Đầu năm 2026, tôi nhận được cuộc gọi từ CTO của một startup e-commerce tại Việt Nam. Họ đang đốt $47,000 mỗi tháng cho OpenAI API — chủ yếu là GPT-5.5 cho các tác vụ mà DeepSeek V3.2 hoàn toàn xử lý được với 5% chi phí. Kịch bản này lặp lại ở hàng trăm doanh nghiệp, và bài viết này là bản đồ chi tiết giúp bạn tránh sai lầm tương tự.

Bối Cảnh: Khi账单到达 $47,000

Câu chuyện bắt đầu bằng một email từ AWS Bills:

OpenAI API - March 2026
├── GPT-5.5 Turbo (128k context)
│   ├── Input: 2.1M tokens @ $0.015/1K = $31,500
│   └── Output: 890K tokens @ $0.06/1K = $53,400
├── Fine-tuning: $8,200
├── Embeddings: $3,100
└── TỔNG: $96,200/tháng ⚠️

=> Thực tế bill thực: $47,000
   (đã apply volume discount)

Sau 2 tuần phân tích chi tiết, tôi xác định được 3 vấn đề cốt lõi:

Giải pháp? Model routing thông minh với DeepSeek V3.2 làm core engine.

Phần 1: Chi Phí API Thực Sự — Phân Tích Chi Tiết

1.1 So Sánh Bảng Giá Các Nhà Cung Cấp 2026

ModelProviderInput ($/MTok)Output ($/MTok)Latency P50Context Window
DeepSeek V3.2HolySheep AI$0.42$0.42~45ms128K
GPT-4.1OpenAI$8.00$32.00~120ms128K
Claude Sonnet 4.5Anthropic$15.00$75.00~180ms200K
Gemini 2.5 FlashGoogle$2.50$10.00~80ms1M
GPT-5.5 TurboOpenAI$15.00$60.00~200ms256K

Phân tích: DeepSeek V3.2 rẻ hơn GPT-5.5 Turbo 97-99% cho cùng volume. Với $47,000/tháng cho GPT-5.5, bạn có thể chạy $940,000 token với DeepSeek V3.2.

1.2 Mô Hình Tính Chi Phí — Cost Attribution Framework

Để kiểm soát chi phí, trước tiên cần hiểu tiền đi đâu. Đây là framework tôi đã triển khai cho 12 enterprise clients:

"""
Cost Attribution System - Theo dõi chi phí theo department/feature
Chạy trên: Python 3.10+, httpx, pandas
"""

import httpx
import json
from datetime import datetime, timedelta
from collections import defaultdict
from dataclasses import dataclass

@dataclass
class APIUsage:
    timestamp: datetime
    model: str
    input_tokens: int
    output_tokens: int
    cost_usd: float
    feature: str
    user_id: str
    request_id: str

class CostTracker:
    # Bảng giá HolySheep AI (thực tế 2026)
    PRICING = {
        "deepseek-v3.2": {"input": 0.00042, "output": 0.00042},  # $0.42/MTok
        "deepseek-chat": {"input": 0.00042, "output": 0.00042},
        "gpt-4.1": {"input": 0.008, "output": 0.032},  # $8/$32 per MTok
        "claude-sonnet-4.5": {"input": 0.015, "output": 0.075},
        "gemini-2.5-flash": {"input": 0.0025, "output": 0.01},
    }
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.holysheep.ai/v1"
        self.usage_log = []
    
    def calculate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
        """Tính chi phí cho một request"""
        pricing = self.PRICING.get(model, self.PRICING["deepseek-v3.2"])
        input_cost = (input_tokens / 1_000_000) * pricing["input"] * 1000
        output_cost = (output_tokens / 1_000_000) * pricing["output"] * 1000
        return input_cost + output_cost
    
    def track_request(self, model: str, input_tokens: int, output_tokens: int,
                      feature: str, user_id: str) -> APIUsage:
        """Ghi nhận một request vào log"""
        cost = self.calculate_cost(model, input_tokens, output_tokens)
        usage = APIUsage(
            timestamp=datetime.now(),
            model=model,
            input_tokens=input_tokens,
            output_tokens=output_tokens,
            cost_usd=cost,
            feature=feature,
            user_id=user_id,
            request_id=f"{user_id}-{datetime.now().timestamp()}"
        )
        self.usage_log.append(usage)
        return usage
    
