2026 aktuelle API-Preise im Vergleich
| Modell | Output-Preis ($/MTok) | 10M Token/Monat | Latenz |
|---|
| DeepSeek V3.2 | $0,42 | $4.200 | <50ms |
| Gemini 2.5 Flash | $2,50 | $25.000 | <80ms |
| GPT-4.1 | $8,00 | $80.000 | <120ms |
| Claude Sonnet 4.5 | $15,00 | $150.000 | <100ms |
Bei HolySheep erhalten Sie alle diese Modelle mit einem Wechselkurs von ¥1 = $1 — das bedeutet eine Ersparnis von über 85% gegenüber den offiziellen Preisen. Für ein Projekt mit 10 Millionen Token monatlich sparen Sie mit HolySheep bis zu $250.000.
Warum HolySheep wählen
- 85%+ Ersparnis durch chinesischen Wechselkurs
- <50ms Latenz für Echtzeitanwendungen
- WeChat/Alipay Zahlung für chinesische Teams
- Kostenlose Credits für den Start
- Native OpenAI-kompatibel — einfache Migration
Geeignet / nicht geeignet für
| Perfekt geeignet | Weniger geeignet |
|---|
| Enterprise-Anwendungen mit hohem Volumen | Kostenlose Projekte mit striktem Budget |
| Multi-Modell-Pipelines | Single-Request-Prototyping |
| Produktionssysteme mit SLA-Anforderungen | Spielprojekte ohne Latenzanforderungen |
| Chinesische Entwicklungsteams | Rein westliche Zahlungsinfrastruktur |
1. Basis-Client-Konfiguration mit HolySheep
# Installation der benötigten Pakete
pip install httpx tenacity prometheus-client
Konfiguration für HolySheep API
import os
from httpx import AsyncClient, Timeout
import asyncio
WICHTIG: base_url MUSS HolySheep sein, NICHT api.openai.com
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
class HolySheepAgent:
"""Production-ready Agent mit allen Enterprise-Features"""
def __init__(self, api_key: str = None):
self.base_url = HOLYSHEEP_BASE_URL
self.api_key = api_key or HOLYSHEEP_API_KEY
self.client = None
async def __aenter__(self):
self.client = AsyncClient(
base_url=self.base_url,
headers={"Authorization": f"Bearer {self.api_key}"},
timeout=Timeout(30.0, connect=5.0),
limits= httpx.Limits(max_keepalive_connections=20, max_connections=100)
)
return self
async def __aexit__(self, *args):
if self.client:
await self.client.aclose()
async def chat(self, model: str, messages: list, **kwargs):
"""Wrapper für ChatCompletions API"""
response = await self.client.post(
"/chat/completions",
json={"model": model, "messages": messages, **kwargs}
)
response.raise_for_status()
return response.json()
Verwendung
async def main():
async with HolySheepAgent() as agent:
result = await agent.chat(
model="deepseek-v3.2",
messages=[{"role": "user", "content": "Analysiere meine Verkaufsdaten"}]
)
print(result)
2. Intelligente Rate-Limiting-Implementierung
import asyncio
import time
from collections import deque
from dataclasses import dataclass, field
from typing import Dict, Optional
import threading
@dataclass
class RateLimiter:
"""Token-Bucket Algorithmus für präzises Rate-Limiting"""
requests_per_minute: int = 60
requests_per_second: int = 10
burst_size: int = 20
_request_times: deque = field(default_factory=deque)
_token_bucket: float = field(default=20.0)
_last_refill: float = field(default_factory=time.time)
_lock: threading.Lock = field(default_factory=threading.Lock)
# RPM-Limits pro Modell (HolySheep spezifisch)
MODEL_LIMITS: Dict[str, Dict] = field(default_factory=lambda: {
"deepseek-v3.2": {"rpm": 120, "tpm": 1_000_000},
"gpt-4.1": {"rpm": 500, "tpm": 2_000_000},
"claude-sonnet-4.5": {"rpm": 400, "tpm": 1_500_000},
"gemini-2.5-flash": {"rpm": 1000, "tpm": 5_000_000},
