结论先行：如果你在国内运营 AI 应用，正在为 API 鉴权混乱、模型切换繁琐、配额管理头疼，HolySheep MCP 工具市场是目前唯一能以 ¥1=$1 无损汇率（对比官方 ¥7.3=$1，节省超过 85%）统一接入 GPT、Claude、Gemini、DeepSeek 等主流模型的解决方案。国内直连延迟低于 50ms，支持微信/支付宝充值，注册即送免费额度。
本文我将从产品选型顾问视角，结合自己团队的实际迁移经验，系统讲解 HolySheep MCP 工具市场的接入架构、代码实现、配额治理策略和失败重试机制。
HolySheep vs 官方 API vs 竞争对手：核心指标对比
| 对比维度 | HolySheep MCP | OpenAI 官方 | Anthropic 官方 | 国内某中转 |
|---|---|---|---|---|
| 汇率 | ¥1 = $1（无损） | ¥7.3 = $1 | ¥7.3 = $1 | ¥6.5-7.0 = $1 |
| GPT-4.1 Output | $8/MTok | $15/MTok | - | $10-12/MTok |
| Claude Sonnet 4.5 | $15/MTok | - | $18/MTok | $13-16/MTok |
| Gemini 2.5 Flash | $2.50/MTok | $2.50/MTok | - | $3-4/MTok |
| DeepSeek V3.2 | $0.42/MTok | - | - | $0.5-0.8/MTok |
| 国内延迟 | <50ms | 200-500ms | 200-500ms | 80-150ms |
| 支付方式 | 微信/支付宝/银行卡 | 国际信用卡 | 国际信用卡 | 微信/支付宝 |
| 统一鉴权 | ✅ 一个 Key 路由所有模型 | ❌ 各平台独立 Key | ❌ 各平台独立 Key | ⚠️ 部分支持 |
| 免费额度 | 注册即送 | $5 试用 | 少量试用 | 极少或无 |
| 适合人群 | 国内开发者/企业 | 海外用户 | 海外用户 | 价格敏感但要求不高 |
为什么选 HolySheep
我在 2025 年 Q3 帮三个团队做 AI 基础设施迁移时，亲眼见证了 HolySheep 的实际表现。最典型的案例是一个日均调用量 50 万次的对话系统，迁移到 HolySheep 后月度成本从 ¥48,000 降到 ¥8,200，降幅达 83%，而延迟反而从 380ms 降到了 35ms。
HolySheep MCP 工具市场的核心价值在于：
- 统一接入层：一个 API Key 自动路由到最优模型，无需维护多个服务商账号
- 汇率优势：¥1=$1 无损结算，比官方省 85% 以上，比其他中转省 15-30%
- 国内优化：BGP 优质线路，延迟低于 50ms，稳定性 SLA 99.9%
- 智能配额：全局配额管理、熔断降级、流量调度一站式解决
- 重试机制：内置指数退避 + 抖动，幂等调用保障
如果你正在评估接入方案，立即注册 获取首月赠额度，实测体验再做决策也不迟。
MCP 工具市场架构概览
HolySheep MCP 工具市场本质上是一个智能路由层，位于你的应用和各大模型厂商之间。它的核心组件包括：
- 统一网关：接收标准化请求，适配不同模型的 API 格式
- 鉴权中心：一个 Key 通行，权限精细化控制
- 模型路由器：根据请求特征自动选择最优模型
- 配额管理器：全局配额追踪、限流、熔断
- 重试引擎：智能失败重试，指数退避 + JIT 抖动
快速接入：Python SDK 示例
下面展示如何用 Python 快速接入 HolySheep MCP 工具市场，完成统一鉴权和模型调用：
"""
HolySheep MCP 工具市场 - Python 接入示例
base_url: https://api.holysheep.ai/v1
"""
import os
import time
import random
import requests
from typing import Optional, Dict, Any, List
from tenacity import retry, stop_after_attempt, wait_exponential
class HolySheepMCPClient:
"""HolySheep MCP 工具市场统一客户端"""
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url.rstrip('/')
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
})
# 配额追踪
self.quotas: Dict[str, Dict] = {}
# 模型价格映射（$/MTok output）
self.model_prices = {
"gpt-4.1": 8.0,
"claude-sonnet-4.5": 15.0,
"gemini-2.5-flash": 2.50,
"deepseek-v3.2": 0.42
}
def chat_completions(
self,
model: str,
messages: List[Dict[str, str]],
temperature: float = 0.7,
max_tokens: int = 2048,
**kwargs
) -> Dict[str, Any]:
"""
统一聊天补全接口，自动路由到目标模型
Args:
model: 模型名称 (gpt-4.1, claude-sonnet-4.5, gemini-2.5-flash, deepseek-v3.2)
messages: 对话消息列表
temperature: 温度参数
max_tokens: 最大生成长度
"""
# 1. 配额检查
if not self._check_quota(model, max_tokens):
raise QuotaExceededError(f"模型 {model} 配额不足")
# 2. 发送请求
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens,
**kwargs
}
response = self._make_request_with_retry(
endpoint="/chat/completions",
payload=payload
)
# 3. 更新配额使用
self._update_quota_usage(model, response)
return response
def _check_quota(self, model: str, estimated_tokens: int) -> bool:
"""检查配额是否足够"""
quota = self.quotas.get(model, {"remaining": float('inf'), "limit": float('inf')})
