As AI infrastructure costs spiral toward $2.4 trillion annually by 2030, engineering teams face a critical question: which model delivers the best quality-to-cost ratio for your specific workload? HolySheep AI answers this with a production-grade A/B testing framework that routes live traffic across multiple models while collecting real-time quality metrics and automatically optimizing traffic allocation based on performance data.
In this hands-on guide, I walk through the complete architecture, implementation code, and cost analysis for deploying HolySheep's multi-model relay with automated traffic steering. The pricing math is compelling: $8/MTok for GPT-4.1, $15/MTok for Claude Sonnet 4.5, $2.50/MTok for Gemini 2.5 Flash, and just $0.42/MTok for DeepSeek V3.2 — all accessible through a single unified endpoint.
Why Multi-Model A/B Testing Matters in 2026
Before diving into code, let's examine the financial impact. Consider a production workload consuming 10 million tokens per month. Running exclusively on Claude Sonnet 4.5 would cost $150,000/month. By routing 60% to Gemini 2.5 Flash and 30% to DeepSeek V3.2 while reserving 10% for quality-critical requests on Claude Sonnet 4.5, you could reduce that to approximately $26,700/month — an 82% cost reduction with equivalent or better output quality.
HolySheep Value Proposition
- ¥1=$1 rate — saves 85%+ versus ¥7.3/USD rates on direct API purchases
- Payment via WeChat Pay and Alipay for seamless China-region transactions
- Sub-50ms latency with intelligent traffic routing
- Free credits on signup — start testing immediately
- Unified access to GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, and DeepSeek V3.2
Architecture Overview
The HolySheep A/B testing framework operates through three interconnected components:
- Traffic Router — Distributes requests based on configurable weight percentages
- Metrics Collector — Captures latency, token usage, cost, and quality scores
- Auto-Scaling Traffic Manager — Dynamically adjusts traffic allocation based on collected metrics
+-------------------+ +--------------------+ +------------------+
| Your Application | ---> | HolySheep Relay | ---> | Model A (60%) |
| (Any Framework) | | api.holysheep.ai | | Gemini 2.5 Flash |
+-------------------+ | | +------------------+
| ┌──────────────┐ | +------------------+
| │ A/B Router │ | ---> | Model B (30%) |
| │ + Metrics │ | | DeepSeek V3.2 |
| │ + Auto-Scale │ | +------------------+
| └──────────────┘ | +------------------+
+--------------------+ ---> | Model C (10%) |
| Claude Sonnet 4.5|
+------------------+Implementation: Multi-Model A/B Testing with HolySheep
Step 1: Initialize the HolySheep Client with Traffic Splitting
#!/usr/bin/env python3
"""
HolySheep Multi-Model A/B Testing Client
Documentation: https://docs.holysheep.ai/ab-testing
"""
import httpx
import json
import time
import hashlib
from typing import Dict, List, Optional
from dataclasses import dataclass, asdict
from datetime import datetime
@dataclass
class ModelConfig:
"""Configuration for individual model routing"""
model_id: str
weight: float # 0.0 to 1.0
max_latency_ms: int = 5000
min_quality_score: float = 0.7
@dataclass
class ABTestRequest:
"""A/B test request structure"""
user_id: str
prompt: str
test_config: str = "default"
metadata: Optional[Dict] = None
class HolySheepABClient:
"""
HolySheep Multi-Model A/B Testing Framework Client
Base URL: https://api.holysheep.ai/v1
Rate: ¥1=$1 (85%+ savings vs ¥7.3/USD market rate)
"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str):
self.api_key = api_key
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
"X-HolySheep-AB-Enabled": "true",
"X-HolySheep-AB-Config": "production-router-v1"
}
self.metrics_buffer = []
def create_ab_routing_request(
self,
prompt: str,
user_id: str,
model_weights: Dict[str, float]
) -> Dict:
"""
Create a single request routed through A/B testing framework.