})
def __post_init__(self):
self._lock = threading.Lock()
def _refill_tokens(self):
"""Automatische Token-Auffüllung"""
now = time.time()
elapsed = now - self._last_refill
tokens_to_add = elapsed * (self.requests_per_second)
self._token_bucket = min(self.burst_size, self._token_bucket + tokens_to_add)
self._last_refill = now
def _clean_old_requests(self):
"""Entfernt Anfragen älter als 60 Sekunden"""
cutoff = time.time() - 60
while self._request_times and self._request_times[0] < cutoff:
self._request_times.popleft()
async def acquire(self, model: str = "deepseek-v3.2") -> bool:
"""Blockiert bis Rate-Limit verfügbar ist"""
model_limit = self.MODEL_LIMITS.get(model, {"rpm": 60, "tpm": 100_000})
while True:
with self._lock:
self._refill_tokens()
self._clean_old_requests()
current_rpm = len(self._request_times)
if (current_rpm < model_limit["rpm"] and
self._token_bucket >= 1):
self._request_times.append(time.time())
self._token_bucket -= 1
return True
# Exponential Backoff bei Limiterreach
await asyncio.sleep(0.1 * (1.5 ** min(current_rpm / 10, 5)))
def get_status(self) -> Dict:
"""Aktueller Status für Monitoring"""
with self._lock:
self._clean_old_requests()
return {
"current_rpm": len(self._request_times),
"available_tokens": round(self._token_bucket, 2),
"wait_time_ms": 100 if self._token_bucket < 1 else 0
}
Production Rate Limiter mit Tier-System
class TieredRateLimiter:
"""Multi-Tier Rate Limiting für verschiedene SLA-Stufen"""
TIERS = {
"free": {"rpm": 60, "tpm": 100_000, "priority": 1},
"pro": {"rpm": 500, "tpm": 2_000_000, "priority": 5},
"enterprise": {"rpm": 2000, "tpm": 10_000_000, "priority": 10},
}
def __init__(self, tier: str = "free"):
self.tier = tier
self.limiters: Dict[str, RateLimiter] = {}
def get_limiter(self, model: str) -> RateLimiter:
if model not in self.limiters:
tier_config = self.TIERS[self.tier]
self.limiters[model] = RateLimiter(
requests_per_minute=tier_config["rpm"],
requests_per_second=tier_config["rpm"] / 60
)
return self.limiters[model]
Verwendung
async def rate_limited_request():
limiter = TieredRateLimiter(tier="pro").get_limiter("deepseek-v3.2")
await limiter.acquire("deepseek-v3.2")
print(f"Rate Limit Status: {limiter.get_status()}")
3. Automatische Retry-Logik mit Exponential Backoff
import asyncio
import logging
from typing import TypeVar, Callable, Any
from dataclasses import dataclass
from enum import Enum
import httpx
T = TypeVar('T')
class RetryStrategy(Enum):
"""Verfügbare Retry-Strategien"""
EXPONENTIAL = "exponential"
LINEAR = "linear"
FIBONACCI = "fibonacci"
@dataclass
class RetryConfig:
"""Konfiguration für Retry-Mechanismus"""
max_retries: int = 5
base_delay: float = 1.0
max_delay: float = 60.0
jitter: bool = True
retry_on: tuple = (httpx.TimeoutException, httpx.HTTPStatusError)
strategy: RetryStrategy = RetryStrategy.EXPONENTIAL
def calculate_delay(self, attempt: int) -> float:
"""Berechnet Delay basierend auf Strategie"""
if self.strategy == RetryStrategy.EXPONENTIAL:
delay = self.base_delay * (2 ** attempt)
elif self.strategy == RetryStrategy.LINEAR:
delay = self.base_delay * attempt
else: # FIBONACCI
a, b = 1, 1
for _ in range(attempt):
a, b = b, a + b
delay = self.base_delay * a
delay = min(delay, self.max_delay)
# JITTER für bessere Verteilung
if self.jitter:
import random
delay *= (0.5 + random.random() * 0.5)
return delay
class HolySheepRetryClient:
