When to Use Claude Haiku vs Sonnet: A Practical Guide

Mar 19, 2026

Problem

I was building an AI pipeline and kept burning through my API budget. I’d default to Sonnet for everything—validation, classification, content generation. When I tried switching to Haiku to save money, I got poor results on complex tasks.

The real issue? I was thinking about model selection as a cost slider. Haiku isn’t just “cheaper Sonnet”—it’s a different tool for different jobs.

What happened?

I built a content processing pipeline that handled 10,000 documents per day. My original setup used Sonnet for everything:

# My original (expensive) setup
async def process_document(doc):
    # Sonnet classified the document
    category = await sonnet.generate(f"Classify: {doc.content}")

    # Sonnet extracted entities
    entities = await sonnet.generate(f"Extract entities from: {doc.content}")

    # Sonnet summarized
    summary = await sonnet.generate(f"Summarize: {doc.content}")

    return {"category": category, "entities": entities, "summary": summary}

This cost me about $300/day. So I switched everything to Haiku:

# My failed cost-cutting attempt
async def process_document(doc):
    category = await haiku.generate(f"Classify: {doc.content}")
    entities = await haiku.generate(f"Extract entities from: {doc.content}")
    summary = await haiku.generate(f"Summarize: {doc.content}")
    return {"category": category, "entities": entities, "summary": summary}

The results were inconsistent. Classification worked fine, but summaries were shallow and missed nuance. Entity extraction had more errors.

How to solve it?

I realized I needed a three-tier approach:

┌─────────────────────────────────────────────────────────┐
│                    OPUS (Orchestrator)                   │
│         Complex reasoning, architectural decisions      │
└─────────────────────────┬───────────────────────────────┘
                          │
          ┌───────────────┴───────────────┐
          ▼                               ▼
┌─────────────────────┐       ┌─────────────────────┐
│   SONNET (Executor) │       │   SONNET (Executor) │
│   Complex execution │       │   Content creation  │
└─────────┬───────────┘       └─────────────────────┘
          │
          ▼
┌─────────────────────┐
│    HAIKU (Worker)   │
│  Narrow, high-volume│
│  validation, class. │
└─────────────────────┘

Here’s my corrected approach:

from enum import Enum
from dataclasses import dataclass

class TaskComplexity(Enum):
    NARROW = "narrow"      # Haiku territory
    MODERATE = "moderate"  # Sonnet territory
    COMPLEX = "complex"    # Opus territory

@dataclass
class TaskProfile:
    complexity: TaskComplexity
    volume: int
    requires_reasoning: bool

class ModelRouter:
    def route(self, profile: TaskProfile):
        # High-volume + narrow + no deep reasoning = Haiku
        if (profile.volume > 10 and
            profile.complexity == TaskComplexity.NARROW and
            not profile.requires_reasoning):
            return self.haiku

        # Requires reasoning or complex = Opus
        if profile.requires_reasoning or profile.complexity == TaskComplexity.COMPLEX:
            return self.opus

        # Default to Sonnet
        return self.sonnet

Now my document pipeline routes intelligently:

async def process_document(doc):
    # Haiku: classification is narrow, high-volume
    category = await haiku.generate(
        f"Classify into [tech, business, other]: {doc.title}"
    )

    # Sonnet: extraction needs some context understanding
    entities = await sonnet.generate(
        f"Extract people, places, organizations from: {doc.content}"
    )

    # Sonnet: summarization requires nuance
    summary = await sonnet.generate(
        f"Summarize in 3 sentences: {doc.content}"
    )

    return {"category": category, "entities": entities, "summary": summary}

Cost dropped to $90/day while maintaining quality.

The reason

The key insight from practitioners: Haiku excels as a specialized sub-agent, not as a cheaper Sonnet alternative.

When I match model to task:

Task	Model	Why
Classify 10K titles	Haiku	Pattern matching, explicit output
Debug race condition	Sonnet	Needs reasoning about timing
Design architecture	Opus	Requires deep system understanding
Validate JSON schema	Haiku	Binary decision, high volume
Write documentation	Sonnet	Needs context, nuance

Haiku requires explicit instructions. It won’t infer intent the way Sonnet can. But for narrow, well-defined tasks with clear output schemas, Haiku often performs just as well at 10-20x lower cost.

Summary

In this post, I showed how to choose between Claude models based on task characteristics. The key point is: Haiku for narrow high-volume tasks, Sonnet for balanced execution, Opus for deep reasoning. Match the model to the task, not just your budget.

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!