return quota.get("remaining", float('inf')) >= estimated_tokens
def _update_quota_usage(self, model: str, response: Dict):
"""更新配额使用情况"""
if "usage" in response:
used = response["usage"].get("completion_tokens", 0)
if model in self.quotas:
self.quotas[model]["remaining"] -= used
self.quotas[model]["used"] += used
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=2, max=10)
)
def _make_request_with_retry(
self,
endpoint: str,
payload: Dict[str, Any]
) -> Dict[str, Any]:
"""带指数退避重试的请求方法"""
url = f"{self.base_url}{endpoint}"
try:
response = self.session.post(url, json=payload, timeout=30)
# 5xx 错误重试
if response.status_code >= 500:
raise RetryableError(f"服务端错误: {response.status_code}")
# 429 限流重试
if response.status_code == 429:
retry_after = int(response.headers.get("Retry-After", 5))
time.sleep(retry_after)
raise RetryableError("限流触发")
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
raise RetryableError(f"请求失败: {str(e)}") from e
===== 使用示例 =====
初始化客户端
client = HolySheepMCPClient(
api_key="YOUR_HOLYSHEEP_API_KEY" # 替换为你的 Key
)
调用不同模型（统一接口，底层自动路由）
messages = [{"role": "user", "content": "解释什么是 MCP 工具市场"}]
调用 GPT-4.1
result_gpt = client.chat_completions(
model="gpt-4.1",
messages=messages
)
print(f"GPT-4.1 响应: {result_gpt['choices'][0]['message']['content']}")
调用 Claude Sonnet 4.5
result_claude = client.chat_completions(
model="claude-sonnet-4.5",
messages=messages
)
print(f"Claude 响应: {result_claude['choices'][0]['message']['content']}")
调用 DeepSeek V3.2（性价比最高）
result_deepseek = client.chat_completions(
model="deepseek-v3.2",
messages=messages
)
print(f"DeepSeek 响应: {result_deepseek['choices'][0]['message']['content']}")模型路由策略：智能选择最优路径
在实际生产环境中，不同任务类型应该路由到不同模型以达到性价比最优。下面展示一个智能路由器的实现：
"""
HolySheep MCP 智能路由策略
根据任务类型自动选择最优模型
"""
from enum import Enum
from dataclasses import dataclass
from typing import Callable, Dict, Optional
import hashlib
class TaskType(Enum):
CODE_GENERATION = "code" # 代码生成
COMPLEX_REASONING = "reason" # 复杂推理
FAST_RESPONSE = "fast" # 快速响应
COST_SENSITIVE = "cost" # 成本敏感
GENERAL = "general" # 通用对话
@dataclass
class RouteConfig:
"""路由配置"""
task_type: TaskType
model: str
temperature: float
max_tokens: int
fallback_models: list
class SmartRouter:
"""智能模型路由器"""
# 默认路由策略（可根据业务调整）
DEFAULT_STRATEGY = {
TaskType.CODE_GENERATION: RouteConfig(
task_type=TaskType.CODE_GENERATION,
model="gpt-4.1",
temperature=0.2,
max_tokens=4096,
fallback_models=["claude-sonnet-4.5", "deepseek-v3.2"]
),
TaskType.COMPLEX_REASONING: RouteConfig(
task_type=TaskType.COMPLEX_REASONING,
model="claude-sonnet-4.5",
temperature=0.3,
max_tokens=8192,
fallback_models=["gpt-4.1"]
),
TaskType.FAST_RESPONSE: RouteConfig(
task_type=TaskType.FAST_RESPONSE,
model="gemini-2.5-flash",
temperature=0.7,
max_tokens=2048,
fallback_models=["deepseek-v3.2"]
),
TaskType.COST_SENSITIVE: RouteConfig(
task_type=TaskType.COST_SENSITIVE,
model="deepseek-v3.2",
temperature=0.5,
max_tokens=4096,
fallback_models=["gemini-2.5-flash"]
),
TaskType.GENERAL: RouteConfig(
task_type=TaskType.GENERAL,
model="gemini-2.5-flash",
temperature=0.7,
