AI 编程工具全景对比 2026：Claude Code、Copilot、Cursor、Codex、Windsurf、Roo Code 深度横评与选型指南

from dataclasses import dataclass, field
from typing import Callable, Optional
import time
import json
from enum import Enum

class TaskDifficulty(Enum):
    EASY = "简单（单文件补全）"
    MEDIUM = "中等（多函数重构）"
    HARD = "困难（跨模块架构设计）"
    EXPERT = "专家（系统设计+实现）"

@dataclass
class Task:
    """编程评测任务"""
    id: str
    description: str
    difficulty: TaskDifficulty
    expected_output: str
    time_limit_seconds: int = 300
    test_cases: list = field(default_factory=list)
    
    def add_test_case(self, input_data: str, expected: str):
        self.test_cases.append({"input": input_data, "expected": expected})

@dataclass
class EvaluationResult:
    """单次评测结果"""
    task_id: str
    tool_name: str
    correctness: float  # 0-1
    code_quality: float  # 0-1
    context_depth: float  # 0-1
    time_taken: float  # 秒
    tokens_used: int
    cost: float  # 美元
    
    @property
    def total_score(self) -> float:
        return (
            self.correctness * 0.35 +
            self.code_quality * 0.25 +
            self.context_depth * 0.25 +
            max(0, 1 - self.time_taken / 300) * 0.15
        )
    
    @property
    def cost_efficiency(self) -> float:
        return self.total_score / max(self.cost, 0.01)

class BenchmarkSuite:
    """AI 编程工具评测基准"""
    
    def __init__(self):
        self.tasks: dict[str, Task] = {}
        self.results: list[EvaluationResult] = []
    
    def add_task(self, task: Task):
        self.tasks[task.id] = task
    
    def run_evaluation(
        self,
        tool_name: str,
        task_id: str,
        execute_fn: Callable[[Task], dict],
    ) -> EvaluationResult:
        """运行单个评测任务"""
        task = self.tasks[task_id]
        start_time = time.time()
        
        # 执行任务
        result_data = execute_fn(task)
        time_taken = time.time() - start_time
        
        # 评分
        correctness = self._score_correctness(
            result_data.get("output", ""),
            task.expected_output,
            task.test_cases,
        )
        code_quality = self._score_code_quality(
            result_data.get("code", ""),
        )
        context_depth = result_data.get("context_depth", 0.5)
        
        eval_result = EvaluationResult(
            task_id=task_id,
            tool_name=tool_name,
            correctness=correctness,
            code_quality=code_quality,
            context_depth=context_depth,
            time_taken=time_taken,
            tokens_used=result_data.get("tokens", 0),
            cost=result_data.get("cost", 0),
        )
        self.results.append(eval_result)
        return eval_result
    
    def _score_correctness(self, output: str, expected: str, test_cases: list) -> float:
        """评分：正确性"""
        if not test_cases:
            return 0.8 if output.strip() == expected.strip() else 0.3
        
        passed = sum(
            1 for tc in test_cases
            if tc["expected"] in output
        )
        return passed / len(test_cases)
    
    def _score_code_quality(self, code: str) -> float:
        """评分：代码质量（启发式）"""
        score = 0.5
        if code:
            if "def " in code or "class " in code:
                score += 0.1
            if "import " in code:
                score += 0.05
            if '"""' in code or "'''" in code:
                score += 0.1
            lines = code.strip().split("\n")
            if len(lines) > 5:
                score += 0.05
            # 检查是否有错误处理
            if "try:" in code or "except" in code:
                score += 0.1
        return min(score, 1.0)
    
    def generate_report(self) -> str:
        """生成评测报告"""
        tools = set(r.tool_name for r in self.results)
        report = "## AI 编程工具评测报告\n\n"
        
        for tool in sorted(tools):
            tool_results = [r for r in self.results if r.tool_name == tool]
            avg_score = sum(r.total_score for r in tool_results) / len(tool_results)
            avg_cost = sum(r.cost for r in tool_results) / len(tool_results)
            avg_efficiency = sum(r.cost_efficiency for r in tool_results) / len(tool_results)
            
