作为长期在一线做 AI 应用集成的工程师，我深知 API 稳定性与成本控制的重要性。今天这篇文章，我将手把手教大家如何用 HolySheep AI 做完整的负载测试与基准评测，包括压测脚本、性能监控、成本分析，以及常见坑的排查方案。

HolySheep vs 官方 API vs 其他中转站：核心差异对比

对比维度 HolySheep AI 官方 API 其他中转站
汇率 ¥1 = $1（无损） ¥7.3 = $1 ¥6.5-$7.0 = $1
国内延迟 <50ms 150-300ms 80-200ms
充值方式 微信/支付宝直充 国际信用卡/PayPal USDT/银行卡
免费额度 注册即送 $5体验额度 通常无
GPT-4.1 $8/MTok $8/MTok $8.5-$10/MTok
Claude Sonnet 4.5 $15/MTok $15/MTok $16-$18/MTok
Gemini 2.5 Flash $2.50/MTok $2.50/MTok $3-$4/MTok
DeepSeek V3.2 $0.42/MTok $0.55/MTok $0.45-$0.60/MTok
SLA保障 99.9%可用性 企业级SLA 不稳定
Dashboard 实时用量/账单 详细控制台 简陋或无

为什么选 HolySheep

我在实际项目中对比过七八家中转平台，HolySheep 的核心优势有三个：

适合谁与不适合谁

✅ 强烈推荐使用 HolySheep 的场景

❌ 不适合的场景

价格与回本测算

假设一个中型 AI 应用每天处理 500 万 token 输出，按照 GPT-4.1 计算：

平台 每日成本 每月成本 年化成本
HolySheep AI $40 $1,200 $14,400
官方 API（¥7.3汇率） ¥292（$40） ¥8,760 ¥105,120
其他中转（约¥6.8汇率） ¥272（$40+$8%溢价） ¥8,160 ¥97,920

测算结论：用 HolySheep 相比其他中转每月节省约 ¥1,000，相比官方节省 ¥7,560，一年下来就是 ¥90,720 的差距。这还没算延迟优化带来的响应速度提升和用户体验改善。

负载测试脚本：Python + asyncio 并发压测

下面是我在实际项目中使用的压测脚本，基于 Python 3.10+，依赖 aiohttp 和 asyncio：

# requirements.txt

aiohttp>=3.9.0


asyncio-pool>=0.8.0


import asyncio
import aiohttp
import time
import statistics
from typing import List, Dict
from dataclasses import dataclass
from datetime import datetime

@dataclass
class LoadTestResult:
    model: str
    total_requests: int
    success_count: int
    failure_count: int
    avg_latency_ms: float
    p50_latency_ms: float
    p95_latency_ms: float
    p99_latency_ms: float
    min_latency_ms: float
    max_latency_ms: float
    throughput_rps: float
    total_cost_usd: float

class HolySheepBenchmark:
    def __init__(
        self,
        api_key: str,
        base_url: str = "https://api.holysheep.ai/v1",
        max_concurrent: int = 50,
        requests_per_batch: int = 500
    ):
        self.api_key = api_key
        self.base_url = base_url
        self.max_concurrent = max_concurrent
        self.requests_per_batch = requests_per_batch
        self.pricing = {
            "gpt-4.1": {"input": 2.0, "output": 8.0},      # $/MTok
            "claude-sonnet-4-5": {"input": 3.0, "output": 15.0},
            "gemini-2.5-flash": {"input": 0.35, "output": 2.50},
            "deepseek-v3.2": {"input": 0.10, "output": 0.42},
        }
    
    async def send_chat_request(
        self,
        session: aiohttp.ClientSession,
        model: str,
        prompt: str,
        semaphore: asyncio.Semaphore
    ) -> Dict:
        """发送单个 chat/completions 请求并测量延迟"""
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
        
        payload = {
            "model": model,
            "messages": [
                {"role": "system", "content": "你是一个专业的AI助手。"},
                {"role": "user", "content": prompt}
            ],
            "max_tokens": 500,
            "temperature": 0.7
        }
        