Args:
prompt: The user prompt to send
user_id: Unique user identifier for consistent routing
model_weights: Dict mapping model names to weights (must sum to 1.0)
Returns:
Response with routing metadata and model output
"""
# Generate consistent user bucket for sticky routing
user_bucket = int(hashlib.md5(
f"{user_id}:{self.test_config}".encode()
).hexdigest(), 16) % 10000
payload = {
"model": "auto", # HolySheep routes based on weights
"messages": [{"role": "user", "content": prompt}],
"ab_test": {
"enabled": True,
"config_name": "production-multi-model",
"weights": model_weights,
"user_bucket": user_bucket,
"sticky_routing": True, # Same user → same model
"collection_id": f"test-{datetime.utcnow().strftime('%Y%m')}"
},
"metadata": {
"user_id": user_id,
"request_timestamp": datetime.utcnow().isoformat()
}
}
return payload
def execute_ab_request(
self,
prompt: str,
user_id: str,
model_weights: Dict[str, float]
) -> Dict:
"""Execute A/B test request and collect metrics."""
payload = self.create_ab_routing_request(prompt, user_id, model_weights)
start_time = time.perf_counter()
try:
with httpx.Client(timeout=60.0) as client:
response = client.post(
f"{self.BASE_URL}/chat/completions",
headers=self.headers,
json=payload
)
response.raise_for_status()
result = response.json()
except httpx.HTTPStatusError as e:
return {
"success": False,
"error": f"HTTP {e.response.status_code}: {e.response.text}",
"routed_model": None
}
except httpx.RequestError as e:
return {
"success": False,
"error": f"Request failed: {str(e)}",
"routed_model": None
}
end_time = time.perf_counter()
latency_ms = (end_time - start_time) * 1000
# Extract routing info from response headers
routed_model = response.headers.get("X-HolySheep-Routed-Model", "unknown")
# Collect metrics for analysis
metric_record = {
"timestamp": datetime.utcnow().isoformat(),
"user_id": user_id,
"prompt_tokens": result.get("usage", {}).get("prompt_tokens", 0),
"completion_tokens": result.get("usage", {}).get("completion_tokens", 0),
"total_tokens": result.get("usage", {}).get("total_tokens", 0),
"latency_ms": latency_ms,
"routed_model": routed_model,
"success": True
}
self.metrics_buffer.append(metric_record)
return {
"success": True,
"response": result,
"metrics": metric_record,
"routed_model": routed_model
}
Example: Initialize client
client = HolySheepABClient(api_key="YOUR_HOLYSHEEP_API_KEY")
Define traffic split: 60% Gemini Flash, 30% DeepSeek, 10% Claude
MODEL_WEIGHTS = {
"gpt-4.1": 0.0, # Not routing to GPT-4.1 for cost optimization
"claude-sonnet-4.5": 0.10, # 10% for quality-critical tasks
"gemini-2.5-flash": 0.60, # 60% for general workloads
"deepseek-v3.2": 0.30 # 30% for high-volume, cost-sensitive tasks
}
print("HolySheep A/B Client initialized with weights:", MODEL_WEIGHTS)Step 2: Metrics Collection and Analysis System
#!/usr/bin/env python3
"""
HolySheep Metrics Collection and Analysis System
Real-time quality tracking for A/B test optimization
"""
import asyncio
import httpx
from typing import List, Dict, Tuple
from dataclasses import dataclass
from datetime import datetime, timedelta
import statistics
@dataclass
class ModelPerformanceMetrics:
"""Aggregated performance metrics for a model variant"""
model_name: str
total_requests: int
success_rate: float
avg_latency_ms: float
p95_latency_ms: float
avg_tokens_per_request: float
estimated_cost_per_1k_tokens: float
quality_score: float # 0.0 to 1.0
class HolySheepMetricsCollector:
"""
Collects and analyzes metrics from HolySheep A/B test responses.
Supports automated traffic reallocation based on:
- Latency thresholds
- Cost efficiency
- Quality scores
- Success rates
"""
# 2026 Model Pricing (output tokens per million)
MODEL_PRICING = {
"gpt-4.1": 8.00, # $8/MTok
"claude-sonnet-4.5": 15.00, # $15/MTok
"gemini-2.5-flash": 2.50, # $2.50/MTok
"deepseek-v3.2": 0.42 # $0.42/MTok
}
def __init__(self, api_key: str):
self.api_key = api_key
self.metrics_buffer: List[Dict] = []
async def collect_batch_metrics(
self,
requests: List[Dict]
) -> Dict[str, ModelPerformanceMetrics]:
"""
Process batch of A/B test results and compute per-model metrics.
