Claude Haiku Vs Sonnet Vs Opus

The Model Tier Overview

Claude Haiku: The smallest, fastest, most affordable model. Designed for high-volume, simpler tasks where speed and cost efficiency are paramount. Think of it as the specialist for tasks that don't require deep reasoning — classification, short extraction, simple Q&A, and data transformation.

Claude Sonnet: The balanced model — the best combination of capability and cost for most business applications. Handles complex analysis, long-form writing, nuanced reasoning, and sophisticated instruction following at a cost that's economically viable for production workloads.

Claude Opus: The most capable model in the Claude family. Reserved for the hardest tasks where maximum intelligence matters more than cost or speed — complex multi-step reasoning, advanced research synthesis, nuanced creative work, and problems that require the deepest thinking.

When to Use Each Model

Use Haiku for:

• High-volume classification tasks (routing support tickets, categorising leads)
• Short, structured data extraction (pulling specific fields from a form or message)
• Simple yes/no or low/medium/high decisions with clear criteria
• Summarising short texts (under 500 words)
• Filtering and preprocessing (identifying whether an email needs processing at all)
• Any task where you've tested that Haiku produces acceptable quality

Use Sonnet for:

• Most business automation tasks — the default choice when Haiku doesn't quite get there
• Long-form writing (proposals, reports, blog posts, documentation)
• Complex analysis requiring multi-step reasoning
• Customer-facing responses where quality matters
• Document analysis (contracts, reports, emails of moderate length)
• Code generation for non-trivial implementations
• Summarising long documents (500-10,000 words)

Use Opus for:

• The most complex analytical tasks requiring deep expertise
• Advanced research synthesis across multiple complex sources
• Legal document analysis requiring nuanced interpretation
• Complex code architecture and review
• Tasks where errors are costly and the quality bar is very high
• Long, complex documents requiring full comprehension

Performance vs. Cost Trade-offs

Approximate pricing (check anthropic.com for current rates):

A typical 1,000-token automation task (500 input, 500 output):

• Haiku: ~$0.00075 (~$7.50 per 10,000 tasks)
• Sonnet: ~$0.009 (~$90 per 10,000 tasks)
• Opus: ~$0.045 (~$450 per 10,000 tasks)

At Haiku pricing, processing 100,000 support ticket classifications per month costs ~$75. The same volume on Sonnet costs ~$900. On Opus, ~$4,500. The right model selection can be a 10-100x cost difference.

Testing Your Model Selection

The correct approach to model selection is empirical, not theoretical. The process:

1. Define your quality threshold (what accuracy/quality level is "good enough")
2. Create a test set of 50-100 representative real-world inputs
3. Run the test set on Haiku first
4. Measure quality against your threshold
5. If Haiku meets the threshold, use Haiku
6. If not, test Sonnet
7. Only use Opus if Sonnet doesn't meet the threshold

In practice: Haiku handles perhaps 40% of real business automation tasks acceptably. Sonnet handles perhaps 55%. Opus is needed for perhaps 5%.

Many teams default to Sonnet without testing Haiku. This costs 12x more than necessary for the tasks that Haiku can handle.

Mixed Model Architecture

For automation workflows with multiple AI steps, using different models for different steps is standard practice.

Example: customer support ticket processing

• Step 1 (Haiku): Classify the ticket type (billing, technical, feature, other) — simple classification
• Step 2 (Haiku): Determine urgency (high/medium/low) — simple classification
• Step 3 (Sonnet): Generate the draft response — complex generation requiring quality
• Step 4 (Haiku, optional): Final quality check — simple format verification

This architecture uses the expensive model only where it's needed. The classification steps (90% of API calls in a support workflow) use Haiku. The generation step (10% of calls) uses Sonnet. Total cost: close to Haiku pricing on a per-ticket basis.

Recommended Tools

• Anthropic API — Access to all Claude model tiers
• Claude.ai — Test prompts on different model tiers before API deployment
• n8n — Configure different models for different workflow steps
• Make.com — HTTP module allows specifying model per automation step

“Choose the cheapest model that produces acceptable quality. Everything else is waste.”