        start_time = time.perf_counter()
        try:
            async with semaphore:
                async with session.post(
                    f"{self.base_url}/chat/completions",
                    headers=headers,
                    json=payload,
                    timeout=aiohttp.ClientTimeout(total=30)
                ) as response:
                    await response.json()
                    latency = (time.perf_counter() - start_time) * 1000
                    return {
                        "success": response.status == 200,
                        "status": response.status,
                        "latency_ms": latency,
                        "model": model
                    }
        except Exception as e:
            latency = (time.perf_counter() - start_time) * 1000
            return {
                "success": False,
                "status": 0,
                "latency_ms": latency,
                "model": model,
                "error": str(e)
            }
    
    async def run_load_test(
        self,
        model: str,
        prompt: str = "请用100字介绍一下人工智能的发展历史。"
    ) -> LoadTestResult:
        """执行完整负载测试"""
        print(f"\n{'='*60}")
        print(f"开始测试模型: {model}")
        print(f"并发数: {self.max_concurrent}, 总请求: {self.requests_per_batch}")
        print(f"{'='*60}")
        
        latencies: List[float] = []
        success_count = 0
        failure_count = 0
        
        semaphore = asyncio.Semaphore(self.max_concurrent)
        
        async with aiohttp.ClientSession() as session:
            start_time = time.time()
            
            # 分批执行请求
            tasks = [
                self.send_chat_request(session, model, prompt, semaphore)
                for _ in range(self.requests_per_batch)
            ]
            
            results = await asyncio.gather(*tasks)
            
            total_time = time.time() - start_time
            
            for result in results:
                latencies.append(result["latency_ms"])
                if result["success"]:
                    success_count += 1
                else:
                    failure_count += 1
        
        latencies.sort()
        
        # 计算成本（估算）
        input_tokens_est = self.requests_per_batch * 50  # 估算每个请求50token
        output_tokens_est = self.requests_per_batch * 200
        price = self.pricing.get(model, {"input": 1.0, "output": 5.0})
        total_cost = (input_tokens_est * price["input"] + output_tokens_est * price["output"]) / 1_000_000
        
        return LoadTestResult(
            model=model,
            total_requests=self.requests_per_batch,
            success_count=success_count,
            failure_count=failure_count,
            avg_latency_ms=statistics.mean(latencies),
            p50_latency_ms=latencies[int(len(latencies) * 0.50)],
            p95_latency_ms=latencies[int(len(latencies) * 0.95)],
            p99_latency_ms=latencies[int(len(latencies) * 0.99)],
            min_latency_ms=min(latencies),
            max_latency_ms=max(latencies),
            throughput_rps=self.requests_per_batch / total_time,
            total_cost_usd=total_cost
        )
    
    def print_report(self, result: LoadTestResult):
        """打印测试报告"""
        print(f"\n📊 {result.model} 压测报告")
        print(f"├─ 总请求数: {result.total_requests}")
        print(f"├─ 成功: {result.success_count} | 失败: {result.failure_count}")
        print(f"├─ 平均延迟: {result.avg_latency_ms:.2f}ms")
        print(f"├─ P50延迟: {result.p50_latency_ms:.2f}ms")
        print(f"├─ P95延迟: {result.p95_latency_ms:.2f}ms")
        print(f"├─ P99延迟: {result.p99_latency_ms:.2f}ms")
        print(f"├─ 最小延迟: {result.min_latency_ms:.2f}ms")
        print(f"├─ 最大延迟: {result.max_latency_ms:.2f}ms")
        print(f"├─ 吞吐量: {result.throughput_rps:.2f} req/s")
        print(f"└─ 估算成本: ${result.total_cost_usd:.4f}")
        
        # 性能评级
        if result.failure_count / result.total_requests > 0.05:
            print("⚠️  警告: 失败率超过5%，建议检查网络或API配置")
        elif result.p95_latency_ms < 1000:
            print("✅ 性能评级: 优秀 (P95 < 1s)")
        elif result.p95_latency_ms < 3000:
            print("✅ 性能评级: 良好 (P95 < 3s)")
        else:
            print("⚠️  性能评级: 一般，建议优化")


async def main():
    # 初始化基准测试（请替换为你的 HolySheep API Key）
    benchmark = HolySheepBenchmark(
        api_key="YOUR_HOLYSHEEP_API_KEY",
        max_concurrent=30,
        requests_per_batch=300
    )
    
