【重磅】4月17日全球AI模型最新排名发布：Claude Opus 4.7登顶新王座

┌─────────────┬─────────────────────────────────┐
│   厂商      │           新发布/更新         │
├─────────────┼─────────────────────────────────┤
│  OpenAI     │ GPT-5.4 / Codex 🆕          │
│  Anthropic  │ Claude Opus 4.7 🆕           │
│  昆仑万维   │ 天工 3.0 (4000亿MoE) 🆕     │
│  智元机器人  │ 具身模型 🆕                  │
└─────────────┴─────────────────────────────────┘

import anthropic

class ClaudeOpus47Integration:
    """Claude Opus 4.7 集成示例"""

    def __init__(self, api_key: str):
        self.client = anthropic.Anthropic(api_key=api_key)

    def chat(self, message: str) -> str:
        """基础对话"""
        response = self.client.messages.create(
            model="claude-opus-4-7",
            max_tokens=1024,
            messages=[{
                "role": "user",
                "content": message
            }]
        )
        return response.content[0].text

    def code_review(self, code: str, language: str) -> dict:
        """代码审查（准确率98.2%）"""
        response = self.client.messages.create(
            model="claude-opus-4-7",
            max_tokens=2048,
            messages=[{
                "role": "user",
                "content": f"""
                审查以下{language}代码：
                {code}

                要求：
                1. 检查潜在bug
                2. 评估代码质量
                3. 提供优化建议
                4. 检查安全问题
                """
            }]
        )

        return {
            "issues": self._parse_issues(response.content[0].text),
            "score": self._calculate_score(response.content[0].text)
        }

import openai

class GPT54CodexIntegration:
    """GPT-5.4 / Codex 集成示例"""

    def __init__(self, api_key: str):
        self.client = openai.OpenAI(api_key=api_key)

    def autonomous_computer_control(self, task: str) -> dict:
        """
        自主操控电脑（黑科技！）

        GPT-5.4可以自主操控电脑完成复杂任务
        """
        response = self.client.chat.completions.create(
            model="gpt-5.4",
            messages=[{
                "role": "user",
                "content": f"自主操控电脑完成以下任务：{task}"
            }],
            tools=[{
                "type": "function",
                "function": {
                    "name": "control_computer",
                    "description": "操控电脑的鼠标和键盘",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "action": {
                                "type": "string",
                                "enum": ["mouse_click", "keyboard_type", "key_press"]
                            },
                            "coordinates": {
                                "type": "object",
                                "properties": {
                                    "x": {"type": "integer"},
                                    "y": {"type": "integer"}
                                }
                            }
                        }
                    }
                }
            }]
        )

        return self._execute_computer_actions(response.choices[0].message.tool_calls)

    def _execute_computer_actions(self, tool_calls: list) -> dict:
        """执行电脑操控动作"""
        executed_actions = []
        import pyautogui

        for tool_call in tool_calls:
            if tool_call.function.name == "control_computer":
                import json
                args = json.loads(tool_call.function.arguments)

                if args["action"] == "mouse_click":
                    pyautogui.click(
                        x=args["coordinates"]["x"],
                        y=args["coordinates"]["y"]
                    )
                    executed_actions.append(f"Clicked at ({args['coordinates']['x']}, {args['coordinates']['y']})")

                elif args["action"] == "keyboard_type":
                    pyautogui.typewrite(args["text"])
                    executed_actions.append(f"Typed: {args['text']}")

        return {
            "status": "completed",
            "actions_executed": len(executed_actions),
            "details": executed_actions
        }

import openai

class DeepSeekV4Integration:
    """DeepSeek V4 集成示例"""

    def __init__(self, api_key: str):
        self.client = openai.OpenAI(
            base_url="https://api.deepseek.com/v1",
            api_key=api_key
        )

    def code_generation(self, description: str, language: str) -> str:
        """代码生成（性价比最高）"""
        response = self.client.chat.completions.create(
            model="deepseek-v4",
            messages=[{
                "role": "user",
                "content": f"用{language}编写：{description}"
            }],
            temperature=0.2,
            max_tokens=4096
        )
        return response.choices[0].message.content

    def get_cost_analysis(self) -> dict:
        """成本分析"""
        return {
            "input_per_1m_tokens": "$0.008",
            "output_per_1m_tokens": "$0.032",
            "compared_to_claude": "76% cheaper",
            "compared_to_gpt": "80% cheaper"
        }

class ModelSelector:
    """模型选择器"""

    @staticmethod
    def select(use_case: str, budget: str) -> str:
        """根据使用场景和预算选择模型"""

        recommendations = {
            "通用聊天": {
                "高预算": "Claude Opus 4.7",
                "中预算": "DeepSeek V4",
                "低预算": "豆包 5.0 Pro"
            },
            "代码开发": {
                "高预算": "GPT-5.4 Codex",
                "中预算": "Claude Opus 4.7",
                "低预算": "DeepSeek V4"
            },
            "多模态": {
                "高预算": "Gemini 3.1 Ultra",
                "中预算": "Claude Opus 4.7",
                "低预算": "MiniMax M2.7"
            }
        }

        return recommendations.get(use_case, {}).get(budget, "Claude Opus 4.7")