Can Claude Haiku Write Code? Using the Lightweight Model for Development Tasks

Mar 18, 2026

Problem

When I started using AI coding assistants, I kept running into the same issue: Opus is expensive for routine tasks. I was burning through tokens on simple code generation, documentation updates, and log analysis.

I needed a way to reduce costs without sacrificing code quality. The question was: can Claude Haiku handle real development work?

Environment

Claude Opus 4.5 (planning)
Claude Haiku 4.5 (execution)
Python/TypeScript codebases
Multi-agent development workflows

What Happened?

I was using Opus for everything: code reviews, bug fixes, feature implementation, even simple refactoring tasks. My monthly API costs were climbing, and I noticed that many tasks didn’t actually require Opus-level reasoning.

Here’s what my workflow looked like:

┌─────────────────────────────────────────┐
│         Opus for Everything              │
├─────────────────────────────────────────┤
│  - Architecture decisions  ✅            │
│  - Code generation         ✅ (overkill) │
│  - Documentation           ✅ (overkill) │
│  - Log analysis            ✅ (overkill) │
│  - Simple refactoring      ✅ (overkill) │
│                                          │
│  Cost: High for routine tasks            │
└─────────────────────────────────────────┘

The Reddit community confirmed I wasn’t alone:

“When you research your code, you’re using Haiku with subagents and not Opus. If Opus was doing that you would be shredding your tokens like taco chicken.”

Another developer shared:

“With proper spec and planning, Haiku is an excellent coding model. I’m building a rather complex code transformation tool in Rust and using Opus to spec and plan, Haiku has been crushing the coding.”

How to Solve It?

I implemented a two-tier architecture: Opus plans, Haiku executes.

┌─────────────────────────────────────────────────────────────┐
│                    Development Workflow                      │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│   ┌──────────────┐         ┌──────────────────────────┐    │
│   │   Claude     │         │     Claude Haiku          │    │
│   │   Opus       │────────▶│     (Execution Layer)     │    │
│   │   (Planner)  │         │                            │    │
│   └──────────────┘         │  - Code generation         │    │
│                            │  - Refactoring              │    │
│   Tasks:                   │  - Documentation            │    │
│   - Architecture           │  - Log analysis            │    │
│   - System design          │  - Test writing             │    │
│   - Complex reasoning      │  - Code exploration         │    │
│   - Edge case handling     │  - Data formatting          │    │
│                            └──────────────────────────┘    │
│                                                              │
└─────────────────────────────────────────────────────────────┘

Step 1: Create a Plan with Opus

First, I use Opus to create a detailed implementation plan:

from anthropic import Anthropic

client = Anthropic()

def create_plan(requirements: str, codebase: str) -> dict:
    """Opus handles planning with full context"""
    response = client.messages.create(
        model="claude-opus-4-5-20250514",
        max_tokens=2048,
        messages=[{
            "role": "user",
            "content": f"""
Requirements: {requirements}

Current codebase structure:
{codebase}

Create a detailed implementation plan with:
1. Files to modify (specific paths)
2. Functions to add/modify (with signatures)
3. Edge cases to handle
4. Test cases needed
"""
        }]
    )
    return parse_plan(response)

Step 2: Execute Tasks with Haiku

Then Haiku executes each task from the plan:

def execute_task(task: dict) -> str:
    """Haiku executes well-defined tasks"""
    response = client.messages.create(
        model="claude-haiku-4-5-20250915",
        max_tokens=4096,
        messages=[{
            "role": "user",
            "content": f"""
Task: {task['description']}
File: {task['file_path']}
Requirements: {task['requirements']}

Implement only what is specified. Do not modify other code.
"""
        }]
    )
    return response.content[0].text

Step 3: Review with Opus

Finally, Opus reviews and integrates the results:

def review_and_integrate(results: list[str]) -> str:
    """Opus reviews Haiku's output"""
    response = client.messages.create(
        model="claude-opus-4-5-20250514",
        max_tokens=1024,
        messages=[{
            "role": "user",
            "content": f"""
Review these implementation results for:
1. Correctness
2. Edge cases
3. Integration issues

Results: {results}
"""
        }]
    )
    return response.content[0].text

Now my workflow is:

Simple task (format, docs)      → Haiku directly
Complex feature                 → Opus plans → Haiku executes
Critical review                 → Opus reviews Haiku output

The Reason

I think the key insight is that different tasks require different levels of reasoning:

Task Type	Model	Why
Architecture decisions	Opus	Requires deep reasoning, edge case handling
Code generation (with specs)	Haiku	Clear requirements, routine execution
Documentation	Haiku	Straightforward writing, no complex decisions
Bug investigation	Opus → Haiku	Opus diagnoses, Haiku fixes
Log analysis	Haiku	Pattern matching, summarization
Code review	Opus	Requires understanding subtle implications

The cost difference is substantial. Haiku is roughly 10x cheaper than Opus for the same token volume. Running continuous code analysis, automated refactoring, and parallel development tasks no longer breaks the budget.

Common Mistakes

I made several mistakes when starting with Haiku:

1. Using Haiku without clear specifications

# WRONG: Vague request
"Fix the bug in auth.ts"

# CORRECT: Opus-generated spec
"In auth.ts, replace the hardcoded JWT secret with an environment variable.
Add validation to throw an error if JWT_SECRET is not set.
Update the import to include 'dotenv/config' at line 1."

Haiku needs clear direction. Vague prompts lead to poor output.

2. Using Opus for everything

Reserve Opus for complex decisions, not routine tasks. I was wasting tokens on simple formatting and documentation.

3. Not validating Haiku output

Always have Opus review critical code changes. Haiku is good, but not infallible.

Parallel Code Exploration

One pattern that works well: running multiple Haiku agents in parallel for code exploration:

import asyncio
from anthropic import AsyncAnthropic

client = AsyncAnthropic()

async def explore_file(file_path: str, query: str) -> dict:
    """Single Haiku agent explores one file"""
    with open(file_path) as f:
        content = f.read()

    response = await client.messages.create(
        model="claude-haiku-4-5-20250915",
        max_tokens=1024,
        messages=[{
            "role": "user",
            "content": f"Analyze this file for: {query}\n\n{content}"
        }]
    )
    return {"file": file_path, "findings": response.content[0].text}

async def explore_codebase(files: list[str], query: str) -> list[dict]:
    """Launch parallel Haiku agents"""
    tasks = [explore_file(f, query) for f in files]
    results = await asyncio.gather(*tasks)
    return results

# Usage: Explore 10 files in parallel
files = ["src/auth.py", "src/api.py", "src/models.py", ...]
findings = asyncio.run(explore_codebase(files, "security vulnerabilities"))

This pattern lets you analyze an entire codebase quickly and cheaply.

Summary

In this post, I showed how to use Claude Haiku effectively for coding tasks. The key point is using a two-tier architecture where Opus plans and Haiku executes.

The benefits:

70-90% cost reduction for routine coding tasks
Faster iteration cycles with Haiku’s quick responses
Scalable parallel processing without budget concerns
Opus-quality output when you need it

The future of AI-assisted development isn’t about choosing between models—it’s about orchestrating them effectively. Let Opus think, let Haiku do.

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:

👨‍💻 Anthropic Claude Models
👨‍💻 Reddit Discussion: Claude Haiku Use Cases

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