    # 测试多个主流模型
    models_to_test = [
        "gpt-4.1",
        "gemini-2.5-flash",
        "deepseek-v3.2"
    ]
    
    all_results = []
    for model in models_to_test:
        result = await benchmark.run_load_test(model)
        benchmark.print_report(result)
        all_results.append(result)
        await asyncio.sleep(2)  # 模型间休息2秒
    
    # 生成对比报告
    print(f"\n\n{'='*60}")
    print("📈 多模型性能对比汇总")
    print(f"{'='*60}")
    print(f"{'模型':<20} {'成功率':<10} {'P95延迟':<12} {'吞吐量':<15} {'估算成本'}")
    print("-" * 60)
    for r in all_results:
        success_rate = f"{r.success_count/r.total_requests*100:.1f}%"
        print(f"{r.model:<20} {success_rate:<10} {r.p95_latency_ms:.0f}ms{'':<6} {r.throughput_rps:.1f} req/s    ${r.total_cost_usd:.4f}")


if __name__ == "__main__":
    asyncio.run(main())

并发连接池配置与连接复用

对于需要持续高并发的生产环境，我推荐使用连接池来避免频繁建立 TCP 连接的开销。以下是生产级别的连接配置：

import aiohttp
import asyncio
from typing import Optional

class HolySheepConnectionPool:
    """
    HolySheep API 连接池管理器
    支持连接复用、自动重试、熔断降级
    """
    
    def __init__(
        self,
        api_key: str,
        base_url: str = "https://api.holysheep.ai/v1",
        max_connections: int = 100,
        max_connections_per_host: int = 30,
        keepalive_timeout: int = 30,
        retry_attempts: int = 3,
        retry_delay: float = 1.0
    ):
        self.api_key = api_key
        self.base_url = base_url
        self.retry_attempts = retry_attempts
        self.retry_delay = retry_delay
        
        # TCP 连接池配置
        self._connector = aiohttp.TCPConnector(
            limit=max_connections,
            limit_per_host=max_connections_per_host,
            ttl_dns_cache=300,  # DNS 缓存5分钟
            enable_cleanup_closed=True,
            force_close=False,
            keepalive_timeout=keepalive_timeout
        )
        
        self._session: Optional[aiohttp.ClientSession] = None
    
    async def __aenter__(self):
        """上下文管理器入口"""
        self._session = aiohttp.ClientSession(
            connector=self._connector,
            timeout=aiohttp.ClientTimeout(
                total=60,      # 总超时60秒
                connect=10,    # 连接超时10秒
                sock_read=30   # 读取超时30秒
            ),
            headers={
                "Authorization": f"Bearer {self.api_key}",
                "Content-Type": "application/json",
                "User-Agent": "HolySheep-Benchmark/1.0"
            }
        )
        return self
    
    async def __aexit__(self, exc_type, exc_val, exc_tb):
        """上下文管理器退出"""
        if self._session:
            await self._session.close()
    
    async def chat_completions(
        self,
        model: str,
        messages: list,
        **kwargs
    ) -> dict:
        """发送 Chat Completions 请求（带自动重试）"""
        payload = {
            "model": model,
            "messages": messages,
            **kwargs
        }
        
        last_error = None
        for attempt in range(self.retry_attempts):
            try:
                async with self._session.post(
                    f"{self.base_url}/chat/completions",
                    json=payload
                ) as response:
                    if response.status == 200:
                        return await response.json()
                    elif response.status == 429:
                        # 限流，等待后重试
                        wait_time = 2 ** attempt
                        print(f"⚠️  Rate limited, waiting {wait_time}s...")
                        await asyncio.sleep(wait_time)
                        continue
                    elif response.status >= 500:
                        # 服务端错误，重试
                        await asyncio.sleep(self.retry_delay * (attempt + 1))
                        continue
                    else:
                        error_body = await response.text()
                        raise Exception(f"API Error {response.status}: {error_body}")
                        
            except aiohttp.ClientError as e:
                last_error = e
                await asyncio.sleep(self.retry_delay * (attempt + 1))
                continue
        
        raise Exception(f"Failed after {self.retry_attempts} attempts: {last_error}")
    
    async def batch_chat(
        self,
        requests: list,
        concurrency: int = 20
    ) -> list:
        """批量并发请求（带信号量控制并发数）"""
        semaphore = asyncio.Semaphore(concurrency)
        
        async def bounded_request(req):
            async with semaphore:
                return await self.chat_completions(**req)
        
        return await asyncio.gather(*[bounded_request(r) for r in requests])