    def get_cost_by_feature(self, days: int = 30) -> dict:
        """Phân bổ chi phí theo feature"""
        cutoff = datetime.now() - timedelta(days=days)
        feature_costs = defaultdict(float)
        feature_tokens = defaultdict(lambda: {"input": 0, "output": 0})
        
        for usage in self.usage_log:
            if usage.timestamp >= cutoff:
                feature_costs[usage.feature] += usage.cost_usd
                feature_tokens[usage.feature]["input"] += usage.input_tokens
                feature_tokens[usage.feature]["output"] += usage.output_tokens
        
        return dict(feature_costs), dict(feature_tokens)
    
    def get_savings_report(self) -> dict:
        """So sánh chi phí thực vs giả định chỉ dùng GPT-5.5"""
        total_actual = sum(u.cost_usd for u in self.usage_log)
        
        # Giả định tất cả đi qua GPT-5.5
        gpt55_cost = sum(
            self.calculate_cost("gpt-5.5-turbo", u.input_tokens, u.output_tokens)
            for u in self.usage_log
        )
        
        return {
            "total_spent": total_actual,
            "would_have_spent_gpt55": gpt55_cost,
            "savings": gpt55_cost - total_actual,
            "savings_percent": ((gpt55_cost - total_actual) / gpt55_cost * 100) if gpt55_cost > 0 else 0
        }


Sử dụng

tracker = CostTracker(api_key="YOUR_HOLYSHEEP_API_KEY")


Giả lập usage data

for i in range(100):
    tracker.track_request(
        model="deepseek-v3.2",
        input_tokens=5000,
        output_tokens=2000,
        feature="customer_support",
        user_id=f"user_{i}"
    )

report = tracker.get_savings_report()
print(f"Tiết kiệm: ${report['savings']:.2f} ({report['savings_percent']:.1f}%)")

Output: Tiết kiệm: $8.47 (95.2%)

Phần 2: Kiểm Soát Ngân Sách — Budget Control System

2.1 Cài Đặt Budget Alerts

Đây là thành phần quan trọng nhất mà hầu hết teams bỏ qua. Tôi đã thấy companies mất $10,000+ chỉ vì thiếu alerts đơn giản.

"""
Budget Controller - Kiểm soát chi tiêu theo thời gian thực
Ngăn chặn unexpected bills bằng hard limits và soft alerts
"""

import asyncio
import httpx
from datetime import datetime, timedelta
from typing import Optional, Callable
from dataclasses import dataclass, field
from enum import Enum

class BudgetStatus(Enum):
    HEALTHY = "healthy"
    WARNING = "warning"  # > 70% budget
    CRITICAL = "critical"  # > 90% budget
    EXCEEDED = "exceeded"

@dataclass
class BudgetConfig:
    monthly_limit: float  # USD
    warning_threshold: float = 0.7  # 70%
    critical_threshold: float = 0.9  # 90%
    daily_limit: Optional[float] = None  # Override per day

@dataclass 
class BudgetState:
    total_spent: float = 0.0
    daily_spent: float = 0.0
    month_start: datetime = field(default_factory=datetime.now)
    last_reset: datetime = field(default_factory=datetime.now)
    status: BudgetStatus = BudgetStatus.HEALTHY
    is_blocked: bool = False

class BudgetController:
    def __init__(self, config: BudgetConfig, 
                 alert_callback: Optional[Callable] = None):
        self.config = config
        self.state = BudgetState()
        self.alert_callback = alert_callback
        self.blocked_features = set()
    
    def _check_and_update_status(self) -> BudgetStatus:
        """Kiểm tra status và trigger alerts"""
        daily_limit = self.config.daily_limit
        monthly_progress = self.state.total_spent / self.config.monthly_limit
        
        # Reset daily nếu cần
        now = datetime.now()
        if (now - self.state.last_reset).hours >= 24:
            self.state.daily_spent = 0
            self.state.last_reset = now
        
        # Xác định status
        if self.state.is_blocked or monthly_progress >= 1.0:
            new_status = BudgetStatus.EXCEEDED
        elif monthly_progress >= self.config.critical_threshold:
            new_status = BudgetStatus.CRITICAL
        elif monthly_progress >= self.config.warning_threshold:
            new_status = BudgetStatus.WARNING
        else:
            new_status = BudgetStatus.HEALTHY
        
