GPT-5.5 深度解析：OpenAI 最新旗舰模型的架构演进、能力边界与 API 实战

import openai
import os

# 初始化客户端
client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# 使用 GPT-5.5 进行对话
response = client.chat.completions.create(
    model="gpt-5.5",
    messages=[
        {"role": "system", "content": "你是一个专业的 Python 开发者。"},
        {"role": "user", "content": "用 Python 实现一个线程安全的 LRU Cache，支持最大容量限制和 TTL 过期。"},
    ],
    temperature=0.3,
    max_tokens=4096,
)

print(response.choices[0].message.content)

# 检查 usage
print(f"Tokens used: {response.usage.total_tokens}")
print(f"  prompt: {response.usage.prompt_tokens}")
print(f"  completion: {response.usage.completion_tokens}")

import openai
import os

# Codex API 需要特殊的 endpoint
client = openai.OpenAI(
    api_key=os.getenv("OPENAI_API_KEY"),
    base_url="https://api.openai.com/v1",
)

# 使用 Codex 模式进行编程
response = client.chat.completions.create(
    model="codex-gpt-5.5",
    messages=[
        {"role": "user", "content": """
创建一个 FastAPI 应用，实现以下功能：
1. POST /api/analyze - 接收文本，返回情感分析结果
2. GET /api/health - 健康检查
3. 包含完整的错误处理和日志
4. 使用 Pydantic 进行请求/响应验证
"""},
    ],
    max_tokens=8192,
)

# 输出完整代码
code_content = response.choices[0].message.content
print(code_content)

# 保存到文件
with open("fastapi_app.py", "w") as f:
    f.write(code_content)

print("✅ 代码已保存到 fastapi_app.py")

import openai
import time

# 对比多个模型的代码生成能力
MODELS = {
    "gpt-5.5": "gpt-5.5",
    "gpt-5.4": "gpt-5.4",
    "deepseek-v4-pro": "deepseek/deepseek-v4-pro",  # via OpenRouter
    "qwen3.6-27b": "qwen/qwen3.6-27b",  # via OpenRouter
}

PROMPT = """
用 Python 实现一个异步并发任务调度器：
- 支持任务优先级
- 支持最大并发数限制
- 支持任务超时和重试
- 返回每个任务的执行结果和耗时
"""

results = {}
for name, model_id in MODELS.items():
    print(f"\n测试 {name}...")
    start = time.time()
    
    client = openai.OpenAI(
        api_key=os.getenv("OPENAI_API_KEY" if "gpt" in name else "OPENROUTER_API_KEY"),
        base_url="https://openrouter.ai/api/v1" if "openrouter" in name.lower() or "deepseek" in name or "qwen" in name else "https://api.openai.com/v1",
    )
    
    try:
        response = client.chat.completions.create(
            model=model_id,
            messages=[{"role": "user", "content": PROMPT}],
            max_tokens=4096,
        )
        
        elapsed = time.time() - start
        results[name] = {
            "success": True,
            "time": elapsed,
            "tokens": response.usage.total_tokens,
            "lines": len(response.choices[0].message.content.split("\n")),
        }
        print(f"  ✅ {elapsed:.1f}s, {response.usage.total_tokens} tokens")
    except Exception as e:
        results[name] = {"success": False, "error": str(e)}
        print(f"  ❌ {e}")

# 输出对比结果
print("\n" + "="*60)
print(f"{'模型':<20} {'耗时':<10} {'Tokens':<10} {'代码行数':<10}")
print("-"*60)
for name, r in results.items():
    if r["success"]:
        print(f"{name:<20} {r['time']:<10.1f} {r['tokens']:<10} {r['lines']:<10}")
    else:
        print(f"{name:<20} 失败: {r['error'][:20]}")