使用示例

async def production_example():
    async with HolySheepConnectionPool(
        api_key="YOUR_HOLYSHEEP_API_KEY",
        max_connections=100
    ) as pool:
        # 单次请求
        response = await pool.chat_completions(
            model="gpt-4.1",
            messages=[
                {"role": "user", "content": "Hello, explain quantum computing in 50 words."}
            ],
            max_tokens=100,
            temperature=0.7
        )
        print(f"Response: {response['choices'][0]['message']['content']}")
        
        # 批量请求
        batch_requests = [
            {"model": "deepseek-v3.2", "messages": [{"role": "user", "content": f"Query {i}"}]}
            for i in range(50)
        ]
        results = await pool.batch_chat(batch_requests, concurrency=15)
        print(f"Processed {len(results)} requests")


if __name__ == "__main__":
    asyncio.run(production_example())

压测数据采集与 Grafana 可视化

我在生产环境中会将压测数据导出到 Prometheus+Grafana 做长期监控。以下是数据采集端点：

# Prometheus metrics endpoint (Flask example)
from flask import Flask, jsonify
from prometheus_client import Counter, Histogram, Gauge, generate_latest
import time

app = Flask(__name__)


定义指标

REQUEST_COUNT = Counter(
    'holysheep_requests_total',
    'Total requests to HolySheep API',
    ['model', 'status']
)

REQUEST_LATENCY = Histogram(
    'holysheep_request_latency_seconds',
    'Request latency in seconds',
    ['model'],
    buckets=[0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0]
)

BILLING_COST = Gauge(
    'holysheep_billing_cost_usd',
    'Estimated billing cost in USD',
    ['model']
)


使用装饰器自动采集指标

def track_request(model: str):
    def decorator(func):
        def wrapper(*args, **kwargs):
            start = time.time()
            status = "success"
            try:
                result = func(*args, **kwargs)
                return result
            except Exception as e:
                status = "error"
                raise
            finally:
                latency = time.time() - start
                REQUEST_COUNT.labels(model=model, status=status).inc()
                REQUEST_LATENCY.labels(model=model).observe(latency)
                # 估算成本（基于token数，实际生产应从响应中获取）
                cost = latency * 0.0001  # 简化估算
                BILLING_COST.labels(model=model).inc(cost)
        return wrapper
    return decorator


@app.route('/metrics')
def metrics():
    return generate_latest(), 200, {'Content-Type': 'text/plain'}



Grafana Dashboard JSON (关键面板配置)

DASHBOARD_CONFIG = """
{
  "panels": [
    {
      "title": "HolySheep API 请求量",
      "type": "graph",
      "targets": [
        {
          "expr": "rate(holysheep_requests_total[5m])",
          "legendFormat": "{{model}} - {{status}}"
        }
      ]
    },
    {
      "title": "P95 响应延迟",
      "type": "graph", 
      "targets": [
        {
          "expr": "histogram_quantile(0.95, rate(holysheep_request_latency_seconds_bucket[5m]))",
          "legendFormat": "{{model}} P95"
        }
      ]
    },
    {
      "title": "累计费用 (USD)",
      "type": "stat",
      "targets": [
        {
          "expr": "holysheep_billing_cost_usd",
          "legendFormat": "{{model}}"
        }
      ]
    }
  ]
}
"""

常见报错排查

在实际压测过程中，我整理了最常见的 8 类错误及解决方案：

错误1：401 Unauthorized - API Key 无效或已过期

# ❌ 错误响应示例
{
  "error": {
    "message": "Incorrect API key provided",
    "type": "invalid_request_error",
    "code": "invalid_api_key"
  }
}


✅ 解决方案


1. 检查 API Key 格式是否正确（应包含 hs_ 前缀）


2. 确认 Key 已正确配置在请求头

headers = {
    "Authorization": f"Bearer {api_key}",  # 不要遗漏 "Bearer " 前缀
    "Content-Type": "application/json"
}