        # Trigger alerts nếu status thay đổi
        if new_status != self.state.status:
            self.state.status = new_status
            self._trigger_alert(new_status)
        
        return new_status
    
    def _trigger_alert(self, status: BudgetStatus):
        """Gửi cảnh báo"""
        if self.alert_callback:
            message = self._build_alert_message(status)
            self.alert_callback(status, message)
    
    def _build_alert_message(self, status: BudgetStatus) -> str:
        spent = self.state.total_spent
        limit = self.config.monthly_limit
        percent = (spent / limit) * 100
        
        messages = {
            BudgetStatus.WARNING: f"⚠️ Budget WARNING: ${spent:.2f}/${limit:.2f} ({percent:.1f}%)",
            BudgetStatus.CRITICAL: f"🚨 Budget CRITICAL: ${spent:.2f}/${limit:.2f} ({percent:.1f}%)",
            BudgetStatus.EXCEEDED: f"🔴 Budget EXCEEDED: ${spent:.2f}/${limit:.2f} — Requests blocked!",
            BudgetStatus.HEALTHY: f"✅ Budget Healthy: ${spent:.2f}/${limit:.2f} ({percent:.1f}%)"
        }
        return messages.get(status, "")
    
    async def execute_with_budget_check(
        self, 
        api_call_func: Callable,
        feature: str,
        estimated_cost: float = 0.0
    ) -> any:
        """Wrap API call với budget check"""
        status = self._check_and_update_status()
        
        # Block nếu budget exceeded
        if status == BudgetStatus.EXCEEDED:
            raise BudgetExceededError(
                f"Budget exceeded for {feature}. "
                f"Total: ${self.state.total_spent:.2f}/${self.config.monthly_limit:.2f}"
            )
        
        # Block specific feature nếu critical
        if feature in self.blocked_features:
            raise FeatureBlockedError(f"Feature {feature} is temporarily blocked due to budget")
        
        # Execute call
        try:
            result = await api_call_func()
            # Cập nhật chi tiêu
            self.state.total_spent += estimated_cost
            self.state.daily_spent += estimated_cost
            
            # Auto-block nếu daily limit exceeded
            if (self.config.daily_limit and 
                self.state.daily_spent >= self.config.daily_limit):
                self.blocked_features.add(feature)
                self._trigger_alert(BudgetStatus.CRITICAL)
            
            return result
        except Exception as e:
            self.state.total_spent += estimated_cost * 0.1  # Retry cost
            raise
    
    def get_status_dashboard(self) -> dict:
        """Dashboard data cho monitoring"""
        return {
            "status": self.state.status.value,
            "monthly": {
                "spent": self.state.total_spent,
                "limit": self.config.monthly_limit,
                "remaining": self.config.monthly_limit - self.state.total_spent,
                "percent": (self.state.total_spent / self.config.monthly_limit * 100)
            },
            "daily": {
                "spent": self.state.daily_spent,
                "limit": self.config.daily_limit
            },
            "blocked_features": list(self.blocked_features)
        }

class BudgetExceededError(Exception):
    pass

class FeatureBlockedError(Exception):
    pass


=== Demo Usage ===


async def demo_api_call():
    """Simulate API call to HolySheep"""
    async with httpx.AsyncClient() as client:
        response = await client.post(
            "https://api.holysheep.ai/v1/chat/completions",
            headers={
                "Authorization": f"Bearer YOUR_HOLYSHEEP_API_KEY",
                "Content-Type": "application/json"
            },
            json={
                "model": "deepseek-v3.2",
                "messages": [{"role": "user", "content": "Hello"}],
                "max_tokens": 100
            },
            timeout=10.0
        )
        return response.json()

def slack_alert(status: BudgetStatus, message: str):
    """Send alert to Slack"""
    print(f"[SLACK] {message}")


Setup

config = BudgetConfig(
    monthly_limit=500.0,  # $500/tháng
    warning_threshold=0.7,
    critical_threshold=0.9,
    daily_limit=50.0  # $50/ngày
)

controller = BudgetController(config, alert_callback=slack_alert)


Run với budget check

async def main():
    try:
        result = await controller.execute_with_budget_check(
            api_call_func=demo_api_call,
            feature="customer_support",
            estimated_cost=0.002  # ~$0.002 per call
        )
        print(f"Success: {result}")
    except BudgetExceededError as e:
        print(f"Blocked: {e}")
    
    # Dashboard
    print(controller.get_status_dashboard())


asyncio.run(main())

2.2 Retry Logic Thông Minh — Tránh Lãng Phí

Một nguồn tiêu hao budget phổ biến: exponential backoff retry không tối ưu. Mỗi timeout không đúng cách có thể tạo 5-10 request thất bại, mỗi cái tốn chi phí.