            report += f"### {tool}\n"
            report += f"- 综合得分: {avg_score:.2f}/1.00\n"
            report += f"- 平均成本: ${avg_cost:.3f}/任务\n"
            report += f"- 成本效益: {avg_efficiency:.2f}\n\n"
        
        return report

# 使用示例
if __name__ == "__main__":
    suite = BenchmarkSuite()
    
    suite.add_task(Task(
        id="task-001",
        description="实现一个支持并发请求的异步 HTTP 客户端",
        difficulty=TaskDifficulty.HARD,
        expected_output="AsyncHttpClient",
        test_cases=[
            {"input": "并发 10 个请求", "expected": "asyncio.gather"},
            {"input": "超时处理", "expected": "timeout"},
        ],
    ))
    
    print("Benchmark suite initialized.")
    print(suite.generate_report())

from dataclasses import dataclass
from typing import Optional
import time

@dataclass
class ToolProfile:
    """AI 编程工具配置"""
    name: str
    monthly_cost: float
    max_monthly_tokens: Optional[int] = None
    cost_per_extra_token: float = 0.0
    strengths: list = None
    
    def __post_init__(self):
        if self.strengths is None:
            self.strengths = []

@dataclass
class UsagePattern:
    """使用模式"""
    daily_tasks: int  # 每天任务数
    avg_complexity: float  # 0-1，平均复杂度
    session_hours: float  # 每天使用时长
    quality_requirement: float  # 0-1，质量要求

class MultiToolOptimizer:
    """多工具成本优化器"""
    
    def __init__(self, tools: list[ToolProfile]):
        self.tools = {t.name: t for t in tools}
        self.allocations: dict[str, float] = {}
    
    def optimize(self, pattern: UsagePattern, budget: float) -> dict:
        """基于使用模式和预算，优化多工具分配"""
        monthly_tasks = pattern.daily_tasks * 22  # 工作日
        
        # 根据复杂度和质量要求分配任务
        for name, tool in self.tools.items():
            if pattern.quality_requirement > 0.8:
                # 高质量需求：优先用强推理工具
                if "Claude" in name or "Opus" in str(tool.strengths):
                    self.allocations[name] = 0.5
                elif "Codex" in name:
                    self.allocations[name] = 0.3
                else:
                    self.allocations[name] = 0.2
            elif pattern.quality_requirement > 0.5:
                # 中等质量：均衡分配
                self.allocations[name] = 1.0 / len(self.tools)
            else:
                # 低质量需求：优先用低成本工具
                if tool.monthly_cost == 0:
                    self.allocations[name] = 0.6
                else:
                    self.allocations[name] = 0.4 / (len(self.tools) - 1)
        
        # 计算成本
        total_cost = sum(
            tool.monthly_cost
            for tool in self.tools.values()
            if self.allocations.get(tool.name, 0) > 0
        )
        
        return {
            "allocations": self.allocations,
            "total_monthly_cost": total_cost,
            "within_budget": total_cost <= budget,
            "recommended_tools": [
                name for name, ratio in self.allocations.items()
                if ratio > 0.1
            ],
        }

# 使用示例
if __name__ == "__main__":
    tools = [
        ToolProfile("Claude Code Max", 100, strengths=["推理最强"]),
        ToolProfile("Cursor Pro", 20, strengths=["IDE体验"]),
        ToolProfile("Roo Code", 0, strengths=["开源免费"]),
        ToolProfile("Codex Plus", 20, strengths=["自主执行"]),
    ]
    
    optimizer = MultiToolOptimizer(tools)
    
    pattern = UsagePattern(
        daily_tasks=15,
        avg_complexity=0.6,
        session_hours=4.0,
        quality_requirement=0.7,
    )
    
    result = optimizer.optimize(pattern, budget=150)
    print(f"推荐工具: {result['recommended_tools']}")
    print(f"月成本: ${result['total_monthly_cost']}")
    print(f"预算内: {result['within_budget']}")