3. 验证 Key 有效性（调用 /models 端点）

async def verify_api_key(api_key: str) -> bool:
    async with aiohttp.ClientSession() as session:
        headers = {"Authorization": f"Bearer {api_key}"}
        async with session.get(
            "https://api.holysheep.ai/v1/models",
            headers=headers
        ) as resp:
            return resp.status == 200

错误2：429 Rate Limit Exceeded - 请求过于频繁

# ❌ 错误响应
{
  "error": {
    "message": "Rate limit exceeded",
    "type": "rate_limit_error",
    "code": "rate_limit_exceeded",
    "retry_after_ms": 5000
  }
}


✅ 解决方案：实现指数退避重试

async def request_with_retry(session, url, payload, max_retries=5):
    for attempt in range(max_retries):
        try:
            async with session.post(url, json=payload) as resp:
                if resp.status == 429:
                    # 读取 Retry-After 头，如果没有则使用指数退避
                    retry_after = resp.headers.get('Retry-After', 2 ** attempt)
                    wait_time = float(retry_after) if retry_after.isdigit() else 2 ** attempt
                    print(f"⏳ Rate limited, waiting {wait_time}s (attempt {attempt+1}/{max_retries})")
                    await asyncio.sleep(wait_time)
                    continue
                return await resp.json()
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            await asyncio.sleep(2 ** attempt)
    

✅ 预防措施：使用令牌桶算法控制速率

import time

class TokenBucket:
    def __init__(self, rate: float, capacity: int):
        self.rate = rate  # 每秒补充的令牌数
        self.capacity = capacity
        self.tokens = capacity
        self.last_update = time.time()
    
    def consume(self, tokens: int = 1) -> bool:
        now = time.time()
        # 补充令牌
        elapsed = now - self.last_update
        self.tokens = min(self.capacity, self.tokens + elapsed * self.rate)
        self.last_update = now
        
        if self.tokens >= tokens:
            self.tokens -= tokens
            return True
        return False
    
    async def wait_for_token(self, tokens: int = 1):
        while not self.consume(tokens):
            await asyncio.sleep(0.1)

错误3：Connection Timeout - 连接超时

# ❌ 错误信息
asyncio.exceptions.TimeoutError: Connection timeout


✅ 解决方案


1. 增加超时配置

async with aiohttp.ClientSession(
    timeout=aiohttp.ClientTimeout(
        total=60,      # 总超时60秒（原默认5分钟）
        connect=15,    # 连接超时15秒（原默认5分钟）
        sock_read=45   # 读取超时45秒
    )
) as session:
    ...


2. 添加 DNS 优化（使用国内 DNS）

import aiohttp
resolver = aiohttp.AsyncResolver(nameservers=["223.5.5.5", "119.29.29.29"])  # 阿里/腾讯DNS
connector = aiohttp.TCPConnector(resolver=resolver)
session = aiohttp.ClientSession(connector=connector)


3. 检查网络路由（从服务器traceroute测试）


$ traceroute api.holysheep.ai

错误4：400 Bad Request - 请求参数错误

# ❌ 常见错误场景

1. model 字段拼写错误

payload = {
    "model": "gpt-4",        # ❌ 错误（已被弃用）
    # "model": "gpt-4.1",   # ✅ 正确
}


2. messages 格式错误

payload = {
    "messages": "Hello"  # ❌ 错误（应为数组）
}

✅ 正确格式

payload = {
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello"}
    ]
}


3. max_tokens 超出范围

payload = {
    "max_tokens": 100000  # ❌ 最大通常是 4096 或 8192
}


✅ 完整参数校验示例

def validate_chat_payload(model: str, messages: list, **kwargs) -> dict:
    valid_models = ["gpt-4.1", "claude-sonnet-4-5", "gemini-2.5-flash", "deepseek-v3.2"]
    if model not in valid_models:
        raise ValueError(f"Invalid model: {model}. Must be one of {valid_models}")
    
    if not messages or not isinstance(messages, list):
        raise ValueError("messages must be a non-empty list")
    
    for msg in messages:
        if "role" not in msg or "content" not in msg:
            raise ValueError(f"Each message must have 'role' and 'content': {msg}")
    
    if "max_tokens" in kwargs:
        if not 1 <= kwargs["max_tokens"] <= 8192:
            raise ValueError("max_tokens must be between 1 and 8192")
    
    return {"model": model, "messages": messages, **kwargs}

错误5：500 Internal Server Error - 服务端错误

# ❌ 错误响应
{
  "error": {
    "message": "An unexpected error occurred",
    "type": "server_error",
    "code": "internal_error"
  }
}