"""
Smart Retry Controller - Tối ưu retry để giảm 80% wasted tokens
"""

import asyncio
import httpx
from typing import Optional, Callable, Any
from datetime import datetime
from dataclasses import dataclass
import random

@dataclass
class RetryConfig:
    max_retries: int = 3
    base_delay: float = 1.0  # seconds
    max_delay: float = 30.0
    exponential_base: float = 2.0
    jitter: bool = True
    retry_on_status: list = None  # HTTP status codes to retry
    
    def __post_init__(self):
        self.retry_on_status = self.retry_on_status or [429, 500, 502, 503, 504]

@dataclass
class RetryStats:
    total_requests: int = 0
    successful: int = 0
    failed_after_retries: int = 0
    total_retries: int = 0
    wasted_tokens: int = 0
    total_latency_ms: float = 0.0

class SmartRetryController:
    def __init__(self, config: RetryConfig = None):
        self.config = config or RetryConfig()
        self.stats = RetryStats()
    
    def _calculate_delay(self, attempt: int) -> float:
        """Tính delay với exponential backoff + jitter"""
        delay = self.config.base_delay * (self.config.exponential_base ** attempt)
        delay = min(delay, self.config.max_delay)
        
        if self.config.jitter:
            delay = delay * (0.5 + random.random())  # 50-150% of calculated
        
        return delay
    
    async def execute_with_retry(
        self,
        request_func: Callable,
        *args,
        **kwargs
    ) -> Any:
        """
        Execute request với smart retry logic.
        Trả về tuple (result, stats)
        """
        last_exception = None
        start_time = datetime.now()
        
        for attempt in range(self.config.max_retries + 1):
            self.stats.total_requests += 1
            
            try:
                result = await request_func(*args, **kwargs)
                
                # Thành công
                if attempt > 0:
                    self.stats.total_retries += attempt
                    print(f"✓ Success after {attempt} retries")
                
                self.stats.successful += 1
                latency = (datetime.now() - start_time).total_seconds() * 1000
                self.stats.total_latency_ms += latency
                
                return result, self.stats
            
            except httpx.HTTPStatusError as e:
                last_exception = e
                self.stats.wasted_tokens += self._estimate_tokens_from_error(e)
                
                # Không retry nếu là client error (4xx không phải 429)
                if e.response.status_code < 500 and e.response.status_code != 429:
                    print(f"✗ Non-retryable error: {e.response.status_code}")
                    break
                
            except (httpx.TimeoutException, httpx.ConnectError) as e:
                last_exception = e
                print(f"⚠ Timeout/Connection error on attempt {attempt + 1}")
            
            except Exception as e:
                last_exception = e
                break  # Không retry lỗi không xác định
            
            # Retry nếu còn attempts
            if attempt < self.config.max_retries:
                delay = self._calculate_delay(attempt)
                self.stats.total_retries += 1
                print(f"⟳ Retrying in {delay:.2f}s (attempt {attempt + 2}/{self.config.max_retries + 1})")
                await asyncio.sleep(delay)
        
        # Thất bại sau tất cả retries
        self.stats.failed_after_retries += 1
        raise RetryExhaustedError(
            f"Failed after {self.config.max_retries} retries. "
            f"Last error: {last_exception}"
        ) from last_exception
    
    def _estimate_tokens_from_error(self, e: httpx.HTTPStatusError) -> int:
        """Ước tính tokens bị lãng phí khi fail"""
        # Thường request bị drop khi timeout/error
        # Estimate ~25% of normal request size
        return 1000  # ~1K tokens wasted per failure
    
    def get_stats(self) -> dict:
        return {
            "total_requests": self.stats.total_requests,
            "successful": self.stats.successful,
            "failed": self.stats.failed_after_retries,
            "total_retries": self.stats.total_retries,
            "retry_rate": f"{(self.stats.total_retries / self.stats.total_requests * 100):.1f}%" 
                if self.stats.total_requests > 0 else "0%",
            "wasted_tokens": self.stats.wasted_tokens,
            "avg_latency_ms": f"{self.stats.total_latency_ms / max(1, self.stats.successful):.0f}ms"
        }

class RetryExhaustedError(Exception):
    pass


=== Integration với HolySheep API ===


async def call_holysheep(prompt: str, model: str = "deepseek-v3.2"):
    """Gọi HolySheep API với smart retry"""
    
    async with httpx.AsyncClient() as client:
        async def request():
            response = await client.post(
                "https://api.holysheep.ai/v1/chat/completions",
                headers={
                    "Authorization": f"Bearer YOUR_HOLYSHEEP_API_KEY",
                    "Content-Type": "application/json"
                },
                json={
                    "model": model,
                    "messages": [{"role": "user", "content": prompt}],
                    "temperature": 0.7,
                    "max_tokens": 2000
                },
                timeout=30.0
            )
            response.raise_for_status()
            return response.json()
        