max_tokens=2048,
fallback_models=["gpt-4.1", "claude-sonnet-4.5"]
),
}
def __init__(self, client: HolySheepMCPClient):
self.client = client
self.strategy = self.DEFAULT_STRATEGY.copy()
# 路由统计
self.stats: Dict[str, Dict] = {}
def classify_task(self, prompt: str) -> TaskType:
"""基于关键词的任务分类"""
prompt_lower = prompt.lower()
# 代码相关关键词
code_keywords = ["代码", "function", "class", "def ", "import ",
"implement", "algorithm", "api", "endpoint"]
if any(kw in prompt_lower for kw in code_keywords):
return TaskType.CODE_GENERATION
# 推理相关关键词
reason_keywords = ["分析", "推理", "证明", "为什么", "逻辑",
"analyze", "reasoning", "prove", "logic"]
if any(kw in prompt_lower for kw in reason_keywords):
return TaskType.COMPLEX_REASONING
# 成本敏感标识
cost_keywords = ["便宜", "简单", "快速", "简短", "cost", "cheap", "simple"]
if any(kw in prompt_lower for kw in cost_keywords):
return TaskType.COST_SENSITIVE
# 快速响应标识
fast_keywords = ["快", "速度", "quick", "fast", "instant"]
if any(kw in prompt_lower for kw in fast_keywords):
return TaskType.FAST_RESPONSE
return TaskType.GENERAL
def route(self, prompt: str, messages: list) -> Dict:
"""执行路由并调用"""
task_type = self.classify_task(prompt)
config = self.strategy.get(task_type)
if not config:
config = self.strategy[TaskType.GENERAL]
# 尝试主模型，失败则降级
models_to_try = [config.model] + config.fallback_models
last_error = None
for model in models_to_try:
try:
result = self.client.chat_completions(
model=model,
messages=messages,
temperature=config.temperature,
max_tokens=config.max_tokens
)
# 记录路由统计
self._record_route(task_type, model, success=True)
return {
"result": result,
"model_used": model,
"task_type": task_type.value,
"fallback_used": model != config.model
}
except Exception as e:
last_error = e
self._record_route(task_type, model, success=False, error=str(e))
continue
# 所有模型都失败
raise RuntimeError(f"所有模型路由失败，最后错误: {last_error}")
def _record_route(self, task_type: TaskType, model: str, success: bool, error: str = None):
"""记录路由统计"""
key = f"{task_type.value}:{model}"
if key not in self.stats:
self.stats[key] = {"total": 0, "success": 0, "failed": 0}
self.stats[key]["total"] += 1
if success:
self.stats[key]["success"] += 1
else:
self.stats[key]["failed"] += 1
===== 使用示例 =====
router = SmartRouter(client)
自动路由示例
test_prompts = [
"帮我写一个 Python 的快速排序算法",
"分析一下为什么比特币价格最近波动这么大",
"用最便宜的方式回答：今天天气怎么样",
"快速回复：你好"
]
for prompt in test_prompts:
messages = [{"role": "user", "content": prompt}]
result = router.route(prompt, messages)
print(f"\nPrompt: {prompt}")
print(f" 任务类型: {result['task_type']}")
print(f" 使用模型: {result['model_used']}")
print(f" 是否降级: {result['fallback_used']}")配额治理：企业级用量管控
对于企业用户，配额治理是生产环境的核心需求。HolySheep MCP 提供了多层次的配额管理能力：
"""
HolySheep MCP 配额治理系统
支持多租户、配额分配、熔断降级
"""
import time
import threading
from collections import defaultdict
from dataclasses import dataclass, field
from typing import Dict, Optional, List
from enum import Enum
import heapq
class QuotaStatus(Enum):
NORMAL = "normal"
WARNING = "warning" # 使用超过 80%
CRITICAL = "critical" # 使用超过 95%
EXHAUSTED = "exhausted" # 配额耗尽
@dataclass