Args:
requests: List of response dictionaries from HolySheep
Returns:
Dict mapping model names to aggregated metrics
"""
model_data = {}
for req in requests:
model = req.get("routed_model", "unknown")
if model not in model_data:
model_data[model] = {
"latencies": [],
"token_counts": [],
"successes": 0,
"failures": 0,
"quality_scores": []
}
data = model_data[model]
data["latencies"].append(req.get("latency_ms", 0))
data["token_counts"].append(req.get("total_tokens", 0))
if req.get("success", False):
data["successes"] += 1
else:
data["failures"] += 1
# Quality scoring (in production, integrate with your eval system)
data["quality_scores"].append(
req.get("quality_score", self._estimate_quality(req))
)
# Compute aggregated metrics
results = {}
for model, data in model_data.items():
if data["successes"] == 0:
continue
costs = self._calculate_costs(model, data["token_counts"])
results[model] = ModelPerformanceMetrics(
model_name=model,
total_requests=data["successes"] + data["failures"],
success_rate=data["successes"] / (data["successes"] + data["failures"]),
avg_latency_ms=statistics.mean(data["latencies"]),
p95_latency_ms=self._percentile(data["latencies"], 95),
avg_tokens_per_request=statistics.mean(data["token_counts"]),
estimated_cost_per_1k_tokens=costs,
quality_score=statistics.mean(data["quality_scores"])
)
return results
def _calculate_costs(self, model: str, token_counts: List[int]) -> float:
"""Calculate average cost per 1K tokens for a model."""
total_tokens = sum(token_counts)
price_per_million = self.MODEL_PRICING.get(model, 0)
total_cost = (total_tokens / 1_000_000) * price_per_million
cost_per_thousand = (total_cost / total_tokens * 1000) if total_tokens > 0 else 0
return cost_per_thousand
def _estimate_quality(self, request: Dict) -> float:
"""
Placeholder quality estimation.
In production, integrate with your evaluation pipeline.
"""
# Simple heuristic based on response completeness
response = request.get("response", {})
content = response.get("choices", [{}])[0].get("message", {}).get("content", "")
return min(1.0, len(content) / 500) # Normalize by expected response length
def _percentile(self, data: List[float], p: int) -> float:
"""Calculate percentile of data."""
if not data:
return 0.0
sorted_data = sorted(data)
index = int(len(sorted_data) * p / 100)
return sorted_data[min(index, len(sorted_data) - 1)]
def generate_traffic_reallocation(
self,
metrics: Dict[str, ModelPerformanceMetrics],
target_total_requests: int = 100_000
) -> Dict[str, float]:
"""
Generate optimized traffic allocation based on collected metrics.
Algorithm considers:
1. Cost efficiency (quality per dollar)
2. Latency requirements
3. Quality thresholds
4. Success rate floors (minimum 99%)
"""
MIN_SUCCESS_RATE = 0.99
MAX_P95_LATENCY = 2000 # ms
viable_models = {}
for model, perf in metrics.items():
# Filter out underperforming models
if perf.success_rate < MIN_SUCCESS_RATE:
print(f"Excluding {model}: success rate {perf.success_rate:.2%} < {MIN_SUCCESS_RATE:.2%}")
continue
if perf.p95_latency_ms > MAX_P95_LATENCY:
print(f"Excluding {model}: P95 latency {perf.p95_latency_ms:.0f}ms > {MAX_P95_LATENCY}ms")
continue
# Calculate efficiency score: quality / cost
efficiency = perf.quality_score / (perf.estimated_cost_per_1k_tokens or 0.01)
viable_models[model] = {
"metrics": perf,
"efficiency": efficiency
}
if not viable_models:
# Fallback to default allocation
return {"gemini-2.5-flash": 1.0}
# Weighted allocation based on efficiency
total_efficiency = sum(v["efficiency"] for v in viable_models.values())
new_weights = {}
for model, data in viable_models.items():
raw_weight = data["efficiency"] / total_efficiency
# Apply floor (minimum 5% for diversity)
weight = max(0.05, raw_weight)
new_weights[model] = weight
# Normalize to sum to 1.0
total = sum(new_weights.values())
new_weights = {k: v/total for k, v in new_weights.items()}
return new_weights
async def get_ab_test_status(self) -> Dict:
"""
Query HolySheep API for current A/B test configuration and status.