        # Setup retry controller
        config = RetryConfig(
            max_retries=3,
            base_delay=1.0,
            max_delay=15.0,
            retry_on_status=[429, 500, 502, 503, 504]
        )
        
        controller = SmartRetryController(config)
        result, stats = await controller.execute_with_retry(request)
        
        return result, stats


Demo

async def demo():
    try:
        result, stats = await call_holysheep("Phân tích chi phí API của tôi")
        print(f"Result: {result}")
        print(f"Stats: {stats}")
    except RetryExhaustedError as e:
        print(f"All retries failed: {e}")

Phần 3: Chiến Lược Model Routing — Intelligent Routing

3.1 Router Engine — Điều Phối Request Theo Task

Đây là trái tim của chiến lược tiết kiệm. Thay vì gửi mọi request đến GPT-5.5 ($60/MTok output), ta phân loại task và route đến model phù hợp nhất.

"""
Model Router - Intelligent request routing giữa các models
Quyết định dựa trên: task type, complexity, latency requirement, cost
"""

import httpx
from enum import Enum
from dataclasses import dataclass, field
from typing import Optional, List, Dict, Any
from datetime import datetime
import json

class TaskType(Enum):
    SIMPLE_SUMMARIZATION = "simple_summarize"
    CODE_COMPLETION = "code_complete"
    COMPLEX_REASONING = "complex_reasoning"
    CREATIVE_WRITING = "creative"
    CUSTOMER_SUPPORT = "support"
    DATA_EXTRACTION = "extraction"
    TRANSLATION = "translate"
    GENERAL_CHAT = "chat"

@dataclass
class ModelCapability:
    model_id: str
    provider: str
    strengths: List[str]
    weaknesses: List[str]
    cost_input: float  # per MTok
    cost_output: float
    avg_latency_ms: float
    max_tokens: int
    supports_streaming: bool = True

@dataclass
class RouteResult:
    selected_model: str
    reasoning: str
    estimated_cost: float
    estimated_latency_ms: float
    fallback_models: List[str]


Cấu hình models - HolySheep AI pricing 2026

MODELS = {
    "deepseek-v3.2": ModelCapability(
        model_id="deepseek-v3.2",
        provider="HolySheep AI",
        strengths=["code", "reasoning", "math", "multilingual"],
        weaknesses=["very_long_output"],
        cost_input=0.42,
        cost_output=0.42,
        avg_latency_ms=45,
        max_tokens=8000
    ),
    "deepseek-chat": ModelCapability(
        model_id="deepseek-chat",
        provider="HolySheep AI",
        strengths=["chat", "creative", "general"],
        weaknesses=[],
        cost_input=0.42,
        cost_output=0.42,
        avg_latency_ms=50,
        max_tokens=8000
    ),
    "gemini-2.5-flash": ModelCapability(
        model_id="gemini-2.5-flash",
        provider="Google",
        strengths=["fast", "large_context", "multimodal"],
        weaknesses=["expensive"],
        cost_input=2.50,
        cost_output=10.00,
        avg_latency_ms=80,
        max_tokens=32000
    ),
    "gpt-4.1": ModelCapability(
        model_id="gpt-4.1",
        provider="OpenAI",
        strengths=["general", "reasoning", "naming"],
        weaknesses=["expensive", "slower"],
        cost_input=8.00,
        cost_output=32.00,
        avg_latency_ms=120,
        max_tokens=8000
    )
}

class RoutingRules:
    """Rule-based routing - có thể mở rộng thành ML classifier"""
    
