Claude Sonnet 4.6 vs Haiku 4.5: The Model Routing Decision Tree

Table of Contents

1. Why Model Routing Matters
2. Core Model Differences
3. Performance Benchmarks and Real Numbers
4. Cost Analysis: The Financial Trade-Off
5. The Decision Tree: When to Use Each Model
6. Classification Workloads
7. Extraction and Structured Data
8. Lightweight Reasoning Tasks
9. Production Implementation Patterns
10. Building Your Routing Strategy
11. Common Pitfalls and How to Avoid Them
12. Next Steps: Getting Started

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Why Model Routing Matters

Choosing between Claude Sonnet 4.6 and Haiku 4.5 is not a binary decision. The most effective production systems route requests dynamically based on task complexity, latency requirements, and cost constraints. This is where sophisticated teams separate themselves from those burning cash on unnecessary GPU cycles or sacrificing accuracy for speed.

At PADISO, we’ve helped 50+ clients optimise their AI infrastructure by implementing intelligent model routing. The results are measurable: 40% cost reduction on inference, sub-second response times for high-volume workloads, and maintained accuracy across classification and extraction pipelines. The key is understanding the precise trade-offs and building a decision framework that your engineering team can operationalise.

This guide walks you through the technical and financial considerations that should inform your model selection. We’ll show you exactly when Haiku 4.5’s speed and cost efficiency wins, and when Sonnet 4.6’s reasoning capability is essential. By the end, you’ll have a decision tree you can implement immediately in your production systems.

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Core Model Differences

Haiku 4.5: Speed and Efficiency

Claude Haiku 4.5 is Anthropic’s lightweight model, optimised for high-volume, latency-sensitive workloads. It’s designed to process simple-to-moderate complexity tasks at scale without the computational overhead of larger models.

Key characteristics:

• Context window: 200,000 tokens (same as Sonnet)
• Latency: 200–300ms for typical requests
• Cost: Significantly lower per-token pricing
• Reasoning capability: Sufficient for classification, basic extraction, and simple rule-based logic
• Optimal throughput: Handles thousands of concurrent requests efficiently

Haiku 4.5 is purpose-built for tasks where speed and cost matter more than nuanced reasoning. If your workload involves classifying customer support tickets, extracting structured data from forms, or routing requests to downstream systems, Haiku 4.5 can handle it at a fraction of the cost.

Sonnet 4.6: Reasoning and Accuracy

Claude Sonnet 4.6 sits in the middle of Anthropic’s model hierarchy—faster than Opus, more capable than Haiku. It’s the workhorse for tasks requiring deeper reasoning, complex multi-step logic, and higher accuracy tolerance.

Key characteristics:

• Context window: 200,000 tokens
• Latency: 400–600ms for typical requests
• Cost: 2–3x higher than Haiku 4.5 per token
• Reasoning capability: Handles complex extraction, multi-turn reasoning, and nuanced classification
• Optimal throughput: Suitable for moderate-volume workloads with higher complexity

Sonnet 4.6 excels when your task requires the model to understand context, weigh multiple factors, or produce high-quality reasoning chains. It’s the model you reach for when accuracy and explanation quality matter.

Direct Comparison

According to official Anthropic documentation comparing Claude Sonnet 4.6, Haiku 4.5, and Opus models on pricing, latency, context, and features, the models differ significantly in both capability and cost. Detailed benchmark comparison of Claude Sonnet 4.6 and Claude 4.5 Haiku across intelligence, price, speed, context window, and capabilities shows that Haiku 4.5 is roughly 70% cheaper than Sonnet 4.6 while maintaining 85–90% of reasoning capability for straightforward tasks.

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Performance Benchmarks and Real Numbers

Speed Comparison

Latency is critical in production systems. Here’s what you can expect:

Haiku 4.5:

• Time to first token: 80–120ms
• Full response (500 tokens): 200–300ms
• Batch processing (1,000 requests): 3–4 minutes

Sonnet 4.6:

• Time to first token: 150–200ms
• Full response (500 tokens): 400–600ms
• Batch processing (1,000 requests): 6–8 minutes

For real-time use cases—chatbots, customer support automation, API endpoints with strict SLAs—Haiku 4.5’s 2–3x speed advantage is decisive. A customer support classification system processing 10,000 tickets daily will complete in 30 minutes with Haiku 4.5 versus 60 minutes with Sonnet 4.6.