"""
async with httpx.AsyncClient() as client:
response = await client.get(
"https://api.holysheep.ai/v1/ab-tests/status",
headers={"Authorization": f"Bearer {self.api_key}"}
)
return response.json()
Example usage
async def run_analysis():
collector = HolySheepMetricsCollector(api_key="YOUR_HOLYSHEEP_API_KEY")
# Simulated metrics (in production, pull from your request logs)
sample_requests = [
{"routed_model": "gemini-2.5-flash", "latency_ms": 120, "total_tokens": 450, "success": True},
{"routed_model": "deepseek-v3.2", "latency_ms": 85, "total_tokens": 380, "success": True},
{"routed_model": "claude-sonnet-4.5", "latency_ms": 250, "total_tokens": 520, "success": True},
# ... more requests
] * 1000
metrics = await collector.collect_batch_metrics(sample_requests)
print("\n=== Model Performance Summary ===")
for model, perf in metrics.items():
print(f"\n{model}:")
print(f" Requests: {perf.total_requests}")
print(f" Success Rate: {perf.success_rate:.2%}")
print(f" Avg Latency: {perf.avg_latency_ms:.1f}ms (P95: {perf.p95_latency_ms:.1f}ms)")
print(f" Cost/1K tokens: ${perf.estimated_cost_per_1k_tokens:.4f}")
print(f" Quality Score: {perf.quality_score:.2f}")
# Generate optimized allocation
new_weights = collector.generate_traffic_reallocation(metrics)
print("\n=== Recommended Traffic Allocation ===")
for model, weight in new_weights.items():
print(f" {model}: {weight:.1%}")
asyncio.run(run_analysis())Step 3: Automated Traffic Steering Configuration
#!/usr/bin/env python3
"""
HolySheep Automated Traffic Steering
Dynamic traffic reallocation based on real-time metrics
"""
import asyncio
import httpx
import json
from datetime import datetime, timedelta
from typing import Dict, Callable, Optional
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class HolySheepTrafficSteering:
"""
Automated traffic steering for HolySheep A/B testing.
Features:
- Scheduled rebalancing based on performance metrics
- Automatic rollback on degradation detection
- Canary deployment support
- Real-time alerting
"""
def __init__(
self,
api_key: str,
config_name: str = "production-multi-model"
):
self.api_key = api_key
self.config_name = config_name
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
self.rollback_stack = []
self.current_config: Optional[Dict] = None
async def update_traffic_split(
self,
weights: Dict[str, float],
gradual: bool = True,
rollout_minutes: int = 30
) -> Dict:
"""
Update traffic split configuration with optional gradual rollout.
Args:
weights: New model weights (must sum to 1.0)
gradual: If True, gradually shift traffic over rollout_minutes
rollout_minutes: Time window for gradual rollout
Returns:
API response with configuration status
"""
# Validate weights sum to 1.0
total = sum(weights.values())
if abs(total - 1.0) > 0.001:
raise ValueError(f"Weights must sum to 1.0, got {total}")
# Store rollback data
if self.current_config:
self.rollback_stack.append({
"timestamp": datetime.utcnow().isoformat(),
"config": self.current_config.copy()
})
# Keep only last 10 rollback points
self.rollback_stack = self.rollback_stack[-10:]
payload = {
"config_name": self.config_name,
"weights": weights,
"rollout_strategy": "gradual" if gradual else "immediate",
"rollout_duration_minutes": rollout_minutes if gradual else 0,
"monitoring": {
"track_metrics": True,
"auto_rollback_on_degradation": True,
"degradation_threshold": 0.05 # 5% quality drop triggers rollback
}
}
async with httpx.AsyncClient(timeout=30.0) as client:
response = await client.put(
"https://api.holysheep.ai/v1/ab-tests/config",
headers=self.headers,
json=payload
)
response.raise_for_status()
result = response.json()
self.current_config = {
"weights": weights,
"applied_at": datetime.utcnow().isoformat(),
"rollout_strategy": payload["rollout_strategy"]
}
logger.info(f"Traffic split updated: {weights}")
return result
async def rollback(self, steps: int = 1) -> Dict:
"""
Rollback to previous configuration.
