How to Cut AI Costs 50% with Claude Haiku Batch Processing

Mar 19, 2026

Problem

I was burning through my API budget on tasks that didn’t require expensive models. Classifying 10,000 titles with Sonnet cost me $30. Text extraction on a million documents was hundreds of dollars. I needed to cut costs without sacrificing quality.

What I Discovered

The combination of Claude Haiku with the Batch API delivers massive savings:

Haiku costs ~$0.80 per million input tokens (vs ~$3 for Sonnet)
Batch API offers 50% discount for 24-hour turnaround
Together: ~10-20x cheaper than real-time Sonnet

The Cost Difference

| Scenario | Tokens | Model | Cost |
|----------|--------|-------|------|
| 10K classifications, Opus | 10M | Opus | ~$150 |
| 10K classifications, batched Haiku | 10M | Haiku Batch | ~$0.008 |
| 1M text extractions, Sonnet | 100M | Sonnet | ~$300 |
| 1M text extractions, Haiku Batch | 100M | Haiku Batch | ~$12.50 |

How to Use Batch Processing

Basic Batch Request

import anthropic

client = anthropic.Anthropic()

def classify_titles_batch(titles: list[str]) -> list[str]:
    """Classify titles with Haiku batch processing."""

    # Create batch requests
    requests = [
        {
            "custom_id": f"title-{i}",
            "params": {
                "model": "claude-3-5-haiku-20241022",
                "max_tokens": 50,
                "messages": [
                    {"role": "user", "content": f"Classify as 'tech' or 'business': {title}"}
                ]
            }
        }
        for i, title in enumerate(titles)
    ]

    # Submit batch
    batch = client.messages.create_batch(requests=requests)

    print(f"Batch ID: {batch.id}")
    print(f"Status: {batch.processing_status}")

    return batch.id

Polling for Results

import time

def get_batch_results(batch_id: str) -> list[dict]:
    """Poll for batch completion and retrieve results."""

    while True:
        batch = client.messages.retrieve_batch(id=batch_id)

        if batch.processing_status == "ended":
            break

        print(f"Processing... {batch.processing_status}")
        time.sleep(60)  # Check every minute

    # Retrieve results
    results = []
    for result in client.messages.list_batch_results(batch_id=batch_id):
        results.append({
            "custom_id": result.custom_id,
            "output": result.result.content[0].text
        })

    return results

Tiered Processing Pipeline

Use Haiku as a first pass, escalate uncertain cases to Sonnet:

def classify_with_fallback(titles: list[str]) -> list[dict]:
    """Classify with Haiku, escalate uncertain cases to Sonnet."""

    # First pass: Haiku batch classification
    haiku_results = batch_classify(titles, model="claude-3-5-haiku-20241022")

    # Identify low-confidence results
    uncertain = [
        (i, titles[i])
        for i, result in enumerate(haiku_results)
        if result.get("confidence", 0) < 0.8
    ]

    # Escalate uncertain cases to Sonnet
    if uncertain:
        indices, uncertain_titles = zip(*uncertain)
        sonnet_results = batch_classify(
            list(uncertain_titles),
            model="claude-sonnet-3-5"
        )

        # Merge results
        for i, result in zip(indices, sonnet_results):
            haiku_results[i] = result

    return haiku_results

Token-Efficient Prompting

Cut token usage with structured prompts:

# BAD: Verbose prompt wastes tokens
inefficient_prompt = """
Please carefully analyze the following text and provide a comprehensive
classification. Consider all possible categories and select the most
appropriate one. Think step by step about your reasoning.

Text: {text}

Classification:
"""

# GOOD: Minimal prompt with structured output
efficient_prompt = """
Classify: {text}
Output JSON: {"category": str, "confidence": float}
"""

Complete Batch Processor

from dataclasses import dataclass
from typing import Iterator
import anthropic

@dataclass
class BatchResult:
    custom_id: str
    result: dict
    error: str | None = None

class HaikuBatchProcessor:
    """Efficient batch processing with Haiku for large workloads."""

    def __init__(self, batch_size: int = 1000):
        self.client = anthropic.Anthropic()
        self.batch_size = batch_size

    def process(
        self,
        items: list[str],
        prompt_template: str,
        max_tokens: int = 100
    ) -> Iterator[BatchResult]:
        """Process items in batches, yielding results as they complete."""

        for i in range(0, len(items), self.batch_size):
            batch = items[i:i + self.batch_size]

            requests = [
                {
                    "custom_id": f"item-{i + j}",
                    "params": {
                        "model": "claude-3-5-haiku-20241022",
                        "max_tokens": max_tokens,
                        "messages": [
                            {"role": "user",
                             "content": prompt_template.format(item=item)}
                        ]
                    }
                }
                for j, item in enumerate(batch)
            ]

            # Submit batch
            batch_job = self.client.messages.create_batch(requests=requests)

            # Poll for completion
            while batch_job.processing_status != "ended":
                time.sleep(60)
                batch_job = self.client.messages.retrieve_batch(id=batch_job.id)

            # Yield results
            for result in batch_job.results:
                yield BatchResult(
                    custom_id=result.custom_id,
                    result=result.result,
                    error=result.error
                )

When to Use Batch Processing

Good for:

Overnight processing of daily aggregates
Weekly report generation
Bulk content classification
Historical data analysis
Non-time-sensitive transformations

Not good for:

Real-time user interactions
Time-sensitive requests
Small batches (<100 requests)

Common Mistakes

Mistake 1: Batching Tiny Requests

# WRONG: Batch overhead isn't worth it
batch_classify([title1, title2, title3])  # 3 items

# RIGHT: Batch when you have 100+ items
batch_classify(titles)  # 1000+ items

Mistake 2: Wrong Model Selection

# WRONG: Haiku for complex reasoning
haiku_batch.generate("Analyze market trends and predict Q4 performance")

# RIGHT: Haiku for narrow tasks
haiku_batch.classify(articles, labels=["tech", "business", "other"])

Mistake 3: No Fallback Strategy

# WRONG: No handling of low-confidence results
results = haiku_batch.classify(titles)

# RIGHT: Escalate uncertain cases
results = classify_with_fallback(titles)

Summary

In this post, I showed how to cut AI costs by combining Claude Haiku with batch processing. The key point is: use Haiku for classification, extraction, and filtering tasks where 24-hour turnaround is acceptable. The 50% batch discount plus Haiku’s lower base cost delivers 10-20x savings.

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 Pricing
👨‍💻 Anthropic Batch Processing Documentation
👨‍💻 Reddit Discussion: Haiku cost optimization

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