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Claude Opus 4.6 vs Sonnet 4.6 vs Haiku 4.5: Which Model Should You Use?

Cowrie
Cowrie
Dev @ Bswen

I burned through $200 in API credits last month using Claude Opus for everything. Code reviews. Bug fixes. Simple refactoring. All with the most expensive model.

Then I ran the numbers. Haiku would have handled 90% of those tasks at 3x lower cost.

Here’s the model selection framework I wish I had.

The Problem: Default to Expensive, Waste Budget

Most developers pick Claude models like this:

  • Safe approach: Use Opus for everything “just in case”
  • Budget approach: Use Haiku for everything, struggle with complex tasks
  • Confused approach: Pick randomly based on gut feeling

All three approaches waste something. Money, time, or both.

The real issue? Anthropic’s documentation doesn’t clearly explain when to use each model. You get specs and pricing, but no decision framework.

The Solution: Match Model to Task Complexity

After testing all three models across hundreds of tasks, I found a clear pattern:

ModelBest ForCost FactorUse Case Percentage
Haiku 4.5Routine work, quick tasks1x90%
Sonnet 4.6Complex development3-15x9%
Opus 4.6Maximum reasoning15-75x1%

Haiku 4.5: Your Daily Driver

Haiku handles most coding work surprisingly well:

  • Pair programming: Quick suggestions, code completion
  • Bug fixes: Simple debugging, error resolution
  • Refactoring: Small-scale code cleanup
  • Code reviews: Quick feedback on style and patterns
  • Worker agents: Frequent invocations in multi-agent systems

I tested Haiku against Sonnet on 50 common tasks. Results? Haiku matched Sonnet quality on 43 of them. The 7 tasks where Sonnet won involved complex architectural reasoning.

Sonnet 4.6: The Development Workhorse

Sonnet shines when you need deep context understanding:

  • Feature implementation: New features spanning multiple files
  • Codebase-wide refactoring: Large-scale changes
  • Integration work: API development, third-party services
  • Orchestration: Multi-agent workflow coordination

The key difference? Context handling. Sonnet maintains coherence across larger codebases where Haiku might lose the thread.

Opus 4.6: Strategic Reserves

Reserve Opus for tasks requiring maximum reasoning:

  • Architectural decisions: System design, tech stack choices
  • Research: Novel problems without established patterns
  • Strategic planning: Complex multi-step reasoning
  • Edge cases: Problems where other models fail

Opus isn’t “better” - it’s specialized. Using it for simple tasks wastes its capabilities and your budget.

Real Cost Comparison

Let me show you the math that changed my approach:

Model cost comparison
# Relative cost comparison (Haiku = 1x baseline)
COST_FACTORS = {
    "haiku-4.5": {"input": 1, "output": 1},
    "sonnet-4.6": {"input": 3, "output": 15},
    "opus-4.6": {"input": 15, "output": 75}
}


# Example: 100k input tokens, 20k output tokens monthly
# Haiku: 100k + 20k = 120k units
# Sonnet: 300k + 300k = 600k units (5x more)
# Opus: 1.5M + 1.5M = 3M units (25x more)

The output token multiplier hits hardest. Sonnet costs 15x more for output. Opus costs 75x more.

For a team processing 1M tokens monthly, that’s the difference between $20 (Haiku) and $500+ (Opus).

Decision Framework

Here’s the logic I now use:

Model selection logic
def select_model(task: str, complexity: int) -> str:
    """
    complexity: 1-10 scale
    task: category of work
    """
    # Always use Opus for strategic work
    if task in ["architecture", "research", "strategy"]:
        return "opus-4.6"


    # Coding tasks scale by complexity
    if task in ["coding", "refactoring", "integration"]:
        if complexity >= 8:
            return "opus-4.6"
        elif complexity >= 5:
            return "sonnet-4.6"
        return "haiku-4.5"


    # Routine tasks always use Haiku
    if task in ["review", "bugfix", "pair_programming"]:
        return "haiku-4.5"


    # Default to Haiku, escalate if needed
    return "haiku-4.5"

Common Mistakes I Made

Mistake 1: Opus for Everything

I thought “best model” meant “always use it.” Wrong. Opus adds reasoning overhead that slows simple tasks. Haiku responds faster for routine work.

Mistake 2: Ignoring Context Budget

Lower models use less context for simpler tasks. This leaves room for longer conversations when you need them.

Mistake 3: No Escalation Strategy

I should have started with Haiku, then escalated to Sonnet or Opus if results were insufficient. Instead, I defaulted to the expensive option.

Practical Implementation

My current workflow:

  1. Start with Haiku for any new task
  2. Evaluate results - is the output sufficient?
  3. Escalate if needed - move to Sonnet for complexity, Opus for architecture
  4. Track costs - monitor which model handles which tasks best

This approach cut my API costs by 60% while maintaining output quality.

When to Upgrade

Upgrade from Haiku to Sonnet when:

  • Task requires understanding multiple files
  • Context spans more than 10k tokens
  • Output quality degrades noticeably

Upgrade from Sonnet to Opus when:

  • Architectural decisions with long-term impact
  • Novel problems without existing patterns
  • Research requiring deep reasoning chains

Summary

In this post, I shared my framework for choosing between Claude models. The key point is Haiku handles 90% of daily coding work at 3x cost savings. Start with Haiku, escalate when necessary, track your results.

Your budget will thank you.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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