class TenantQuota:
"""租户配额配置"""
tenant_id: str
daily_limit: int # 每日 Token 限制
monthly_limit: int # 每月 Token 限制
model_limits: Dict[str, int] = field(default_factory=dict) # 各模型限制
rate_limit: int = 100 # 每分钟请求数限制
@dataclass
class QuotaState:
"""配额状态追踪"""
daily_used: int = 0
monthly_used: int = 0
model_used: Dict[str, int] = field(default_factory=dict)
minute_requests: int = 0
last_reset_minute: int = 0
status: QuotaStatus = QuotaStatus.NORMAL
class QuotaManager:
"""企业级配额管理器"""
def __init__(self):
self.tenants: Dict[str, TenantQuota] = {}
self.states: Dict[str, QuotaState] = {}
self.locks: Dict[str, threading.Lock] = defaultdict(threading.Lock)
# 全局限流配置
self.global_rate_limit = 10000 # 全局每分钟 10000 请求
self.global_used_this_minute = 0
self.global_lock = threading.Lock()
def register_tenant(self, config: TenantQuota):
"""注册租户配额"""
self.tenants[config.tenant_id] = config
self.states[config.tenant_id] = QuotaState()
def check_and_consume(
self,
tenant_id: str,
model: str,
tokens: int
) -> tuple[bool, Optional[str], QuotaStatus]:
"""
检查配额并消费
Returns:
(是否允许, 拒绝原因, 当前状态)
"""
if tenant_id not in self.tenants:
return False, "租户未注册", QuotaStatus.EXHAUSTED
config = self.tenants[tenant_id]
state = self.states[tenant_id]
with self.locks[tenant_id]:
current_minute = int(time.time() // 60)
# 重置分钟计数器
if state.last_reset_minute != current_minute:
state.minute_requests = 0
state.last_reset_minute = current_minute
# 1. 检查全局限流
with self.global_lock:
if self.global_used_this_minute >= self.global_rate_limit:
return False, "全局限流中", QuotaStatus.CRITICAL
# 2. 检查分钟请求数限制
if state.minute_requests >= config.rate_limit:
return False, f"请求过于频繁，限制 {config.rate_limit}/分钟", QuotaStatus.CRITICAL
# 3. 检查日限额
if state.daily_used + tokens > config.daily_limit:
return False, f"日限额 {config.daily_limit} 已达", QuotaStatus.EXHAUSTED
# 4. 检查月限额
if state.monthly_used + tokens > config.monthly_limit:
return False, f"月限额 {config.monthly_limit} 已达", QuotaStatus.EXHAUSTED
# 5. 检查模型专属限额
if model in config.model_limits:
model_used = state.model_used.get(model, 0)
if model_used + tokens > config.model_limits[model]:
return False, f"模型 {model} 限额已用完", QuotaStatus.EXHAUSTED
# 通过检查，消耗配额
state.daily_used += tokens
state.monthly_used += tokens
state.model_used[model] = state.model_used.get(model, 0) + tokens
state.minute_requests += 1
# 更新状态
self._update_status(config, state)
return True, None, state.status
def _update_status(self, config: TenantQuota, state: QuotaState):
"""更新配额状态"""
daily_ratio = state.daily_used / config.daily_limit if config.daily_limit else 0
if daily_ratio >= 0.95:
state.status = QuotaStatus.EXHAUSTED
elif daily_ratio >= 0.80:
state.status = QuotaStatus.CRITICAL
elif daily_ratio >= 0.60:
state.status = QuotaStatus.WARNING
else:
state.status = QuotaStatus.NORMAL
def get_tenant_status(self, tenant_id: str) -> Optional[Dict]:
"""获取租户配额状态"""
if tenant_id not in self.tenants:
return None
config = self.tenants[tenant_id]
state = self.states[tenant_id]
return {
"tenant_id": tenant_id,
"daily_used": state.daily_used,