"""Production-Retry-Client mit holistischer Fehlerbehandlung"""
def __init__(self, base_url: str = "https://api.holysheep.ai/v1",
api_key: str = "YOUR_HOLYSHEEP_API_KEY",
retry_config: RetryConfig = None):
self.base_url = base_url
self.api_key = api_key
self.retry_config = retry_config or RetryConfig()
self.client = None
self.logger = logging.getLogger(__name__)
async def __aenter__(self):
self.client = AsyncClient(
base_url=self.base_url,
headers={"Authorization": f"Bearer {self.api_key}"},
timeout=Timeout(60.0)
)
return self
async def __aexit__(self, *args):
if self.client:
await self.client.aclose()
async def execute_with_retry(
self,
func: Callable[..., Any],
*args,
**kwargs
) -> Any:
"""Führt Funktion mit automatischen Retries aus"""
last_exception = None
for attempt in range(self.retry_config.max_retries + 1):
try:
result = await func(*args, **kwargs)
if attempt > 0:
self.logger.info(
f"Erfolgreich nach {attempt} Retries"
)
return result
except self.retry_config.retry_on as e:
last_exception = e
status_code = getattr(e.response, 'status_code', None)
# Keine Retries für 4xx-Fehler (außer 429)
if status_code and 400 <= status_code < 500 and status_code != 429:
self.logger.error(f"Kritischer Fehler {status_code}: {e}")
raise
# Retry-Code Behandlung
if status_code == 429:
retry_after = e.response.headers.get('retry-after', 60)
delay = float(retry_after) + self.retry_config.calculate_delay(attempt)
self.logger.warning(f"Rate Limit erreicht, Retry in {delay}s")
else:
delay = self.retry_config.calculate_delay(attempt)
if attempt < self.retry_config.max_retries:
self.logger.warning(
f"Retry {attempt + 1}/{self.retry_config.max_retries} "
f"nach {delay:.2f}s: {type(e).__name__}"
)
await asyncio.sleep(delay)
else:
self.logger.error(f"Max Retries erreicht: {e}")
except Exception as e:
self.logger.error(f"Unerwarteter Fehler: {e}")
raise
raise last_exception
Verwendungsbeispiel
async def chat_with_retry(client: HolySheepRetryClient, model: str, messages: list):
"""Chat-Request mit automatischen Retries"""
async def make_request():
response = await client.client.post(
"/chat/completions",
json={"model": model, "messages": messages}
)
response.raise_for_status()
return response.json()
return await client.execute_with_retry(make_request)
Konfiguration für verschiedene Modelle
MODEL_RETRY_CONFIGS = {
"deepseek-v3.2": RetryConfig(max_retries=5, base_delay=2.0, strategy=RetryStrategy.EXPONENTIAL),
"gpt-4.1": RetryConfig(max_retries=3, base_delay=5.0, strategy=RetryStrategy.EXPONENTIAL),
"claude-sonnet-4.5": RetryConfig(max_retries=3, base_delay=3.0, strategy=RetryStrategy.LINEAR),
"gemini-2.5-flash": RetryConfig(max_retries=5, base_delay=1.0, strategy=RetryStrategy.EXPONENTIAL),
}
4. SLA-Monitoring mit Prometheus-Metriken
from prometheus_client import Counter, Histogram, Gauge, start_http_server
from datetime import datetime, timedelta
from dataclasses import dataclass
from typing import Dict, Optional
import asyncio
@dataclass
class SLAMetrics:
"""SLA-Metriken für HolySheep API"""
# Request-Metriken
requests_total = Counter(
'holysheep_requests_total',
'Total number of API requests',
['model', 'status']
)
request_duration = Histogram(
'holysheep_request_duration_seconds',
'Request duration in seconds',
['model'],
buckets=[0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0]
)
# Kosten-Metriken
tokens_used = Counter(
'holysheep_tokens_used_total',
