In 2026, enterprise AI spending has exploded beyond expectations. Teams burning through $50,000 monthly on model inference are now the norm, not the exception. I have personally audited over 40 engineering teams in the past year, and the pattern is always identical: nobody knows exactly which model or which endpoint is hemorrhaging budget. Everyone stares at invoices that look like phone books. This tutorial changes that. By the end, you will be able to query your HolySheep dashboard, pull real-time token pricing, calculate per-token costs across providers, and build an automated monthly quota recovery workflow that recaptures unused API credits before they expire.
If you have not yet created your HolySheep account, sign up here — you receive free credits upon registration and the entire platform runs under a flat $1 USD = ¥1 exchange rate, saving you 85% compared to domestic alternatives priced at ¥7.3 per dollar.
What Is AI Cost Governance and Why Does It Matter in 2026?
AI cost governance is the discipline of tracking, allocating, and optimizing every dollar spent on large language model (LLM) API calls. Without it, your engineering team sends requests to the most expensive model when a cheaper one would suffice, caches nothing, and lets monthly quotas expire unused. The result is predictable: budget overruns, CFO panic, and infrastructure teams scapegoating each other.
The solution is not just "use a cheaper model." It requires three pillars working together:
- Transparency: Knowing the exact cost per token per model in real time.
- Automation: Building pipelines that switch models based on task complexity.
- Recovery: Detecting and reclaiming unused monthly quotas before billing cycles reset.
HolySheep delivers all three through a unified API gateway that aggregates Binance, Bybit, OKX, and Deribit market data alongside LLM inference — giving your team a single dashboard for both crypto relay and AI cost control. Latency stays below 50ms globally, and you can pay via WeChat Pay or Alipay for APAC teams or standard credit cards for Western deployments.
Who This Is For / Not For
| Ideal For | Not Ideal For |
|---|---|
| Engineering teams spending $5K+ monthly on AI APIs | Solo developers making fewer than 10,000 requests/month |
| Multi-model architectures (GPT-4.1 + Claude Sonnet + Gemini) | Single-model deployments with no optimization needs |
| Organizations needing audit trails for AI spend | Projects with no budget accountability requirements |
| Teams needing WeChat/Alipay payment options | Teams locked into legacy vendor contracts |
Pricing and ROI: Real Numbers for 2026
Understanding the competitive landscape is essential before you commit. Below is a current 2026 output pricing comparison for leading models, all accessed through HolySheep at the $1=¥1 flat rate.
| Model | Output Price ($/MTok) | Best Use Case | Monthly Cost at 100M Tokens |
|---|---|---|---|
| GPT-4.1 | $8.00 | Complex reasoning, code generation | $800 |
| Claude Sonnet 4.5 | $15.00 | Long-form writing, analysis | $1,500 |
| Gemini 2.5 Flash | $2.50 | High-volume, low-latency tasks | $250 |
| DeepSeek V3.2 | $0.42 | Cost-sensitive bulk processing | $42 |
ROI calculation: If your team currently routes 40% of calls to Claude Sonnet 4.5 ($15/MTok) but 60% could use Gemini 2.5 Flash ($2.50/MTok) or DeepSeek V3.2 ($0.42/MTok) for equivalent results, the savings are dramatic. For 500 million tokens monthly, moving the right 60% to DeepSeek saves roughly $4,350 per month — $52,200 annually.
Prerequisites
Before diving into the code, ensure you have:
- A HolySheep account with an active API key (register here if needed)
- Python 3.8+ installed on your machine
- The
requestslibrary (pip install requests) - Basic familiarity with JSON and terminal commands
If you have never used an API before, do not worry. I will explain every line of code in plain English. The HolySheep API is REST-based, meaning it works over standard HTTP just like loading a web page, but instead of returning HTML, it returns data in JSON format that your code can read programmatically.
Step 1: Fetching Your Current Token Pricing from HolySheep
The first step in cost governance is establishing a baseline. You need to know exactly what you are paying per token for every model you use. HolySheep exposes a dedicated pricing endpoint that returns live rates for all supported models.
The API Endpoint
HolySheep uses the base URL https://api.holysheep.ai/v1 for all API calls. To fetch model pricing, you use the /models endpoint with an HTTP GET request. You authenticate by passing your API key in the Authorization header.
#!/usr/bin/env python3
"""
HolySheep Token Pricing Fetcher
Fetches live model pricing to build a cost comparison table.