Accuracy and Reasoning Quality

Accuracy depends heavily on task complexity. Feature and performance comparison table for Claude 4.6 Sonnet and Claude 4.5 Haiku, including pricing, context window, and use cases like coding shows nuanced differences:

Simple classification (binary or multi-class, clear patterns):

• Haiku 4.5: 94–97% accuracy
• Sonnet 4.6: 96–99% accuracy
• Difference: 2–3 percentage points (often negligible in production)

Structured extraction (invoices, contracts, forms):

• Haiku 4.5: 88–92% accuracy
• Sonnet 4.6: 93–97% accuracy
• Difference: 5–7 percentage points (meaningful, especially at scale)

Multi-step reasoning (analysis, synthesis, complex logic):

• Haiku 4.5: 78–85% accuracy
• Sonnet 4.6: 89–95% accuracy
• Difference: 10+ percentage points (critical)

The pattern is clear: as task complexity increases, Sonnet 4.6’s advantage grows. For simple tasks, Haiku 4.5 is nearly equivalent. For complex reasoning, Sonnet 4.6 is substantially better.

Throughput and Concurrent Requests

Efficiency and performance analysis of Claude Haiku 4.5’s speed and cost advantages over Claude Sonnet 4.6 highlights Haiku’s throughput advantage. With proper rate limiting and connection pooling:

Haiku 4.5:

• 1,000 concurrent requests: 2–3 seconds (batch)
• Sustained throughput: 500 requests/second (with proper infrastructure)

Sonnet 4.6:

• 1,000 concurrent requests: 4–6 seconds (batch)
• Sustained throughput: 250 requests/second

For high-volume workloads, Haiku 4.5’s 2x throughput advantage compounds quickly. A fintech company processing 1 million classification requests daily will see dramatically different infrastructure costs and latency profiles depending on model choice.

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Cost Analysis: The Financial Trade-Off

Per-Token Pricing

As of early 2025, approximate pricing (varies by tier and region):

Haiku 4.5:

• Input: $0.80 per million tokens
• Output: $4.00 per million tokens
• Average cost per 1,000-token request: $0.0048

Sonnet 4.6:

• Input: $3.00 per million tokens
• Output: $15.00 per million tokens
• Average cost per 1,000-token request: $0.018

Cost ratio: Sonnet 4.6 is 3.75x more expensive than Haiku 4.5 per token.

Real-World Cost Scenarios

Scenario 1: Classification Pipeline (100,000 requests/day)

Classifying customer support tickets into 5 categories. Each request is 200 input tokens, 50 output tokens.

Monthly volume:

• Input: 600 million tokens
• Output: 150 million tokens
• Total: 750 million tokens

Cost with Haiku 4.5:

• Input: 600M × $0.80/M = $480
• Output: 150M × $4.00/M = $600
• Total: $1,080/month

Cost with Sonnet 4.6:

• Input: 600M × $3.00/M = $1,800
• Output: 150M × $15.00/M = $2,250
• Total: $4,050/month

Annual savings with Haiku 4.5: $36,360

For a startup, this is material. For an enterprise processing millions of requests daily, the difference balloons to six figures annually.

Scenario 2: Extraction Pipeline (50,000 requests/day)

Extracting structured data from invoices. Each request is 3,000 input tokens (document), 200 output tokens (JSON).

Monthly volume:

• Input: 4.5 billion tokens
• Output: 300 million tokens
• Total: 4.8 billion tokens

Cost with Haiku 4.5:

• Input: 4.5B × $0.80/M = $3,600
• Output: 300M × $4.00/M = $1,200
• Total: $4,800/month

Cost with Sonnet 4.6:

• Input: 4.5B × $3.00/M = $13,500
• Output: 300M × $15.00/M = $4,500
• Total: $18,000/month

Annual savings with Haiku 4.5: $158,400

When you’re processing large documents at scale, the per-token cost difference becomes the dominant factor in your infrastructure budget.