'Total tokens used',
['model', 'type'] # type: prompt/completion
)
cost_usd = Counter(
'holysheep_cost_usd_total',
'Total cost in USD'
)
# SLA-Gauges
active_requests = Gauge(
'holysheep_active_requests',
'Number of currently active requests',
['model']
)
error_rate = Gauge(
'holysheep_error_rate',
'Current error rate (5min window)',
['model']
)
class SLAMonitor:
"""Production SLA Monitoring für HolySheep"""
# HolySheep Preise 2026 (in USD pro Million Token)
PRICING = {
"deepseek-v3.2": {"input": 0.28, "output": 0.42},
"gpt-4.1": {"input": 2.0, "output": 8.0},
"claude-sonnet-4.5": {"input": 3.0, "output": 15.0},
"gemini-2.5-flash": {"input": 0.30, "output": 2.50},
}
# SLA-Ziele
SLA_TARGETS = {
"latency_p99_ms": 1000,
"availability": 99.5,
"error_rate_max": 1.0,
}
def __init__(self, app_name: str = "holysheep-agent"):
self.app_name = app_name
self._error_window: Dict[str, list] = {}
self._latency_window: Dict[str, list] = {}
self._window_size = timedelta(minutes=5)
self._lock = asyncio.Lock()
async def record_request(
self,
model: str,
duration: float,
tokens_prompt: int,
tokens_completion: int,
status: str = "success",
error_type: str = None
):
"""Record metrics für eine API-Anfrage"""
await self._lock
# Basis-Metriken
SLAMetrics.requests_total.labels(model=model, status=status).inc()
SLAMetrics.request_duration.labels(model=model).observe(duration)
SLAMetrics.active_requests.labels(model=model).dec()
# Token-Metriken
SLAMetrics.tokens_used.labels(model=model, type="prompt").inc(tokens_prompt)
SLAMetrics.tokens_used.labels(model=model, type="completion").inc(tokens_completion)
# Kosten-Berechnung
pricing = self.PRICING.get(model, {"input": 0, "output": 0})
cost = (tokens_prompt * pricing["input"] + tokens_completion * pricing["output"]) / 1_000_000
SLAMetrics.cost_usd.inc(cost)
# Error-Tracking
now = datetime.now()
if model not in self._error_window:
self._error_window[model] = []
self._latency_window[model] = []
self._error_window[model].append((now, error_type))
self._latency_window[model].append((now, duration))
# Window bereinigen
self._clean_window(model, now)
# Error-Rate aktualisieren
await self._update_error_rate(model)
def _clean_window(self, model: str, now: datetime):
"""Entfernt alte Einträge aus dem Window"""
cutoff = now - self._window_size
self._error_window[model] = [
(ts, err) for ts, err in self._error_window[model]
if ts > cutoff
]
self._latency_window[model] = [
(ts, dur) for ts, dur in self._latency_window[model]
if ts > cutoff
]
async def _update_error_rate(self, model: str):
"""Berechnet aktuelle Error-Rate"""
window = self._error_window.get(model, [])
if not window:
return
errors = sum(1 for _, err in window if err is not None)
total = len(window)
error_rate = (errors / total) * 100
SLAMetrics.error_rate.labels(model=model).set(error_rate)
def get_sla_status(self) -> Dict:
"""Gibt aktuellen SLA-Status zurück"""
status = {
"timestamp": datetime.now().isoformat(),
"targets": self.SLA_TARGETS,
"models": {}
}
for model in self._latency_window:
latencies = [dur for _, dur in self._latency_window[model]]
if latencies:
latencies.sort()
p99_latency = latencies[int(len(latencies) * 0.99)]
status["models"][model] = {
"latency_p99_ms": round(p99_latency * 1000, 2),
"sla_compliant": p99_latency * 1000 < self.SLA_TARGETS["latency_p99_ms"],
"requests_in_window": len(latencies)
}
return status
Prometheus-Metriken Server starten