✅ 解决方案


1. 检查 HolySheep 官方状态页


2. 实现熔断降级机制

class CircuitBreaker:
    def __init__(self, failure_threshold=5, recovery_timeout=60):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.failure_count = 0
        self.last_failure_time = None
        self.state = "CLOSED"  # CLOSED, OPEN, HALF_OPEN
    
    def call(self, func, *args, **kwargs):
        if self.state == "OPEN":
            if time.time() - self.last_failure_time > self.recovery_timeout:
                self.state = "HALF_OPEN"
            else:
                raise Exception("Circuit breaker is OPEN")
        
        try:
            result = func(*args, **kwargs)
            if self.state == "HALF_OPEN":
                self.state = "CLOSED"
                self.failure_count = 0
            return result
        except Exception as e:
            self.failure_count += 1
            self.last_failure_time = time.time()
            if self.failure_count >= self.failure_threshold:
                self.state = "OPEN"
            raise e

完整压测工作流：从计划到报告

我通常按以下流程执行完整压测：

#!/bin/bash

holysheep_load_test.sh - 完整的压测脚本


set -e

API_KEY="${HOLYSHEEP_API_KEY:-YOUR_HOLYSHEEP_API_KEY}"
BASE_URL="https://api.holysheep.ai/v1"

echo "🚀 HolySheep AI 负载测试开始"
echo "时间: $(date)"
echo "API Key: ${API_KEY:0:8}..."


1. 健康检查

echo -e "\n📡 Step 1: 健康检查..."
curl -s -w "\nHTTP_CODE:%{http_code}\n" \
    -H "Authorization: Bearer $API_KEY" \
    "$BASE_URL/models" | head -c 200


2. 基础功能测试（单请求）

echo -e "\n\n🔧 Step 2: 基础功能测试..."
curl -s -X POST "$BASE_URL/chat/completions" \
    -H "Authorization: Bearer $API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
        "model": "deepseek-v3.2",
        "messages": [{"role": "user", "content": "Say hello in 10 words"}],
        "max_tokens": 50
    }' | jq -r '.choices[0].message.content'


3. 并发压力测试（使用 Python 脚本）

echo -e "\n\n💪 Step 3: 压力测试（300请求/30并发）..."
python3 -c "
import asyncio
import aiohttp
import time

async def stress_test():
    async with aiohttp.ClientSession() as session:
        headers = {'Authorization': 'Bearer $API_KEY'}
        payload = {
            'model': 'deepseek-v3.2',
            'messages': [{'role': 'user', 'content': 'Test request'}],
            'max_tokens': 100
        }
        
        latencies = []
        start = time.time()
        
        async def single_request():
            t0 = time.time()
            async with session.post(
                '$BASE_URL/chat/completions',
                headers=headers,
                json=payload
            ) as r:
                await r.json()
            latencies.append((time.time() - t0) * 1000)
        
        # 30 并发，300 总请求
        tasks = [single_request() for _ in range(300)]
        await asyncio.gather(*tasks)
        
        elapsed = time.time() - start
        print(f'总耗时: {elapsed:.2f}s')
        print(f'吞吐量: {300/elapsed:.2f} req/s')
        print(f'平均延迟: {sum(latencies)/len(latencies):.2f}ms')
        print(f'P95延迟: {sorted(latencies)[int(len(latencies)*0.95)]:.2f}ms')

asyncio.run(stress_test())
"


4. 费用估算

echo -e "\n💰 Step 4: 费用估算（基于 10000 次请求）"
echo "| 模型 | Input成本 | Output成本 | 总估算 |"
echo "|------|----------|------------|--------|"
echo "| gpt-4.1 | \$0.10 | \$4.00 | \$4.10 |"
echo "| claude-sonnet-4-5 | \$0.15 | \$7.50 | \$7.65 |"
echo "| gemini-2.5-flash | \$0.02 | \$1.25 | \$1.27 |"
echo "| deepseek-v3.2 | \$0.005 | \$0.21 | \$0

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