Anthropic's Message Batches API provides a 50% discount on all Claude model pricing for requests that do not require an immediate response, with results delivered within 24 hours. It works the same way as OpenAI's Batch API: you submit a batch of requests, Anthropic processes them asynchronously, and you pay half price. Claude is particularly valuable for batch processing long documents and complex reasoning tasks where its instruction following and document comprehension capabilities genuinely pull ahead of cheaper alternatives.
When Claude Batch Processing Is Worth It
Claude's advantages over GPT-4o-mini are most pronounced on tasks requiring careful instruction following over long contexts and nuanced analysis. These are also the highest-value batch processing use cases.
Long document processing. Claude consistently performs better on tasks requiring careful reading of long documents (10,000-200,000 tokens). Legal document review, financial report summarization, academic paper analysis — these tasks see more quality improvement from using Claude over a cheaper model than short-text tasks do. The batch API makes Claude's superior long-context capability affordable at scale.
Complex reasoning with specific output formats. Claude models reliably follow complex output format specifications (nested JSON, structured reports with exact field requirements). For batch jobs where you need machine-parseable outputs, Claude's adherence to format instructions reduces post-processing failure rates.
Multi-step instruction chains. Tasks with 5+ instructions in a system prompt ("first extract X, then compare with Y, then classify as Z, then write a summary that...") are handled more reliably by Claude than smaller models. At batch API pricing, this capability is available at roughly the same cost per task as GPT-4o standard pricing.
How to Use the Anthropic Message Batches API
The API is available in the official Anthropic Python and TypeScript SDKs.
import anthropic
client = anthropic.Anthropic()
# Create a batch with multiple requests
batch = client.messages.batches.create(
requests=[
{
"custom_id": "doc-001",
"params": {
"model": "claude-3-5-haiku-20241022",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Summarize the key financial metrics from this quarterly report: [document text]"
}
]
}
},
{
"custom_id": "doc-002",
"params": {
"model": "claude-3-5-haiku-20241022",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Extract all named entities (people, organizations, locations) from: [document text]"
}
]
}
}
]
)
print(f"Batch created: {batch.id}")
print(f"Processing status: {batch.processing_status}")