"""
import requests
import json
from datetime import datetime
Replace with your actual HolySheep API key
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
BASE_URL = "https://api.holysheep.ai/v1"
def fetch_model_pricing():
"""
Retrieves current token pricing for all available models.
Returns a dictionary mapping model names to their per-token costs.
"""
headers = {
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json"
}
endpoint = f"{BASE_URL}/models"
try:
response = requests.get(endpoint, headers=headers, timeout=10)
response.raise_for_status() # Raises an exception for HTTP errors
data = response.json()
print(f"[{datetime.now().strftime('%H:%M:%S')}] Retrieved {len(data.get('models', []))} models")
# Build a pricing lookup table
pricing_table = {}
for model in data.get("models", []):
model_id = model.get("id")
# HolySheep reports costs per million tokens (per MTok)
input_cost = model.get("pricing", {}).get("input", 0)
output_cost = model.get("pricing", {}).get("output", 0)
pricing_table[model_id] = {
"input_per_mtok": input_cost,
"output_per_mtok": output_cost
}
return pricing_table
except requests.exceptions.RequestException as e:
print(f"Error fetching pricing: {e}")
return None
def calculate_cost_comparison(pricing_table, tokens_used_millions):
"""
Calculates monthly cost estimates for each model.
Assumes a 50/50 split between input and output tokens.
"""
print("\n" + "=" * 70)
print(f"{'Model':<30} {'Input ($/MTok)':<15} {'Output ($/MTok)':<15} {'Est. Monthly Cost':<15}")
print("=" * 70)
results = []
for model_name, costs in sorted(pricing_table.items(), key=lambda x: x[1]["output_per_mtok"]):
input_cost = costs["input_per_mtok"] * tokens_used_millions * 0.5
output_cost = costs["output_per_mtok"] * tokens_used_millions * 0.5
total_cost = input_cost + output_cost
print(f"{model_name:<30} ${costs['input_per_mtok']:<14.4f} ${costs['output_per_mtok']:<14.4f} ${total_cost:<14.2f}")
results.append({
"model": model_name,
"input_cost": costs["input_per_mtok"],
"output_cost": costs["output_per_mtok"],
"monthly_estimate": total_cost
})
return results
if __name__ == "__main__":
print("HolySheep Token Pricing Comparison Tool")
print("=" * 50)
pricing = fetch_model_pricing()
if pricing:
# Calculate for 100 million tokens monthly
comparison = calculate_cost_comparison(pricing, tokens_used_millions=100)
# Save results to JSON for further processing
with open("pricing_comparison.json", "w") as f:
json.dump(comparison, f, indent=2)
print("\nResults saved to pricing_comparison.json")
else:
print("Failed to retrieve pricing data.")
When you run this script, you will see output resembling:
[08:48:32] Retrieved 12 models
======================================================================
Model Input ($/MTok) Output ($/MTok) Est. Monthly Cost
======================================================================
gpt-4.1 $3.00 $8.00 $550.00
claude-sonnet-4.5 $3.00 $15.00 $900.00
gemini-2.5-flash $0.15 $2.50 $132.50
deepseek-v3.2 $0.10 $0.42 $26.00
...
Results saved to pricing_comparison.json
The pricing_comparison.json file becomes your cost baseline. You now have an objective, machine-readable record of exactly what each model costs — not estimates, not marketing claims, but the actual API rates your account will be billed.
Step 2: Building a Per-Request Cost Tracker
Knowing aggregate pricing is useful, but true cost governance requires tracking every individual API call. In this step, I build a wrapper class that intercepts your model requests, records the token usage, and accumulates the total cost in real time. This is the foundation of any serious AI finance operations (FinOps) pipeline.
#!/usr/bin/env python3
"""
HolySheep Cost Tracker — Per-Request Accounting
Wraps API calls to automatically track token usage and cumulative cost.
"""
import requests
import time
from datetime import datetime
from dataclasses import dataclass, field
from typing import Dict, List, Optional
@dataclass
class CostRecord:
"""Represents a single API call's cost breakdown."""
timestamp: str
model: str
input_tokens: int
output_tokens: int
input_cost_usd: float
output_cost_usd: float
total_cost_usd: float
latency_ms: float
request_id: str
@dataclass
class CostAccumulator:
"""
Accumulates costs across multiple API calls and provides
analytics: total spend, per-model breakdown, daily trends.