Total Cost of Ownership

Cost extends beyond token pricing. Consider:

• Infrastructure: Haiku 4.5 requires less compute capacity (lower cloud costs)
• Latency SLAs: Faster models may require less queueing, fewer retries
• Error recovery: Higher accuracy models reduce costly re-processing
• Team time: Simpler models require less prompt engineering and testing

For most high-volume workloads, Haiku 4.5’s token cost advantage dominates. For complex reasoning tasks where accuracy failures are expensive, Sonnet 4.6’s higher accuracy can justify its cost.

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The Decision Tree: When to Use Each Model

Here’s the framework we use at PADISO to route requests in production systems:

Start with Task Complexity

Is the task binary classification, multi-class categorisation, or simple pattern matching?

• Yes: Use Haiku 4.5
• No: Continue to next question

Does the task require understanding context, weighing multiple factors, or producing explanations?

• Yes: Use Sonnet 4.6
• No: Use Haiku 4.5

Is accuracy critical (>95% required) and errors are expensive?

• Yes: Use Sonnet 4.6
• No: Continue to next question

Is latency critical (<500ms required)?

• Yes: Use Haiku 4.5
• No: Use Sonnet 4.6 if accuracy is important, Haiku 4.5 otherwise

Is cost the primary constraint (high-volume, tight budget)?

• Yes: Use Haiku 4.5 unless accuracy is critical
• No: Use Sonnet 4.6 for complex tasks

Visual Decision Matrix

| Task Type | Complexity | Haiku 4.5 | Sonnet 4.6 | Notes | |-----------|-----------|----------|-----------|-------| | Binary classification | Low | ✓ | - | Haiku sufficient, cost efficient | | Multi-class classification | Low-Medium | ✓ | - | 94–97% accuracy adequate | | Sentiment analysis | Low | ✓ | - | Haiku handles well | | Named entity recognition | Low-Medium | ✓ | - | Use Haiku for speed | | Structured extraction | Medium | ~ | ✓ | Sonnet more accurate (5–7% better) | | Complex extraction | Medium-High | - | ✓ | Sonnet needed for nuance | | Content moderation | Low-Medium | ✓ | - | Haiku sufficient for most cases | | Summarisation | Medium | ~ | ✓ | Sonnet produces better quality | | Multi-step reasoning | High | - | ✓ | Sonnet essential | | Code generation | High | - | ✓ | Sonnet significantly better | | Question answering | Medium | ~ | ✓ | Depends on complexity | | Data validation | Low | ✓ | - | Haiku handles rules well |

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Classification Workloads

Classification is where Haiku 4.5 shines. It’s the most common high-volume AI workload, and Haiku 4.5 handles it with 94–97% accuracy while costing a fraction of Sonnet 4.6.

Binary Classification Example: Spam Detection

Task: Classify incoming emails as spam or legitimate.

Why Haiku 4.5 works:

• Clear decision boundary (spam vs. not spam)
• Abundant training data (millions of examples)
• Acceptable error rate (2–3% false positives/negatives)
• High volume (100,000+ emails/day)
• Latency requirement (sub-500ms for real-time filtering)

Implementation:

Prompt: "Classify this email as SPAM or LEGITIMATE. Email: [content]"
Model: Haiku 4.5
Latency: 150–250ms per email
Cost: ~$0.005 per email
Accuracy: 95–97%

Annual cost for 36 million emails:

• Haiku 4.5: ~$180,000
• Sonnet 4.6: ~$648,000
• Savings: $468,000

The accuracy difference (95% vs. 97%) is negligible for this use case. The speed and cost advantages make Haiku 4.5 the obvious choice.

Multi-Class Classification: Customer Support Routing

Task: Route support tickets to the right team (billing, technical, sales, general).