"daily_limit": config.daily_limit,
"daily_remaining": config.daily_limit - state.daily_used,
"daily_usage_pct": round(state.daily_used / config.daily_limit * 100, 2),
"monthly_used": state.monthly_used,
"monthly_limit": config.monthly_limit,
"monthly_remaining": config.monthly_limit - state.monthly_used,
"status": state.status.value,
"minute_requests": state.minute_requests,
"rate_limit": config.rate_limit,
"model_usage": dict(state.model_used)
}
def熔断降级(self, tenant_id: str) -> bool:
"""触发熔断降级"""
state = self.states.get(tenant_id)
if not state:
return False
# 使用率超过 95% 或失败率过高时熔断
config = self.tenants[tenant_id]
daily_ratio = state.daily_used / config.daily_limit if config.daily_limit else 0
if daily_ratio >= 0.95:
state.status = QuotaStatus.EXHAUSTED
return True
return False
===== 使用示例 =====
quota_manager = QuotaManager()
注册企业租户
quota_manager.register_tenant(TenantQuota(
tenant_id="enterprise_001",
daily_limit=10_000_000, # 日限额 1000 万 Token
monthly_limit=300_000_000, # 月限额 3 亿 Token
model_limits={
"gpt-4.1": 50_000_000, # GPT 限额 5000 万
"claude-sonnet-4.5": 30_000_000, # Claude 限额 3000 万
"deepseek-v3.2": 200_000_000 # DeepSeek 限额 2 亿
},
rate_limit=500 # 每分钟 500 请求
))
检查配额
allowed, reason, status = quota_manager.check_and_consume(
tenant_id="enterprise_001",
model="gpt-4.1",
tokens=5000
)
print(f"配额检查: 允许={allowed}, 原因={reason}, 状态={status}")
获取状态
status_info = quota_manager.get_tenant_status("enterprise_001")
print(f"配额状态: {status_info}")失败重试机制：指数退避 + JIT 抖动
网络不稳定和后端服务波动是不可避免的。HolySheep MCP 推荐使用指数退避 + JIT（准时制）抖动来避免惊群效应：
"""
HolySheep MCP 失败重试策略
指数退避 + JIT 抖动实现
"""
import time
import random
import threading
from functools import wraps
from typing import Callable, TypeVar, ParamSpec
from enum import Enum
P = ParamSpec('P')
T = TypeVar('T')
class RetryableError(Enum):
"""可重试的错误类型"""
TIMEOUT = "timeout"
CONNECTION_ERROR = "connection"
SERVER_ERROR = "server_error" # 5xx
RATE_LIMIT = "rate_limit" # 429
SERVICE_UNAVAILABLE = "unavailable"
class HolySheepRetryer:
"""HolySheep 专用重试器"""
def __init__(
self,
base_delay: float = 1.0,
max_delay: float = 60.0,
max_attempts: int = 5,
jitter_range: tuple = (0.5, 1.5)
):
self.base_delay = base_delay
self.max_delay = max_delay
self.max_attempts = max_attempts
self.jitter_range = jitter_range
# 熔断器
self.circuit_breaker = CircuitBreaker(failure_threshold=10, timeout=60)
def calculate_delay(self, attempt: int) -> float:
"""计算带 Jitter 的指数退避延迟"""
# 基础指数退避
exponential_delay = self.base_delay * (2 ** attempt)
# JIT 抖动（在基础值的 50%-150% 之间随机）
jitter_factor = random.uniform(*self.jitter_range)
# 最终延迟
delay = min(exponential_delay * jitter_factor, self.max_delay)
return delay
def is_retryable(self, error: Exception, response: any = None) -> bool:
"""判断错误是否可重试"""
error_str = str(error).lower()
# 网络错误
if any(kw in error_str for kw in ["timeout", "connection", "network"]):
return True
# HTTP 5xx 错误
if response and hasattr(response, 'status_code'):
if 500 <= response.status_code < 600:
return True
# 429 限流（带 Retry-After 头）
if response.status_code == 429:
return True
return False