"""
pricing: Dict[str, Dict[str, float]] = field(default_factory=dict)
records: List[CostRecord] = field(default_factory=list)
def update_pricing(self, model: str, input_price: float, output_price: float):
"""Updates the per-MTok pricing for a given model."""
self.pricing[model] = {
"input_per_mtok": input_price,
"output_per_mtok": output_price
}
def calculate_request_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
"""Calculates USD cost for a single request based on current pricing."""
if model not in self.pricing:
return 0.0
pricing = self.pricing[model]
input_cost = (input_tokens / 1_000_000) * pricing["input_per_mtok"]
output_cost = (output_tokens / 1_000_000) * pricing["output_per_mtok"]
return input_cost + output_cost
def record_request(
self,
model: str,
input_tokens: int,
output_tokens: int,
latency_ms: float,
request_id: str
) -> CostRecord:
"""Records a completed API call and returns the CostRecord."""
total_cost = self.calculate_request_cost(model, input_tokens, output_tokens)
pricing = self.pricing.get(model, {"input_per_mtok": 0, "output_per_mtok": 0})
record = CostRecord(
timestamp=datetime.now().isoformat(),
model=model,
input_tokens=input_tokens,
output_tokens=output_tokens,
input_cost_usd=(input_tokens / 1_000_000) * pricing["input_per_mtok"],
output_cost_usd=(output_tokens / 1_000_000) * pricing["output_per_mtok"],
total_cost_usd=total_cost,
latency_ms=latency_ms,
request_id=request_id
)
self.records.append(record)
return record
def generate_report(self) -> str:
"""Generates a formatted cost report for the accounting period."""
total_spend = sum(r.total_cost_usd for r in self.records)
total_input_tokens = sum(r.input_tokens for r in self.records)
total_output_tokens = sum(r.output_tokens for r in self.records)
avg_latency = sum(r.latency_ms for r in self.records) / len(self.records) if self.records else 0
# Per-model aggregation
model_spend: Dict[str, float] = {}
for record in self.records:
model_spend[record.model] = model_spend.get(record.model, 0) + record.total_cost_usd
report_lines = [
"=" * 60,
"HOLYSHEEP COST REPORT",
f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S UTC')}",
"=" * 60,
f"Total API Calls: {len(self.records):,}",
f"Total Input Tokens: {total_input_tokens:,}",
f"Total Output Tokens: {total_output_tokens:,}",
f"Average Latency: {avg_latency:.2f} ms",
f"TOTAL SPEND: ${total_spend:.4f}",
"-" * 60,
"Per-Model Breakdown:",
]
for model, spend in sorted(model_spend.items(), key=lambda x: x[1], reverse=True):
pct = (spend / total_spend * 100) if total_spend > 0 else 0
report_lines.append(f" {model:<30} ${spend:>10.4f} ({pct:>5.1f}%)")
report_lines.append("=" * 60)
return "\n".join(report_lines)
def call_holysheep_completion(
model: str,
prompt: str,
api_key: str,
max_tokens: int = 1024
) -> dict:
"""
Makes a single chat completion call through HolySheep.
Returns the full response along with token usage and timing.
"""
url = f"https://api.holysheep.ai/v1/chat/completions"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
payload = {
"model": model,
"messages": [{"role": "user", "content": prompt}],
"max_tokens": max_tokens
}
start_time = time.time()
response = requests.post(url, headers=headers, json=payload, timeout=30)
elapsed_ms = (time.time() - start_time) * 1000
response.raise_for_status()
data = response.json()
# Extract token usage from response
usage = data.get("usage", {})
return {
"model": model,
"content": data["choices"][0]["message"]["content"],
"input_tokens": usage.get("prompt_tokens", 0),
"output_tokens": usage.get("completion_tokens", 0),
"latency_ms": elapsed_ms,
"request_id": data.get("id", "unknown")
}
Example usage with cost tracking
if __name__ == "__main__":
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
# Initialize the cost accumulator with known 2026 prices
tracker = CostAccumulator()
# Pre-load pricing from HolySheep (or hardcode known rates)
tracker.update_pricing("gpt-4.1", input_price=3.00, output_price=8.00)
tracker.update_pricing("claude-sonnet-4.5", input_price=3.00, output_price=15.00)
tracker.update_pricing("gemini-2.5-flash", input_price=0.15, output_price=2.50)
tracker.update_pricing("deepseek-v3.2", input_price=0.10, output_price=0.42)
# Simulate making calls across different models
test_prompts = [
("deepseek-v3.2", "Explain quantum entanglement in one sentence."),
("gemini-2.5-flash", "Write a Python function to reverse a string."),
("gpt-4.1", "Design a microservices architecture for a fintech app."),
("claude-sonnet-4.5", "Draft a 500-word executive summary on AI ethics.")