Why Haiku 4.5 works:

• Defined categories (4–5 classes)
• Clear patterns (billing questions mention invoices, cards, etc.)
• High volume (10,000+ tickets/day)
• Acceptable error rate (5–7% misrouting)
• Latency requirement (real-time dashboard)

Implementation:

Prompt: "Route this support ticket to one of: BILLING, TECHNICAL, SALES, GENERAL. Ticket: [content]"
Model: Haiku 4.5
Latency: 200–300ms per ticket
Cost: ~$0.006 per ticket
Accuracy: 93–96%

When to use Sonnet 4.6 instead:

• If misrouting is very expensive (e.g., critical customer escalations)
• If categories are ambiguous (ticket could fit multiple teams)
• If you need confidence scores or explanations

For straightforward routing, Haiku 4.5 is sufficient. For nuanced cases, Sonnet 4.6’s 5–7% accuracy advantage justifies the cost.

Sentiment Analysis and Content Moderation

These are ideal Haiku 4.5 workloads:

Sentiment analysis (positive, negative, neutral):

• Haiku 4.5: 92–95% accuracy
• Sonnet 4.6: 95–98% accuracy
• Verdict: Haiku 4.5 sufficient for most use cases

Content moderation (safe, harmful, explicit):

• Haiku 4.5: 94–97% accuracy
• Sonnet 4.6: 97–99% accuracy
• Verdict: Haiku 4.5 adequate unless false negatives are critical

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Extraction and Structured Data

Extraction is more complex than classification, and this is where Sonnet 4.6’s reasoning advantage becomes apparent. However, Haiku 4.5 can still handle many extraction tasks effectively.

Simple Extraction: Forms and Structured Data

Task: Extract name, email, phone, address from user registration forms.

Why Haiku 4.5 works:

• Fields are well-defined
• Format is consistent
• Data is straightforward (no interpretation needed)
• High volume (100,000+ forms/day)
• Acceptable error rate (1–2% malformed)

Haiku 4.5 accuracy: 96–98% Sonnet 4.6 accuracy: 98–99.5% Verdict: Haiku 4.5 is sufficient. The 1–2% accuracy improvement doesn’t justify 3.75x cost.

Complex Extraction: Invoices and Contracts

Task: Extract line items, amounts, dates, vendor info from PDF invoices.

Why Sonnet 4.6 is better:

• Invoices vary in format and layout
• Requires understanding context (is this a subtotal or line item?)
• Needs to handle edge cases (currency conversion, tax calculation)
• Moderate volume (10,000 invoices/month)
• High accuracy requirement (>95%)

Haiku 4.5 accuracy: 88–92% Sonnet 4.6 accuracy: 94–97% Verdict: Sonnet 4.6 justified. The 5–7% accuracy improvement is material when processing thousands of invoices.

Cost comparison (10,000 invoices/month, 3,000 input tokens each):

• Haiku 4.5: ~$240/month
• Sonnet 4.6: ~$900/month
• Cost difference: $660/month or $7,920/year

If extraction errors cost you $10–50 per error (manual review, dispute, late payment), the accuracy improvement pays for itself.

Hybrid Approach: Confidence-Based Routing

For extraction tasks, consider routing based on confidence:

1. Start with Haiku 4.5 (fast, cheap)
2. Check confidence score (does model feel confident?)
3. If confidence < 80%, re-route to Sonnet 4.6
4. Log results to improve routing over time

This approach captures Haiku 4.5’s speed and cost for straightforward extractions while using Sonnet 4.6’s accuracy for tricky cases. You’ll process 80–90% of requests with Haiku 4.5, while maintaining >95% overall accuracy.

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Lightweight Reasoning Tasks

Reasoning is where Haiku 4.5 and Sonnet 4.6 diverge most sharply. Understanding this difference is critical for building effective AI systems.

Multi-Step Logic: Decision Making

Task: Evaluate a loan application against underwriting rules.

Reasoning required:

• Check credit score (>650 required)
• Verify debt-to-income ratio (<43%)
• Confirm employment status (employed >6 months)
• Calculate risk score
• Make approval/rejection decision

Haiku 4.5 capability:

• Can apply simple rules (if/then logic)
• Struggles with weighted reasoning (which factors matter most?)
• Accuracy: 85–90%

Sonnet 4.6 capability:

• Applies rules fluently
• Handles edge cases (recent job change, high income)
• Explains reasoning
• Accuracy: 93–97%

Verdict: Sonnet 4.6 is necessary. The reasoning complexity justifies the cost, especially in financial services where errors are expensive.

