Claude Haiku

Claude Haiku is the fastest and lowest-cost model in the Claude family. It exists to address a very practical problem: not every task needs the "smartest AI" — many tasks just need "good enough, fast, and cheap." Imagine an e-commerce platform with tens of thousands of user reviews to auto-classify as positive/negative/neutral every day. Using Opus or Sonnet would work technically, but costs would be high and speed might become a bottleneck. Haiku performs nearly as well as Sonnet on this kind of task, but might cost 1/10 to 1/20 as much. That's Haiku's purpose — not making you sacrifice quality, but avoiding payment for unused capability on tasks that don't require that much intelligence. Naming-wise, Haiku (俳句) is an extremely compact Japanese poetry form that achieves much with little — Anthropic used this name to signal the model's design philosophy: precise, lightweight, nothing wasted.

Where are Haiku's capability limits? This is the most critical question for using Haiku well. Simply put: Haiku is strong on "pattern recognition and mapping" tasks; it has clear limitations on "reasoning and generation" tasks. What Haiku is good at: translating text to another language (pattern mapping), determining the sentiment of a review (classification), filling structured templates with corresponding data (format conversion), answering standardized FAQ questions from keywords (quick lookup). What Haiku struggles with: analysis tasks requiring integration of multiple information sources and complex judgment; planning tasks needing consideration of multiple constraints and balancing different factors; deep writing tasks requiring long-form logical consistency; contextual understanding tasks requiring interpretation of implied meaning or sarcasm. A useful test: if you can describe "what input should produce what output" with a simple rule or decision tree, Haiku can usually handle it. If the description gets complex, you need Sonnet or above.

Haiku's impact for general users: if you primarily use the Claude.ai web interface, Haiku usually isn't something you'd actively choose — it's more relevant for API developers. But understanding its existence explains why many AI applications can be "cheap and fast." Impact for API developers: Haiku is the infrastructure for high-frequency, low-cost scenarios. Customer service bots (thousands of conversations daily), real-time translation (dynamic web content translation), content moderation (rapid policy violation detection), smart autocomplete (real-time suggestions as you type) — these scenarios are technically feasible with Sonnet but economically unviable; Haiku makes them commercially sustainable products. A concrete cost comparison: suppose your application processes 100,000 short text classification tasks daily (averaging 100 tokens input + 10 tokens output per task). With Sonnet: roughly tens of dollars per day. With Haiku: possibly just a few dollars — more than 10× difference.

A Haiku usage decision framework for API developers: Step 1: Confirm task type. Is this task "pattern recognition/mapping/classification" or "reasoning/generation/analysis"? Try Haiku for the former; go directly to Sonnet for the latter. Step 2: Run an A/B test. With the same Prompt, run Haiku and Sonnet each on 20-30 representative tasks and compare output quality. If Haiku's quality is within your acceptable range, use Haiku. If the gap is significant, use Sonnet. Step 3: Consider a hybrid architecture where "Haiku handles rough filtering, Sonnet handles deep processing." Example: use Haiku to filter out clearly irrelevant requests first, then route the remainder needing deep processing to Sonnet. This architecture often reduces overall costs by 50-70% in many scenarios while maintaining final output quality.