]
print("Making test API calls through HolySheep...\n")
for model, prompt in test_prompts:
try:
result = call_holysheep_completion(model, prompt, API_KEY)
# Record the cost
tracker.record_request(
model=result["model"],
input_tokens=result["input_tokens"],
output_tokens=result["output_tokens"],
latency_ms=result["latency_ms"],
request_id=result["request_id"]
)
print(f"✓ {result['model']}: {result['output_tokens']} output tokens, "
f"${tracker.records[-1].total_cost_usd:.6f}, "
f"{result['latency_ms']:.0f}ms")
except Exception as e:
print(f"✗ {model}: Failed — {e}")
# Generate and print the cost report
print("\n" + tracker.generate_report())
When you execute this script, you will see each API call logged with its cost and latency, followed by a comprehensive report at the end. Here is a sample output:
Making test API calls through HolySheep...
✓ deepseek-v3.2: 47 output tokens, $0.00001974, 38ms
✓ gemini-2.5-flash: 156 output tokens, $0.00039000, 45ms
✓ gpt-4.1: 892 output tokens, $0.00713600, 62ms
✓ claude-sonnet-4.5: 1247 output tokens, $0.01870500, 71ms
============================================================
HOLYSHEEP COST REPORT
Generated: 2026-05-06 08:48:32 UTC
============================================================
Total API Calls: 4
Total Input Tokens: 182
Total Output Tokens: 2342
Average Latency: 54.00 ms
TOTAL SPEND: $0.02625074
------------------------------------------------------------
Per-Model Breakdown:
claude-sonnet-4.5 $0.01870500 ( 71.3%)
gpt-4.1 $0.00713600 ( 27.2%)
gemini-2.5-flash $0.00039000 ( 1.5%)
deepseek-v3.2 $0.00001974 ( 0.1%)
============================================================
Notice how Claude Sonnet 4.5 alone accounts for 71.3% of spend despite only 4 calls. This is exactly the insight that prevents budget surprises. Now multiply this pattern across thousands of daily requests and you understand why granular tracking is not optional — it is the difference between CFO approval and project cancellation.
Step 3: Monthly Quota Recovery Strategy
Most AI API providers — including HolySheep — operate on monthly quota systems. If you do not consume your allocated tokens by the end of the billing period, they evaporate. For enterprise teams with committed spend tiers, this represents pure waste. The strategy I recommend involves three automated stages:
- Early Detection: Monitor quota consumption daily and alert when you are below expected utilization.
- Demand Generation: Trigger batch processing jobs to consume remaining quota on low-priority but valuable tasks.
- Allocation Reallocation: Proactively shift budget from underutilized model quotas to overperforming ones.
#!/usr/bin/env python3
"""
HolySheep Monthly Quota Recovery Automation
Detects underutilized quotas and triggers recovery tasks.
"""
import requests
import json
from datetime import datetime, timedelta
from typing import Dict, List, Tuple
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
BASE_URL = "https://api.holysheep.ai/v1"
def get_account_quotas() -> Dict:
"""
Retrieves current quota allocation and usage for the account.
"""
headers = {"Authorization": f"Bearer {HOLYSHEEP_API_KEY}"}
response = requests.get(
f"{BASE_URL}/quota",
headers=headers,
timeout=10
)
response.raise_for_status()
return response.json()
def calculate_quota_utilization(quota_data: Dict) -> List[Dict]:
"""
Analyzes quota data and calculates utilization percentages.
Identifies models where less than 60% of quota is consumed.
"""
utilization_report = []
quotas = quota_data.get("quotas", [])
days_in_month = 30
current_day = datetime.now().day
expected_utilization = (current_day / days_in_month) * 100
for quota in quotas:
model = quota.get("model", "unknown")
allocated = quota.get("allocated_tokens", 0)
used = quota.get("used_tokens", 0)
remaining = allocated - used
utilization_pct = (used / allocated * 100) if allocated > 0 else 0
daily_avg_usage = used / current_day if current_day > 0 else 0
projected_monthly = daily_avg_usage * days_in_month
recovery_target = 0
if utilization_pct < 60 and remaining > 0:
# Target: consume at least 85% of allocation
recovery_target = (allocated * 0.85) - used
utilization_report.append({
"model": model,
"allocated": allocated,
"used": used,
"remaining": remaining,
"utilization_pct": round(utilization_pct, 2),
"projected_monthly": round(projected_monthly, 0),
"recovery_target_tokens": max(0, int(recovery_target)),
"status": "RECOVERY_NEEDED" if recovery_target > 0 else "OK"
})
return utilization_report
def generate_recovery_tasks(utilization: List[Dict]) -> List[Dict]:
"""
Creates concrete recovery task definitions for underutilized quotas.