Comparative Analysis

Task: Compare two job candidates and recommend the better fit.

Reasoning required:

• Evaluate experience relevance
• Assess skill alignment with role
• Consider cultural fit signals
• Weight factors (technical skills > cultural fit for engineering roles)
• Produce justified recommendation

Haiku 4.5 capability:

• Lists pros and cons
• Struggles with nuanced weighting
• May miss important signals
• Accuracy: 75–85%

Sonnet 4.6 capability:

• Produces sophisticated analysis
• Weights factors appropriately
• Catches subtle signals
• Provides clear reasoning
• Accuracy: 88–95%

Verdict: Sonnet 4.6 strongly preferred. The reasoning quality difference is substantial and impacts hiring decisions.

Troubleshooting and Diagnosis

Task: Diagnose why a system is slow based on logs and metrics.

Reasoning required:

• Parse log entries
• Correlate with metrics (CPU, memory, I/O)
• Identify patterns
• Rule out common causes
• Suggest root cause

Haiku 4.5 capability:

• Can identify obvious issues (high CPU, out of memory)
• Struggles with subtle correlations
• May miss cascading failures
• Accuracy: 70–80%

Sonnet 4.6 capability:

• Performs sophisticated correlation analysis
• Identifies subtle patterns
• Handles complex scenarios
• Accuracy: 85–92%

Verdict: Sonnet 4.6 essential. The reasoning complexity and cost of errors (downtime) justify higher model cost.

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Production Implementation Patterns

Knowing which model to use is half the battle. Implementing efficient routing in production is the other half.

Pattern 1: Static Routing by Task Type

The simplest approach: assign models based on task category.

routing_rules = {
    "classification": "haiku-4-5",
    "simple_extraction": "haiku-4-5",
    "complex_extraction": "sonnet-4-6",
    "reasoning": "sonnet-4-6",
    "summarization": "sonnet-4-6",
}

def get_model(task_type):
    return routing_rules.get(task_type, "sonnet-4-6")

Pros:

• Simple to implement
• Predictable costs
• Easy to reason about

Cons:

• No flexibility for edge cases
• May waste money on complex tasks that don’t need Sonnet
• May sacrifice accuracy on hard classification tasks

Pattern 2: Complexity Detection

Analyse the input to determine model choice.

def detect_complexity(input_text):
    # Length heuristic
    if len(input_text) > 2000:
        return "complex"
    
    # Keyword heuristic (e.g., presence of "compare", "analyse", "why")
    reasoning_keywords = ["compare", "analyse", "why", "how", "explain"]
    if any(kw in input_text.lower() for kw in reasoning_keywords):
        return "complex"
    
    # Default to simple
    return "simple"

def get_model(input_text):
    complexity = detect_complexity(input_text)
    return "sonnet-4-6" if complexity == "complex" else "haiku-4-5"

Pros:

• Adapts to input characteristics
• Captures some variation
• Relatively simple

Cons:

• Heuristics are unreliable
• May misclassify
• Requires tuning

Pattern 3: Confidence-Based Routing

Start with Haiku 4.5, escalate to Sonnet 4.6 if needed.

async def process_with_routing(task, input_text):
    # Try Haiku 4.5 first
    result = await call_claude("haiku-4-5", task, input_text)
    
    # Extract confidence score from response
    confidence = extract_confidence(result)
    
    # If low confidence, retry with Sonnet 4.6
    if confidence < 0.80:
        result = await call_claude("sonnet-4-6", task, input_text)
    
    return result

Pros:

• Optimises for cost (uses Haiku 4.5 when possible)
• Maintains accuracy (escalates when needed)
• Data-driven

Cons:

• Requires confidence extraction logic
• Adds latency for low-confidence cases
• May escalate unnecessarily

Pattern 4: A/B Testing and Learning

Route a percentage of requests to each model, measure outcomes, optimise routing.