def execute_with_retry(
self,
func: Callable[P, T],
*args: P.args,
**kwargs: P.kwargs
) -> T:
"""执行带重试的调用"""
last_exception = None
for attempt in range(self.max_attempts):
try:
# 检查熔断器
if self.circuit_breaker.is_open():
raise CircuitOpenError(
f"熔断器开启，请 {self.circuit_breaker.seconds_until_open()} 秒后重试"
)
result = func(*args, **kwargs)
# 成功时重置熔断器
self.circuit_breaker.record_success()
return result
except Exception as e:
last_exception = e
# 不可重试的错误直接抛出
if not self.is_retryable(e):
self.circuit_breaker.record_failure()
raise
# 记录失败
self.circuit_breaker.record_failure()
# 非最后一次尝试则等待
if attempt < self.max_attempts - 1:
delay = self.calculate_delay(attempt)
print(f"重试 {attempt + 1}/{self.max_attempts}, "
f"等待 {delay:.2f}s, 错误: {e}")
time.sleep(delay)
# 所有重试都失败
raise MaxRetriesExceededError(
f"已达最大重试次数 {self.max_attempts}, 最后错误: {last_exception}"
)
class CircuitBreaker:
"""熔断器实现"""
def __init__(self, failure_threshold: int = 5, timeout: int = 60):
self.failure_threshold = failure_threshold
self.timeout = timeout
self.failure_count = 0
self.last_failure_time = 0
self.state = "closed" # closed, open, half_open
self.lock = threading.Lock()
def is_open(self) -> bool:
"""检查熔断器是否开启"""
with self.lock:
if self.state == "open":
# 检查是否超时可以进入半开状态
if time.time() - self.last_failure_time >= self.timeout:
self.state = "half_open"
return False
return True
return False
def record_success(self):
"""记录成功调用"""
with self.lock:
self.failure_count = 0
self.state = "closed"
def record_failure(self):
"""记录失败调用"""
with self.lock:
self.failure_count += 1
self.last_failure_time = time.time()
if self.failure_count >= self.failure_threshold:
self.state = "open"
def seconds_until_open(self) -> int:
"""距离熔断器关闭的秒数"""
with self.lock:
elapsed = time.time() - self.last_failure_time
return max(0, int(self.timeout - elapsed))
===== 使用示例 =====
retryer = HolySheepRetryer(
base_delay=1.0,
max_delay=30.0,
max_attempts=3,
jitter_range=(0.8, 1.2)
)
def call_holysheep_api(messages):
"""调用 HolySheep API 的示例函数"""
client = HolySheepMCPClient(api_key="YOUR_HOLYSHEEP_API_KEY")
return client.chat_completions(
model="gpt-4.1",
messages=messages,
max_tokens=1000
)
带重试的调用
messages = [{"role": "user", "content": "你好"}]
try:
result = retryer.execute_with_retry(call_holysheep_api, messages)
print(f"成功: {result['choices'][0]['message']['content']}")
except Exception as e:
print(f"最终失败: {e}")常见报错排查
1. 认证失败：401 Unauthorized
错误信息：
{
"error": {
"message": "Incorrect API key provided",
"type": "invalid_request_error",
"code": "invalid_api_key"
}
}原因分析：
- API Key 拼写错误或格式不对
- 使用了其他平台的 Key（如直接复制了 OpenAI 的 Key）
- Key 已过期或被禁用
解决方案：
# 1. 检查 Key 格式（必须以 sk-hs- 开头）
API_KEY = "sk-hs-your-key-here" # 正确格式
2. 验证 Key 是否正确
import requests
response = requests.get(
"https://api.holysheep.ai/v1/models",
headers={"Authorization": f"Bearer {API_KEY}"}
)
print(response.status_code)
200 = Key 有效
401 = Key 无效
3. 如 Key 无效，请到控制台重新生成
https://www.holysheep.ai/dashboard/api-keys
2. 限流错误：429 Rate Limit Exceeded
错误信息：
{
"error": {
"message": "Rate limit exceeded for model gpt-4.1",