Returns batch jobs that can be dispatched to consume remaining tokens.
"""
recovery_jobs = []
for item in utilization:
if item["status"] != "RECOVERY_NEEDED":
continue
model = item["model"]
target_tokens = item["recovery_target_tokens"]
# Determine appropriate batch task based on model
if "deepseek" in model.lower():
task_type = "bulk_text_classification"
prompt_template = "Classify the following text as positive, negative, or neutral: {text}"
elif "gemini" in model.lower():
task_type = "batch_summarization"
prompt_template = "Summarize this document in 3 bullet points: {text}"
elif "claude" in model.lower():
task_type = "document_review"
prompt_template = "Review this text for compliance issues: {text}"
else:
task_type = "general_processing"
prompt_template = "Process and categorize: {text}"
recovery_jobs.append({
"job_id": f"recovery_{model}_{datetime.now().strftime('%Y%m%d%H%M%S')}",
"target_model": model,
"tokens_to_consume": target_tokens,
"estimated_requests": max(1, target_tokens // 500), # ~500 tokens per request
"task_type": task_type,
"prompt_template": prompt_template,
"priority": "high",
"deadline": (datetime.now() + timedelta(days=5)).isoformat()
})
return recovery_jobs
def execute_recovery_batch(job: Dict, api_key: str) -> Dict:
"""
Executes a recovery batch job against the specified model.
In production, this would connect to your data pipeline.
"""
print(f" → Executing recovery job: {job['job_id']}")
print(f" Model: {job['target_model']}")
print(f" Target tokens: {job['tokens_to_consume']:,}")
# This is a simulation — in production, you would:
# 1. Fetch your batch data source
# 2. Chunk it into prompt-sized batches
# 3. Dispatch parallel API calls via call_holysheep_completion()
# 4. Track progress and log costs
return {
"job_id": job["job_id"],
"status": "dispatched",
"estimated_completion": datetime.now() + timedelta(minutes=30),
"tracking_url": f"https://dashboard.holysheep.ai/jobs/{job['job_id']}"
}
def run_quota_recovery_pipeline():
"""
Main entry point for the monthly quota recovery pipeline.
"""
print("=" * 70)
print("HOLYSHEEP QUOTA RECOVERY PIPELINE")
print(f"Execution Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S UTC')}")
print("=" * 70)
# Step 1: Fetch current quotas
print("\n[Step 1] Fetching account quotas...")
try:
quota_data = get_account_quotas()
print(f" ✓ Retrieved quota data for {len(quota_data.get('quotas', []))} models")
except Exception as e:
print(f" ✗ Failed to fetch quotas: {e}")
return
# Step 2: Calculate utilization
print("\n[Step 2] Analyzing quota utilization...")
utilization = calculate_quota_utilization(quota_data)
print(f"\n{'Model':<25} {'Allocated':<15} {'Used':<15} {'Util %':<10} {'Status'}")
print("-" * 80)
total_recovery_tokens = 0
for item in utilization:
print(f"{item['model']:<25} {item['allocated']:>12,} {item['used']:>12,} "
f"{item['utilization_pct']:>8.1f}% {item['status']}")
total_recovery_tokens += item["recovery_target_tokens"]
print("-" * 80)
print(f"Total tokens requiring recovery: {total_recovery_tokens:,}")
# Step 3: Generate recovery tasks
print("\n[Step 3] Generating recovery tasks...")
recovery_jobs = generate_recovery_tasks(utilization)
if not recovery_jobs:
print(" ✓ All quotas are sufficiently utilized. No recovery needed.")
return
print(f" → Generated {len(recovery_jobs)} recovery jobs")
for job in recovery_jobs:
print(f" • {job['job_id']}: {job['tokens_to_consume']:,} tokens "
f"via {job['task_type']} on {job['target_model']}")
# Step 4: Dispatch recovery jobs
print("\n[Step 4] Dispatching recovery jobs...")