def start_metrics_server(port: int = 9090):
"""Startet Prometheus-Metrics-Endpunkt"""
start_http_server(port)
print(f"Metrics Server gestartet auf Port {port}")
Verwendung im Agent
async def monitored_request(monitor: SLAMonitor, model: str, messages: list):
"""Request mit vollständigem Monitoring"""
import time
start = time.time()
SLAMetrics.active_requests.labels(model=model).inc()
try:
async with HolySheepAgent() as client:
result = await chat_with_retry(client, model, messages)
duration = time.time() - start
await monitor.record_request(
model=model,
duration=duration,
tokens_prompt=result.get("usage", {}).get("prompt_tokens", 0),
tokens_completion=result.get("usage", {}).get("completion_tokens", 0),
status="success"
)
return result
except Exception as e:
duration = time.time() - start
await monitor.record_request(
model=model,
duration=duration,
tokens_prompt=0,
tokens_completion=0,
status="error",
error_type=type(e).__name__
)
raise
5. Multi-Provider Failover-System
import asyncio
from typing import List, Dict, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
import logging
class Provider(Enum):
HOLYSHEEP = "holysheep"
OPENAI = "openai"
ANTHROPIC = "anthropic"
GOOGLE = "google"
@dataclass
class ProviderConfig:
"""Konfiguration für einen API-Provider"""
name: Provider
base_url: str
api_key: str
priority: int = 1
enabled: bool = True
max_retries: int = 3
timeout: float = 30.0
health_check_interval: int = 60
# Modell-Mapping
models: Dict[str, str] = field(default_factory=dict)
@dataclass
class FallbackChain:
"""Definiert eine Failover-Kette für ein Modell"""
primary: Provider
fallbacks: List[Provider] = field(default_factory=list)
class HolySheepFailoverClient:
"""Production Multi-Provider Client mit automatischem Failover"""
def __init__(self):
self.providers: Dict[Provider, ProviderConfig] = {}
self.fallback_chains: Dict[str, FallbackChain] = {}
self.health_status: Dict[Provider, bool] = {}
self.logger = logging.getLogger(__name__)
self._health_check_tasks: List[asyncio.Task] = []
def register_provider(self, config: ProviderConfig):
"""Registriert einen API-Provider"""
self.providers[config.name] = config
self.health_status[config.name] = True
# HolySheep als primären Provider konfigurieren
if config.name == Provider.HOLYSHEEP:
self._setup_holysheep_fallbacks(config)
def _setup_holysheep_fallbacks(self, config: ProviderConfig):
"""Konfiguriert HolySheep mit sekundären Providern"""
# Modell-spezifische Fallback-Ketten
self.fallback_chains = {
"deepseek-v3.2": FallbackChain(
primary=Provider.HOLYSHEEP,
fallbacks=[Provider.OPENAI, Provider.GOOGLE]
),
"gpt-4.1": FallbackChain(
primary=Provider.HOLYSHEEP,
fallbacks=[Provider.OPENAI]
),
"claude-sonnet-4.5": FallbackChain(
primary=Provider.HOLYSHEEP,
fallbacks=[Provider.ANTHROPIC]
),
"gemini-2.5-flash": FallbackChain(
primary=Provider.HOLYSHEEP,
fallbacks=[Provider.GOOGLE]
),
}
async def call_with_failover(
self,
model: str,
messages: list,
**kwargs
) -> Dict:
"""Führt Request mit automatischem Failover aus"""
chain = self.fallback_chains.get(model)
if not chain:
chain = FallbackChain(primary=Provider.HOLYSHEEP, fallbacks=[])
providers_to_try = [chain.primary] + chain.fallbacks
last_error = None
for provider in providers_to_try:
if not self.health_status.get(provider, False):
self.logger.warning(f"Provider {provider.value} ist nicht verfügbar, überspringe...")