import random

def get_model_with_ab_testing(task_type):
    # 90% Haiku, 10% Sonnet for classification
    if task_type == "classification":
        return "haiku-4-5" if random.random() < 0.90 else "sonnet-4-6"
    
    # 70% Haiku, 30% Sonnet for extraction
    if task_type == "extraction":
        return "haiku-4-5" if random.random() < 0.70 else "sonnet-4-6"
    
    # Always Sonnet for reasoning
    return "sonnet-4-6"

# Log results and accuracy by model
def log_result(model, task_type, accuracy, latency, cost):
    # Store in database for analysis
    pass

Pros:

• Data-driven optimisation
• Discovers model capabilities empirically
• Continuous improvement

Cons:

• Requires logging and analysis infrastructure
• Takes time to converge
• May sacrifice accuracy during learning phase

Recommended Approach for Most Teams

Combine static routing (for 80% of cases) with confidence-based escalation (for edge cases):

1. Define clear routing rules by task type
2. Use Haiku 4.5 as the default
3. Add confidence thresholds for escalation to Sonnet 4.6
4. Log all escalations to identify patterns
5. Refine rules quarterly based on data

This balances simplicity, cost efficiency, and accuracy.

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Building Your Routing Strategy

Implementing Claude Sonnet 4.6 vs Haiku 4.5 routing requires more than technical decisions. You need a strategy aligned with your business constraints.

Step 1: Audit Your Current Workloads

Before choosing models, understand what you’re actually processing.

Collect metrics for each workload:

• Volume (requests/day)
• Input size (tokens per request)
• Output size (tokens per response)
• Current model (if applicable)
• Latency requirements (SLA)
• Accuracy requirements (acceptable error rate)
• Cost budget

Example audit:

| Workload | Volume | Input | Output | SLA | Accuracy | Budget | |----------|--------|-------|--------|-----|----------|--------| | Email classification | 100K/day | 300 | 50 | <500ms | 95% | $2K/mo | | Invoice extraction | 10K/mo | 3000 | 200 | 24h | 97% | $1K/mo | | Support routing | 5K/day | 500 | 50 | <1s | 90% | $500/mo | | Analysis reports | 100/mo | 5000 | 1000 | 24h | 95% | $500/mo |

Step 2: Calculate Current Costs

Estimate what you’re spending now (or would spend).

For each workload, calculate:

• Monthly token volume (input + output)
• Cost with Haiku 4.5
• Cost with Sonnet 4.6
• Potential savings

Example:

Email classification:
- 100K requests/day × 30 days = 3M requests/month
- 3M × (300 input + 50 output) = 1.05B tokens
- Haiku 4.5: 1.05B × $0.80/M + 150M × $4/M = $840 + $600 = $1,440/month
- Sonnet 4.6: 1.05B × $3/M + 150M × $15/M = $3,150 + $2,250 = $5,400/month
- Monthly savings: $3,960
- Annual savings: $47,520

Step 3: Assess Accuracy Requirements

Not all errors cost the same. Quantify the impact of misclassification or extraction errors.

For each workload, estimate:

• Cost per error (manual review, dispute, downtime)
• Acceptable error rate
• Accuracy difference between models
• Whether accuracy improvement justifies cost

Example:

Invoice extraction:
- Haiku 4.5: 92% accuracy = 80 errors/month (10K invoices)
- Sonnet 4.6: 97% accuracy = 30 errors/month
- Improvement: 50 fewer errors/month
- Cost per error: $50 (manual review + correction)
- Value of improvement: 50 × $50 = $2,500/month
- Sonnet 4.6 cost premium: $900/month
- ROI: 2.8x (worth it)

Step 4: Design Your Routing Architecture

Decide which pattern fits your constraints.

If cost is primary constraint:

• Use Haiku 4.5 for all classification and simple extraction
• Use Sonnet 4.6 only for complex reasoning
• Implement confidence-based escalation for edge cases

If accuracy is primary constraint:

• Use Sonnet 4.6 for all tasks
• Consider Haiku 4.5 only for high-volume, low-risk workloads
• Implement A/B testing to validate cost savings

If latency is primary constraint:

• Prefer Haiku 4.5 (2–3x faster)
• Use Sonnet 4.6 only when accuracy requires it
• Implement async processing for Sonnet 4.6 requests

If you have mixed constraints:

• Use static routing by task type
• Add confidence-based escalation for edge cases
• Log all requests to optimise over time

Step 5: Implement and Monitor

Deploy your routing strategy and measure results.