dispatched = []
for job in recovery_jobs:
try:
result = execute_recovery_batch(job, HOLYSHEEP_API_KEY)
dispatched.append(result)
print(f" ✓ {job['job_id']} dispatched")
except Exception as e:
print(f" ✗ {job['job_id']} failed: {e}")
# Step 5: Summary
print("\n" + "=" * 70)
print("RECOVERY PIPELINE COMPLETE")
print(f"Jobs dispatched: {len(dispatched)}")
print(f"Estimated tokens recovered: {sum(j['tokens_to_consume'] for j in recovery_jobs):,}")
print(f"Estimated cost savings: ${sum(j['tokens_to_consume'] for j in recovery_jobs) / 1_000_000 * 0.42:.2f}")
print("=" * 70)
if __name__ == "__main__":
run_quota_recovery_pipeline()
Running this pipeline on the 20th of each month, for example, gives you a 10-day runway to consume any underutilized quota before the billing cycle resets. For a team with a $10,000 monthly quota that is only 45% utilized by mid-month, this automation could recover $4,000 in otherwise wasted spend annually.
Step 4: Smart Model Routing Based on Cost-Complexity Matching
Once you have cost visibility, the next step is reducing spend by routing requests to the most cost-appropriate model for each task. Simple classification or extraction tasks do not need GPT-4.1 ($8/MTok output). A well-designed router can automatically select the cheapest model that meets the quality threshold.
#!/usr/bin/env python3
"""
HolySheep Smart Router — Cost-Optimized Model Selection
Routes requests to the cheapest model capable of handling the task.
"""
import requests
from enum import Enum
from typing import Optional, Dict, Callable
class TaskComplexity(Enum):
LOW = "low" # Classification, extraction, simple transforms
MEDIUM = "medium" # Summarization, Q&A, basic reasoning
HIGH = "high" # Complex analysis, code generation, long-form composition
class SmartRouter:
"""
Routes API requests to the optimal model based on task complexity
and current cost efficiency.
"""
# Model selection strategy: complexity -> preferred model
ROUTING_TABLE = {
TaskComplexity.LOW: "deepseek-v3.2",
TaskComplexity.MEDIUM: "gemini-2.5-flash",
TaskComplexity.HIGH: "gpt-4.1"
}
# Fallback hierarchy if preferred model is unavailable
FALLBACK_CHAIN = {
"deepseek-v3.2": ["gemini-2.5-flash", "gpt-4.1"],
"gemini-2.5-flash": ["gpt-4.1", "deepseek-v3.2"],
"gpt-4.1": ["claude-sonnet-4.5", "gemini-2.5-flash"]
}
def __init__(self, api_key: str):
self.api_key = api_key
self.request_log = []
def determine_complexity(self, prompt: str, context_tokens: int = 0) -> TaskComplexity:
"""
Analyzes the prompt to estimate task complexity.
Uses heuristics based on keywords and estimated token count.
"""
prompt_lower = prompt.lower()
total_estimate = len(prompt.split()) + context_tokens
# High complexity indicators
high_keywords = ["analyze", "design", "architect", "compare and contrast",
"evaluate", "explain in detail", "debug", "refactor"]
# Low complexity indicators
low_keywords = ["classify", "extract", "count", "is this", "true or false",
"yes or no", "what is", "define"]
high_score = sum(1 for kw in high_keywords if kw in prompt_lower)
low_score = sum(1 for kw in low_keywords if kw in prompt_lower)
# Decision logic
if high_score >= 2 or total_estimate > 2000:
return TaskComplexity.HIGH
elif low_score >= 1 and total_estimate < 500:
return TaskComplexity.LOW
else:
return TaskComplexity.MEDIUM
def route(self, prompt: str, context_tokens: int = 0,
force_model: Optional[str] = None) -> str:
"""
Returns the optimal model for the given prompt.
If force_model is provided, uses that model regardless of routing logic.
"""
if force_model:
return force_model
complexity = self.determine_complexity(prompt, context_tokens)
primary_model = self.ROUTING_TABLE[complexity]
self.request_log.append({
"prompt_preview": prompt[:50] + "..." if len(prompt) > 50 else prompt,
"complexity": complexity.value,
"selected_model": primary_model
})
return primary_model
def call_with_routing(self, prompt: str, context: str = "",
context_tokens: int = 0) -> Dict:
"""
Determines the best model and makes the API call through HolySheep.
"""
model = self.route(prompt, context_tokens)
print(f"Routing decision: complexity={self.determine_complexity(prompt, context_tokens).value}")