continue
config = self.providers.get(provider)
if not config or not config.enabled:
continue
try:
self.logger.info(f"Versuche {provider.value} für Modell {model}")
result = await self._make_request(config, model, messages, **kwargs)
self.logger.info(f"Erfolgreich mit {provider.value}")
return result
except Exception as e:
last_error = e
self.logger.error(f"Fehler bei {provider.value}: {e}")
self.health_status[provider] = False
# Asynchroner Health-Check
asyncio.create_task(self._delayed_health_check(provider, config))
raise last_error or Exception("Alle Provider fehlgeschlagen")
async def _make_request(
self,
config: ProviderConfig,
model: str,
messages: list,
**kwargs
) -> Dict:
"""Führt einzelnen Request aus"""
# Modell-Mapping wenn nötig
target_model = config.models.get(model, model)
async with AsyncClient(
base_url=config.base_url,
headers={"Authorization": f"Bearer {config.api_key}"},
timeout=Timeout(config.timeout)
) as client:
response = await client.post(
"/chat/completions",
json={
"model": target_model,
"messages": messages,
**kwargs
}
)
response.raise_for_status()
return response.json()
async def _delayed_health_check(self, provider: Provider, config: ProviderConfig):
"""Führt verzögerten Health-Check durch"""
await asyncio.sleep(config.health_check_interval)
is_healthy = await self._check_provider_health(config)
self.health_status[provider] = is_healthy
self.logger.info(f"Health-Check {provider.value}: {'OK' if is_healthy else 'FAILED'}")
async def _check_provider_health(self, config: ProviderConfig) -> bool:
"""Prüft ob Provider erreichbar ist"""
try:
async with AsyncClient(base_url=config.base_url, timeout=5.0) as client:
response = await client.get("/models")
return response.status_code == 200
except:
return False
async def start_health_checks(self):
"""Startet periodische Health-Checks für alle Provider"""
for provider, config in self.providers.items():
task = asyncio.create_task(self._periodic_health_check(provider, config))
self._health_check_tasks.append(task)
async def _periodic_health_check(self, provider: Provider, config: ProviderConfig):
"""Periodischer Health-Check"""
while True:
await asyncio.sleep(config.health_check_interval)
is_healthy = await self._check_provider_health(config)
# Recovery-Logik
if is_healthy and not self.health_status[provider]:
self.logger.warning(f"Provider {provider.value} erholt, füge wieder hinzu")
self.health_status[provider] = is_healthy
Production-Setup mit HolySheep
async def setup_production_client():
"""Konfiguriert Production-Client mit HolySheep"""
client = HolySheepFailoverClient()
# HolySheep als primären Provider
client.register_provider(ProviderConfig(
name=Provider.HOLYSHEEP,
base_url="https://api.holysheep.ai/v1", # WICHTIG: HolySheep URL
api_key="YOUR_HOLYSHEEP_API_KEY",
priority=1,
models={
"deepseek-v3.2": "deepseek-v3.2",
"gpt-4.1": "gpt-4.1",
"claude-sonnet-4.5": "claude-sonnet-4.5",
"gemini-2.5-flash": "gemini-2.5-flash",
}
))
# Sekundäre Provider (nur für Failover)
client.register_provider(ProviderConfig(
name=Provider.OPENAI,
base_url="https://api.openai.com/v1",
api_key="YOUR_OPENAI_KEY",
priority=2,
enabled=True
))
# Health-Checks starten
await client.start_health_checks()
return client
6. Kosten-Optimierung mit Smart Routing
from typing import List, Dict, Optional
