Gemini 3.7 Flash与GPT-5.6 Sol Ultrafast同台竞技：2026年8月AI模型效率革命深度解析

import google.generativeai as genai

# 配置Gemini 3.7 Flash
genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel(
    model_name="gemini-3.7-flash",
    generation_config={
        "temperature": 0.2,  # 代码任务使用低温度
        "max_output_tokens": 8192,
        "thinking_effort": "high"  # 新增：推理深度控制
    }
)

# 代码审查prompt
code_review_prompt = (
    "请审查以下Python代码，重点关注：
"
    "1. 安全漏洞（SQL注入、XSS、路径遍历等）
"
    "2. 性能瓶颈
"
    "3. 代码异味（code smell）
"
    "4. 最佳实践偏离

"
    "代码：
"
    "def get_user_data(user_id):
"
    "    conn = sqlite3.connect('app.db')
"
    "    cursor = conn.cursor()
"
    "    query = f'SELECT * FROM users WHERE id = {user_id}'
"
    "    cursor.execute(query)
"
    "    result = cursor.fetchone()
"
    "    conn.close()
"
    "    return result"
)

response = model.generate_content(code_review_prompt)
print(response.text)

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY")

# 实时对话：利用Ultrafast的低延迟特性
def real_time_chat(messages):
    response = client.chat.completions.create(
        model="gpt-5.6-sol-ultrafast",
        messages=messages,
        stream=True,
        stream_options={"include_usage": True}
    )
    
    total_tokens = 0
    for chunk in response:
        if chunk.choices and chunk.choices[0].delta.content:
            content = chunk.choices[0].delta.content
            print(content, end="", flush=True)
        if chunk.usage:
            total_tokens = chunk.usage.total_tokens
    
    print(f"

--- 总Token消耗: {total_tokens} ---")

# 模拟实时客服对话
conversation = [
    {"role": "system", "content": "你是一个专业的技术支持助手，回答简洁准确。"},
    {"role": "user", "content": "我的Docker容器一直重启，日志显示OOM Killed，怎么排查？"}
]

real_time_chat(conversation)

场景1：代码生成（500行Python脚本）
- Gemini 3.7 Flash: 3.2秒 | 代码质量评分: 8.7/10
- GPT-5.6 Sol Ultrafast: 1.8秒 | 代码质量评分: 8.5/10

场景2：复杂Bug修复（多文件项目）
- Gemini 3.7 Flash: 8.5秒 | 修复成功率: 82%
- GPT-5.6 Sol Ultrafast: 5.1秒 | 修复成功率: 76%

场景3：实时对话（多轮交互）
- Gemini 3.7 Flash: 首token 280ms
- GPT-5.6 Sol Ultrafast: 首token 180ms

场景4：文档摘要（10万字长文）
- Gemini 3.7 Flash: 6.3秒 | 摘要质量: 优秀
- GPT-5.6 Sol Ultrafast: 3.9秒 | 摘要质量: 良好

# 使用Gemini 3.7 Flash构建自主部署Agent
import google.generativeai as genai
import subprocess
import json

class DeployAgent:
    def __init__(self):
        self.model = genai.GenerativeModel(
            "gemini-3.7-flash",
            tools=[
                self.run_tests,
                self.deploy_to_staging,
                self.run_smoke_tests,
                self.deploy_to_production
            ]
        )
    
    def run_tests(self, test_path: str) -> str:
        """运行测试套件"""
        result = subprocess.run(
            ["pytest", test_path, "--tb=short", "-q"],
            capture_output=True, text=True
        )
        return f"Exit code: {result.returncode}
{result.stdout[-500:]}"
    
    def deploy_to_staging(self, branch: str) -> str:
        """部署到staging环境"""
        result = subprocess.run(
            ["./deploy.sh", "--env", "staging", "--branch", branch],
            capture_output=True, text=True
        )
        return result.stdout
    
    def run_smoke_tests(self, env: str) -> str:
        """运行冒烟测试"""
        result = subprocess.run(
            ["pytest", "tests/smoke/", f"--base-url=https://{env}.example.com"],
            capture_output=True, text=True
        )
        return f"Pass: {result.returncode == 0}"
    
    def deploy_to_production(self) -> str:
        """部署到生产环境"""
        result = subprocess.run(
            ["./deploy.sh", "--env", "production"],
            capture_output=True, text=True
        )
        return result.stdout
    
    def execute(self, task: str):
        """自主执行部署任务"""
        chat = self.model.start_chat(enable_automatic_function_calling=True)
        response = chat.send_message(
            f"请执行以下部署任务：{task}
"
            "步骤：1.运行测试 2.部署staging 3.冒烟测试 4.部署生产
"
            "如果任何步骤失败，请停止并报告原因。"
        )
        return response.text

agent = DeployAgent()
result = agent.execute("部署feature/payment-v2分支到生产环境")
print(result)