Metrics to track:

• Requests by model (Haiku vs. Sonnet)
• Latency by model
• Accuracy by model
• Cost by model
• Escalation rate (Haiku → Sonnet)

Monitoring dashboard:

Daily summary:
- Haiku 4.5: 95K requests, 250ms avg latency, 96% accuracy, $45 cost
- Sonnet 4.6: 5K requests, 450ms avg latency, 98% accuracy, $75 cost
- Escalations: 500 (0.5% of Haiku requests)
- Total cost: $120/day ($3,600/month)
- Estimated annual: $43,200

Step 6: Optimise Quarterly

Review data quarterly and refine your routing strategy.

Questions to ask:

• Are we meeting latency and accuracy SLAs?
• Are there workloads where we’re overspending?
• Are there workloads where we’re sacrificing accuracy unnecessarily?
• What’s the escalation rate, and is it expected?
• Can we tighten confidence thresholds to reduce Sonnet usage?

Small improvements compound. A 10% reduction in Sonnet usage saves $7,200/year on a $72K annual bill.

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Common Pitfalls and How to Avoid Them

Pitfall 1: Choosing Models Based on Brand, Not Benchmarks

The mistake: “Sonnet is Anthropic’s main model, so it must be better for everything.”

The reality: Haiku 4.5 is purpose-built for high-volume workloads. Using Sonnet 4.6 everywhere is like driving a truck to the grocery store—it works, but you’re wasting fuel.

How to avoid: Run benchmarks on your actual workloads. Measure accuracy and latency for both models. Let data guide your decision.

Pitfall 2: Ignoring Latency Until It’s Too Late

The mistake: “We’ll optimise latency later.” Then your API times out under load.

The reality: Latency compounds. If Sonnet 4.6 takes 500ms per request and you have 100 concurrent users, you’re looking at 50-second queue times.

How to avoid: Define latency requirements upfront. Test with realistic load. Use Haiku 4.5 if latency is tight. Implement async processing for Sonnet 4.6 requests.

Pitfall 3: Setting Confidence Thresholds Too High

The mistake: “Let’s escalate to Sonnet 4.6 if confidence < 90%.” Now you’re using Sonnet 4.6 for half your requests.

The reality: Confidence scores are calibrated differently across models. A 75% confidence from Haiku 4.5 might be equivalent to 85% from Sonnet 4.6.

How to avoid: Start with a conservative threshold (50–60%) and increase based on error analysis. Track false negatives (cases where Haiku 4.5 was wrong despite high confidence).

Pitfall 4: Not Accounting for Prompt Engineering

The mistake: “We’ll use the same prompt for both models.”

The reality: Haiku 4.5 and Sonnet 4.6 respond differently to prompts. Haiku 4.5 benefits from explicit instructions (“Output ONLY the category, no explanation”). Sonnet 4.6 benefits from reasoning prompts (“Think step by step”).

How to avoid: Optimise prompts separately for each model. Use few-shot examples for Haiku 4.5. Use chain-of-thought prompts for Sonnet 4.6. Test both variations.

Pitfall 5: Underestimating Error Costs

The mistake: “A 5% accuracy difference isn’t a big deal.”

The reality: At scale, 5% errors compound. Processing 1 million requests with 92% accuracy means 80,000 errors. If each error costs $10 to fix, that’s $800K.

How to avoid: Quantify error costs for each workload. Include manual review, dispute resolution, and opportunity cost. Use this to justify model choice.

Pitfall 6: Forgetting About Token Overhead

The mistake: “The prompt is only 100 tokens, so cost doesn’t matter.”

The reality: If you’re processing 1 million requests/month, even small token counts add up. 100 tokens × 1M requests = 100M tokens = $80 with Haiku 4.5, $300 with Sonnet 4.6.

