, system: str = None, **kwargs) -> str:
        """根据业务场景代号，自动选用最优性价比模型"""
        model_id = cls.SCENARIO_MAPPING.get(scenario, "deepseek-v4")
        print(f"[*] 业务场景: [{scenario}] -> 匹配模型: [{model_id}]")
        
        messages = []
        if system:
            messages.append({"role": "system", "content": system})
        messages.append({"role": "user", "content": prompt})

        try:
            resp = client.chat.completions.create(
                model=model_id,
                messages=messages,
                **kwargs
            )
            return resp.choices[0].message.content
        except Exception as err:
            # 统一容灾兜底至 GLM-5.3
            print(f"[!] {model_id} 调用异常: {err}，启动通用容灾降级...")
            resp = client.chat.completions.create(
                model="glm-5.3",
                messages=messages,
                **kwargs
            )
            return resp.choices[0].message.content

if __name__ == "__main__":
    # 场景 1：日常极速代码生成
    code_res = UniversalScenarioRouter.dispatch(
        scenario="coding_fast",
        prompt="写一个高效的字符串反转并去除首尾空白的 Go 函数。"
    def __init__(self, client: OpenAI):
        self.client = client

    def route_and_execute(self, scenario: str, prompt: str, system_prompt: str = "You are a professional assistant."):
        model = self.SCENARIO_MAPPING.get(scenario, "deepseek-v4")
        response = self.client.chat.completions.create(
            model=model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": prompt}
            ],
            temperature=0.2 if "coding" in scenario or "security" in scenario else 0.7
        )
        return {
            "scenario": scenario,
            "model_used": model,
            "content": response.choices[0].message.content,
            "usage": response.usage.model_dump()
        }

# 实例化并针对不同任务路由执行
router = UniversalScenarioRouter(client)

# 1. 快速编程任务 -> 分流至高性价比通义千问或 DeepSeek
res_code = router.route_and_execute(
    scenario="coding_complex",
    prompt="请用 Python 实现一个支持指数退避与重试机制的异步 HTTP 请求客户端。"
)
print(f"[{res_code['model_used']}] 生成完毕，耗费 tokens: {res_code['usage']['total_tokens']}")

# 2. 中文行政公文 -> 分流至 GLM-5.3
res_writing = router.route_and_execute(
    scenario="writing_official",
    prompt="请草拟一份关于公司内部 API 网关升级降本 80% 的全员通告邮件。"
)
print(f"[{res_writing['model_used']}] 撰写完成，用时极短。")