from dataclasses import dataclass
from enum import Enum
class TaskComplexity(Enum):
SIMPLE = "simple" # <100 Token
MEDIUM = "medium" # 100-1000 Token
COMPLEX = "complex" # >1000 Token
@dataclass
class ModelCapability:
"""Fähigkeiten eines Modells"""
name: str
provider: str
context_window: int
cost_per_1k_input: float
cost_per_1k_output: float
speed_factor: float # Relativ zu DeepSeek
MODEL_CATALOG = {
# HolySheep Modelle (priorisiert wegen Kosten)
"deepseek-v3.2": ModelCapability(
name="DeepSeek V3.2",
provider="holysheep",
context_window=128000,
cost_per_1k_input=0.28,
cost_per_1k_output=0.42,
speed_factor=1.0
),
"gemini-2.5-flash": ModelCapability(
name="Gemini 2.5 Flash",
provider="holysheep",
context_window=1000000,
cost_per_1k_input=0.30,
cost_per_1k_output=2.50,
speed_factor=0.8
),
"gpt-4.1": ModelCapability(
name="GPT-4.1",
provider="holysheep",
context_window=128000,
cost_per_1k_input=2.0,
cost_per_1k_output=8.0,
speed_factor=0.5
),
"claude-sonnet-4.5": ModelCapability(
name="Claude Sonnet 4.5",
provider="holysheep",
context_window=200000,
cost_per_1k_input=3.0,
cost_per_1k_output=15.0,
speed_factor=0.6
),
}
class SmartRouter:
"""Intelligenter Router für Kostenoptimierung"""
# Routing-Regeln basierend auf Task-Typ
TASK_MODEL_MAP = {
"code_generation": ["deepseek-v3.2", "gpt-4.1"],
"text_summarization": ["gemini-2.5-flash", "deepseek-v3.2"],
"creative_writing": ["claude-sonnet-4.5", "gpt-4.1"],
"data_analysis": ["deepseek-v3.2", "gemini-2.5-flash"],
"chat": ["deepseek-v3.2", "gemini-2.5-flash"],
}
# Komplexitäts-Threshold
COMPLEXITY_TOKEN_THRESHOLD = 2000
def __init__(self, max_budget_monthly: float = 10000):
self.max_budget = max_budget_monthly
self.current_spend = 0.0
self.monthly_costs: Dict[str, float] = {}
def estimate_complexity(self, messages: List[Dict]) -> TaskComplexity:
"""Schätzt Task-Komplexität basierend auf Input"""
total_tokens = sum(
len(msg.get("content", "").split()) * 1.3 # Rough token estimation
for msg in messages
)
if total_tokens < 100:
return TaskComplexity.SIMPLE
elif total_tokens < self.COMPLEXITY_TOKEN_THRESHOLD:
return TaskComplexity.MEDIUM
return TaskComplexity.COMPLEX
def route(
self,
task_type: str,
messages: List[Dict],
required_capabilities: List[str] = None
) -> str:
"""Wählt optimalen Route für Task"""
complexity = self.estimate_complexity(messages)
candidate_models = self.TASK_MODEL_MAP.get(task_type, ["deepseek-v3.2"])
# Komplexität beeinflusst Modellwahl
if complexity == TaskComplexity.SIMPLE:
# Für einfache Tasks: billigstes Modell
candidate_models = ["deepseek-v3.2", "gemini-2.5-flash"]
elif complexity == TaskComplexity.COMPLEX:
# Für komplexe Tasks: Modelle mit größerem Context
candidate_models = ["claude-sonnet-4.5", "gpt-4.1", "deepseek-v3.2"]
# Budget-Prüfung
remaining_budget = self.max_budget - self.current_spend
# Wähle günstigstes Modell das Anforderungen erfüllt
for model_name in candidate_models:
model = MODEL_CATALOG.get(model_name)
if not model:
continue
estimated_cost = self._estimate_task_cost(model, messages)
if estimated_cost <= remaining_budget:
self._track_cost(model_name, estimated_cost)
return model_name
# Fallback zu DeepSeek wenn Budget überschritten
return "deepseek-v3.2"
def _estimate_task_cost(self, model: ModelCapability, messages: List[Dict])
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