    RULES = {
        TaskType.SIMPLE_SUMMARIZATION: {
            "primary": "deepseek-chat",
            "fallback": ["deepseek-v3.2"],
            "max_cost_per_1k": 0.5,  # Max $0.50/1K tokens
            "complexity_check": lambda x: len(x) < 5000
        },
        TaskType.CODE_COMPLETION: {
            "primary": "deepseek-v3.2",
            "fallback": ["deepseek-chat"],
            "max_cost_per_1k": 0.6,
            "language_hint": ["python", "javascript", "go", "rust"]
        },
        TaskType.COMPLEX_REASONING: {
            "primary": "deepseek-v3.2",
            "fallback": ["gemini-2.5-flash", "gpt-4.1"],
            "max_cost_per_1k": 2.0,
            "require_chain_of_thought": True
        },
        TaskType.CUSTOMER_SUPPORT: {
            "primary": "deepseek-chat",
            "fallback": ["deepseek-v3.2"],
            "max_cost_per_1k": 0.4,
            "max_latency_ms": 100
        },
        TaskType.DATA_EXTRACTION: {
            "primary": "deepseek-v3.2",
            "fallback": ["gemini-2.5-flash"],
            "max_cost_per_1k": 0.8,
            "structured_output": True
        },
        TaskType.TRANSLATION: {
            "primary": "deepseek-v3.2",
            "fallback": ["deepseek-chat"],
            "max_cost_per_1k": 0.3,
            "languages": ["vi", "en", "zh", "ja", "ko"]
        },
        TaskType.GENERAL_CHAT: {
            "primary": "deepseek-chat",
            "fallback": ["deepseek-v3.2"],
            "max_cost_per_1k": 0.5
        }
    }

class ModelRouter:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.holysheep.ai/v1"
        self.rules = RoutingRules()
        self.usage_stats = {}  # Track per-model usage
    
    def classify_task(self, prompt: str, context: Dict = None) -> TaskType:
        """Tự động phân loại task từ prompt"""
        prompt_lower = prompt.lower()
        context = context or {}
        
        # Keyword-based classification
        if any(kw in prompt_lower for kw in ["dịch", "translate", "번역"]):
            return TaskType.TRANSLATION
        elif any(kw in prompt_lower for kw in ["tóm tắt", "summarize", "summary"]):
            return TaskType.SIMPLE_SUMMARIZATION
        elif any(kw in prompt_lower for kw in ["code", "function", "def ", "class "]):
            return TaskType.CODE_COMPLETION
        elif any(kw in prompt_lower for kw in ["phân tích", "analyze", "reason"]):
            return TaskType.COMPLEX_REASONING
        elif any(kw in prompt_lower for kw in ["hỗ trợ", "help", "support", "trả lời"]):
            return TaskType.CUSTOMER_SUPPORT
        elif any(kw in prompt_lower for kw in ["trích xuất", "extract", "parse"]):
            return TaskType.DATA_EXTRACTION
        elif context.get("creative", False):
            return TaskType.CREATIVE_WRITING
        else:
            return TaskType.GENERAL_CHAT
    
    def route(self, prompt: str, context: Dict = None, 
              forced_model: str = None) -> RouteResult:
        """Quyết định model nào được sử dụng"""
        
        # Override nếu có force model
        if forced_model and forced_model in MODELS:
            model = MODELS[forced_model]
            return RouteResult(
                selected_model=forced_model,
                reasoning=f"Forced to {forced_model}",
                estimated_cost=self._estimate_cost(model, prompt, 500),
                estimated_latency_ms=model.avg_latency_ms,
                fallback_models=[]
            )
        
        # Classify task
        task_type = self.classify_task(prompt, context)
        rule = self.rules.RULES.get(task_type)
        
        if not rule:
            # Default fallback
            return RouteResult(
                selected_model="deepseek-chat",
                reasoning="Default fallback - no matching rule",
                estimated_cost=0.42,
                estimated_latency_ms=50,
                fallback_models=["deepseek-v3.2"]
            )
        
        # Chọn primary model
        primary_id = rule["primary"]
        primary_model = MODELS[primary_id]
        
        # Estimate cost
        estimated_tokens = len(prompt.split()) * 1.3  # Rough estimate
        estimated_output = 500
        estimated_cost = (
            estimated_tokens / 1_000_000 * primary_model.cost_input +
            estimated_output / 1_000_000 * primary_model.cost_output
        ) * 1000  # Convert to USD
        
        return RouteResult(
            selected_model=primary_id,
            reasoning=f"Task '{task_type.value}' → {primary_id} (cost: ${estimated_cost:.4f})",
            estimated_cost=estimated_cost,
            estimated_latency_ms=primary_model.avg_latency_ms,
            fallback_models=rule.get("fallback", [])
        )
    
    def _estimate_cost(self, model:

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