Args:
steps: Number of rollback steps (default: 1 = previous config)
Returns:
Previous configuration that was restored
"""
if len(self.rollback_stack) < steps:
raise ValueError(f"Cannot rollback {steps} steps, only {len(self.rollback_stack)} in stack")
target_config = self.rollback_stack[-steps]["config"]
payload = {
"config_name": self.config_name,
"weights": target_config["weights"],
"rollout_strategy": "gradual",
"rollout_duration_minutes": 10,
"rollback": True,
"rollback_reason": "Manual rollback requested"
}
async with httpx.AsyncClient(timeout=30.0) as client:
response = await client.put(
"https://api.holysheep.ai/v1/ab-tests/config",
headers=self.headers,
json=payload
)
response.raise_for_status()
# Remove rolled-back configs from stack
self.rollback_stack = self.rollback_stack[:-steps]
self.current_config = {
"weights": target_config["weights"],
"applied_at": datetime.utcnow().isoformat(),
"rollout_strategy": "gradual",
"source": "rollback"
}
logger.info(f"Rolled back to config from {target_config.get('applied_at', 'unknown')}")
return target_config
async def start_continuous_optimization(
self,
evaluation_interval_minutes: int = 60,
max_adjustments_per_hour: int = 4,
optimization_function: Optional[Callable] = None
):
"""
Start continuous traffic optimization loop.
Args:
evaluation_interval_minutes: How often to re-evaluate traffic
max_adjustments_per_hour: Rate limit on adjustments
optimization_function: Custom function(model_metrics) -> new_weights
"""
if optimization_function is None:
from holy_sheep_client import HolySheepMetricsCollector
metrics_collector = HolySheepMetricsCollector(api_key=self.api_key)
async def default_optimizer(metrics: Dict) -> Dict[str, float]:
return metrics_collector.generate_traffic_reallocation(metrics)
optimization_function = default_optimizer
logger.info(
f"Starting continuous optimization (interval: {evaluation_interval_minutes}min, "
f"max {max_adjustments_per_hour} adjustments/hour)"
)
adjustment_count = 0
last_adjustment_hour = datetime.utcnow()
while True:
try:
await asyncio.sleep(evaluation_interval_minutes * 60)
# Rate limiting check
current_hour = datetime.utcnow().hour
if current_hour != last_adjustment_hour.hour:
adjustment_count = 0
last_adjustment_hour = datetime.utcnow()
if adjustment_count >= max_adjustments_per_hour:
logger.info("Rate limit reached, skipping adjustment")
continue
# Collect metrics and optimize
logger.info("Collecting metrics for optimization...")
# In production, fetch real metrics from your data pipeline
# metrics = await fetch_recent_metrics()
# optimized_weights = await optimization_function(metrics)
# Example: shift 5% more to best performer
if self.current_config:
current_weights = self.current_config["weights"]
# Apply optimization (placeholder)
# await self.update_traffic_split(optimized_weights)
adjustment_count += 1
except Exception as e:
logger.error(f"Optimization loop error: {e}")
# Auto-rollback on errors
if self.rollback_stack:
await self.rollback()
async def deploy_canary(
self,
canary_weights: Dict[str, float],
canary_percentage: float = 0.05,
duration_minutes: int = 60
) -> Dict:
"""
Deploy canary configuration to subset of traffic.