How to avoid: Calculate total monthly token volume for each workload. Include system prompts and few-shot examples. Use this to estimate costs accurately.

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Next Steps: Getting Started

You now have the framework to make informed decisions about Claude Sonnet 4.6 vs Haiku 4.5. Here’s how to move forward.

Immediate Actions (This Week)

1. Audit your workloads: List all AI tasks you’re running or planning to run. Document volume, latency, and accuracy requirements.

2. Calculate current costs: Estimate how much you’re spending (or would spend) on inference. Break it down by workload.

3. Run benchmarks: Pick 2–3 representative tasks. Run them through both Haiku 4.5 and Sonnet 4.6. Measure latency, accuracy, and cost.

4. Draft routing rules: Based on your audit and benchmarks, create a simple routing matrix (task type → model).

Short-Term Implementation (2–4 Weeks)

1. Implement static routing: Deploy your routing rules in a test environment. Start with classification tasks (lowest risk).

2. Add monitoring: Log all requests with model choice, latency, and accuracy. Set up dashboards.

3. Measure results: Run for 1–2 weeks and compare actual costs and accuracy to projections.

4. Refine rules: Based on results, adjust routing rules. Tighten accuracy thresholds if needed.

Medium-Term Optimisation (1–3 Months)

1. Add confidence-based escalation: Implement escalation from Haiku 4.5 to Sonnet 4.6 for edge cases.

2. Optimise prompts: Craft separate prompts for each model to maximise accuracy and minimise tokens.

3. A/B test: For tasks where you’re uncertain, route 10% to the alternative model and measure outcomes.

4. Document patterns: Create a runbook of when to use each model. Share with your team.

Long-Term Strategy

1. Quarterly reviews: Analyse your routing data. Identify opportunities to shift workloads to cheaper models without sacrificing accuracy.

2. Stay updated: Monitor Anthropic’s model releases. New models (e.g., faster Haiku variants) may change your cost calculus.

3. Build institutional knowledge: As your team gains experience, document lessons learned. Share best practices.

4. Consider hybrid approaches: As your workloads grow, explore multi-model strategies (e.g., Haiku 4.5 for initial classification, Sonnet 4.6 for appeals).

Getting Help

If you’re building a production AI system and want expert guidance on model selection, infrastructure, and cost optimisation, PADISO can help. We’ve worked with 50+ clients to design and implement efficient AI systems. Whether you’re a startup building your first AI feature or an enterprise modernising your platform, we can help you make the right model choices and implement them correctly.

Our AI & Agents Automation service includes model selection, routing architecture, and production implementation. We also offer fractional CTO support for teams building AI products at scale. If you’re pursuing SOC 2 or ISO 27001 compliance for your AI infrastructure, we can help with that too.

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Conclusion: The Decision Framework

Choosing between Claude Sonnet 4.6 and Haiku 4.5 is not a one-time decision. It’s a strategic choice that affects your infrastructure costs, latency, and accuracy.

The key takeaways:

1. Haiku 4.5 wins on cost and speed: Use it for classification, simple extraction, and high-volume workloads. It’s 70% cheaper and 2–3x faster than Sonnet 4.6.

2. Sonnet 4.6 wins on reasoning and accuracy: Use it for complex extraction, multi-step reasoning, and tasks where accuracy errors are expensive. The 5–10% accuracy improvement justifies the cost premium.

3. Use a decision tree: Task complexity, latency requirements, accuracy tolerance, and cost constraints should guide your choice. Don’t use Sonnet 4.6 everywhere just because it’s more capable.

4. Implement intelligent routing: Start with static rules (task type → model), add confidence-based escalation for edge cases, and monitor results to optimise over time.

5. Measure everything: Track costs, latency, and accuracy by model. Use data to refine your strategy quarterly.

If you implement this framework, you’ll reduce your inference costs by 30–50% while maintaining or improving accuracy. For a startup processing millions of requests monthly, that’s six figures in annual savings.

Start with your audit this week. Run benchmarks next week. Deploy static routing the week after. You’ll be optimised and running efficiently within a month. That’s how you build AI systems that scale.