When should I use Haiku instead of Sonnet or Opus?

Haiku is a good fit when speed and cost matter most and the task is relatively simple, such as classification, short summarization, or routine chat responses.

Is Claude Haiku less accurate than Opus?

Generally yes on complex reasoning tasks, though newer Haiku generations have narrowed the gap with older, larger Claude models on many everyday tasks.

Can Haiku be used inside a multi-model system?

Yes, it is commonly used to handle simple sub-tasks delegated by a more capable orchestrating model like Sonnet or Opus, reducing overall cost.

AI Models

Claude Haiku

Q: What is Claude Haiku?

Claude Haiku is the fastest, most cost-efficient model in Anthropic's Claude family, designed for high-volume, low-latency tasks.

By Anthropic

BeginnerModel7.3K learners

Claude Haiku is the fastest and most cost-efficient model in Anthropic's Claude family, designed for high-volume, low-latency tasks where speed matters more than maximum reasoning depth.

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Definition

Claude Haiku is the fastest and most cost-efficient model in Anthropic's Claude family, designed for high-volume, low-latency tasks where speed matters more than maximum reasoning depth.

Overview

Claude Haiku is the smallest of the three tiers in Anthropic's Claude lineup, sitting below Sonnet and Opus. It is optimized for near-instant responses and low per-token cost, making it well suited to applications that process large volumes of requests, such as customer support triage, content moderation, and simple data extraction, where a smaller model's accuracy is sufficient. Despite its smaller size, successive Haiku releases have improved meaningfully in capability, with newer Haiku models approaching the performance of older, larger Claude models on many everyday tasks while remaining dramatically cheaper and faster. This 'capability migrating downward' pattern is common across the AI industry as training techniques and model distillation improve efficiency at every size tier. Haiku is often used as the routing target for simple sub-tasks within larger AI agent systems, where a Sonnet- or Opus-powered orchestrator delegates straightforward steps to Haiku to reduce overall cost and latency, and it is available through Claude.ai, the Anthropic API, and major cloud platforms.

Key Features

Smallest, fastest, and lowest-cost model in the Claude family
Optimized for high-volume, latency-sensitive applications
Improves each generation, narrowing the gap with older larger models
Commonly used for simple sub-tasks within multi-model agent pipelines
Built with Anthropic's Constitutional AI alignment approach
Available via Claude.ai, the Anthropic API, and cloud marketplaces

Use Cases

Customer support chat triage and FAQ answering

Content moderation and classification at scale

Simple data extraction and structured formatting

High-volume summarization of short documents

Low-latency sub-tasks within larger agent orchestration pipelines

Frequently Asked Questions

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