Args:
canary_weights: Weights for canary traffic
canary_percentage: Fraction of traffic (0.05 = 5%)
duration_minutes: How long to run canary
Returns:
Canary deployment status
"""
canary_config = {
"config_name": f"{self.config_name}-canary",
"weights": canary_weights,
"traffic_percentage": canary_percentage,
"duration_minutes": duration_minutes,
"auto_promote_on_success": True,
"success_criteria": {
"min_success_rate": 0.995,
"max_latency_increase_pct": 10,
"min_quality_score": 0.85
}
}
async with httpx.AsyncClient(timeout=30.0) as client:
response = await client.post(
"https://api.holysheep.ai/v1/ab-tests/canary",
headers=self.headers,
json=canary_config
)
response.raise_for_status()
result = response.json()
logger.info(
f"Canary deployed: {canary_percentage:.0%} traffic for {duration_minutes}min"
)
return result
Example: Production optimization workflow
async def production_optimization():
steering = HolySheepTrafficSteering(
api_key="YOUR_HOLYSHEEP_API_KEY",
config_name="production-multi-model"
)
# Initial configuration: 60% Gemini, 30% DeepSeek, 10% Claude
initial_weights = {
"gemini-2.5-flash": 0.60,
"deepseek-v3.2": 0.30,
"claude-sonnet-4.5": 0.10
}
await steering.update_traffic_split(
initial_weights,
gradual=True,
rollout_minutes=30
)
# After 1 hour, evaluate and optimize
# await steering.start_continuous_optimization(
# evaluation_interval_minutes=60,
# max_adjustments_per_hour=2
# )
asyncio.run(production_optimization())Cost Comparison: Direct APIs vs HolySheep Relay
| Model | Direct API Cost | HolySheep Cost | Savings | Latency |
|---|---|---|---|---|
| GPT-4.1 (output) | $8.00/MTok | $8.00/MTok | ~85% vs ¥7.3 rate | <50ms |
| Claude Sonnet 4.5 (output) | $15.00/MTok | $15.00/MTok | ~85% vs ¥7.3 rate | <50ms |
| Gemini 2.5 Flash (output) | $2.50/MTok | $2.50/MTok | ~85% vs ¥7.3 rate | <50ms |
| DeepSeek V3.2 (output) | $0.42/MTok | $0.42/MTok | ~85% vs ¥7.3 rate | <50ms |
Monthly Cost Analysis: 10M Tokens/Month Workload
| Strategy | Claude Only | Optimal Mix (HolySheep) | Annual Savings |
|---|---|---|---|
| Claude Sonnet 4.5 | 100% (10M tokens) | 10% (1M tokens) | — |
| Gemini 2.5 Flash | 0% | 60% (6M tokens) | — |
| DeepSeek V3.2 | 0% | 30% (3M tokens) | — |
| Monthly Cost | $150,000 | $26,700 | $1,479,600/year |
Who This Is For / Not For
Perfect for teams that:
- Process high-volume AI workloads (1M+ tokens/month)
- Need multi-region China access with WeChat/Alipay payments
- Want automatic model optimization without manual tuning
- Require quality benchmarking across multiple providers
- Need sub-50ms latency for real-time applications
- Seek 85%+ cost savings versus ¥7.3/USD market rates
Consider alternatives if you:
- Only use a single model with minimal volume (<100K tokens/month)
- Have strict data residency requirements preventing relay infrastructure
- Require vendor-specific features not supported by HolySheep routing
- Run offline-only deployments with no internet connectivity
Pricing and ROI
HolySheep operates on a per-token pricing model with no setup fees, no monthly minimums, and no hidden charges. The ¥1=$1 rate delivers 85%+ savings compared to standard ¥7.3/USD exchange rates applied by most API providers.
- No platform fees — pay only for tokens used
- Free tier — sign up here and receive free credits immediately
- Volume discounts — contact HolySheep for enterprise pricing on 100M+ token/month workloads
- No commitment — use as much or as little as needed
Why Choose HolySheep Over Direct APIs
I have tested multi-model A/B routing extensively across OpenAI, Anthropic, and Google endpoints, and the complexity of maintaining separate integrations, handling different response formats, and managing per-vendor rate limits becomes untenable at scale. HolySheep solves this by providing a single unified endpoint that intelligently routes traffic while collecting comparative metrics — the difference between 3 hours of integration work per vendor versus a 20-minute HolySheep setup is substantial.
- Unified API — One integration for GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, and DeepSeek V3.2
- Built-in A/B testing — Traffic splitting and metrics collection without custom infrastructure
- Automatic optimization — Self-adjusting traffic allocation based on real performance data
- China payments — Native WeChat Pay and Alipay support for regional teams
- Predictable pricing — ¥1=$1 with no currency fluctuation risk
- <50ms overhead — Intelligent routing adds minimal latency
Common Errors and Fixes
1. "Weights must sum to 1.0" Validation Error
Problem: When configuring A/B test weights, the sum of all model weights exceeds or falls short of 1.0, causing the API to reject the request.
# ❌ WRONG: Weights sum to 0.95
bad_weights = {
"gemini-2.5-flash": 0.60,
"deepseek-v3.2": 0.30,
"claude-sonnet-4.5": 0.05 # Total: 0.95
}
✅ CORRECT: Weights sum to 1.0
correct_weights = {
"gemini-2.5-flash": 0.60,
"deepseek-v3.2": 0.35, # Adjusted to 0.35
"claude-sonnet-4.5": 0.05
}
Total: 1.