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Anthropic

Claude 3 Haiku

Claude 3 Haiku handles enterprise document workloads at a fraction of Opus-tier cost, serving as the speed-and-affordability anchor of the Claude 3 family.

Tool UseVision (Image)Explicit Caching
index.ts
import { streamText } from 'ai'


const result = streamText({
  model: 'anthropic/claude-3-haiku',
  prompt: 'Why is the sky blue?'
})
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Frequently Asked Questions

  • How fast is Claude 3 Haiku compared to other Claude 3 models?

    Anthropic described Claude 3 Haiku as substantially faster than peer models in its performance tier at launch. See live throughput on this page for current numbers.

  • What happens to Claude 3 Haiku's speed on long prompts?

    Prompts exceeding 32K tokens reduce throughput meaningfully. Factor this slowdown into latency estimates if your workload regularly involves long-context inputs.

  • Can Claude 3 Haiku process images?

    Yes. Haiku shares the same vision architecture as Sonnet and Opus. It processes photos, charts, graphs, and technical diagrams. Anthropic highlighted enterprise document analysis and large-scale image annotation as primary vision use cases.

  • How does Claude 3 Haiku differ from Claude 3.5 Haiku?

    Claude 3.5 Haiku matched Claude 3 Opus on many intelligence benchmarks while maintaining Haiku-class speed. The original Claude 3 Haiku is faster on a per-token basis but operates at a lower capability tier. Choose based on whether throughput or reasoning depth matters more.

  • What is the cost of processing images with Claude 3 Haiku?

    Exact per-image cost depends on image resolution and token count. Check the pricing panel on this page for current rates.

  • How do I authenticate requests to Claude 3 Haiku through AI Gateway?

    Configure your Anthropic API key in your AI Gateway project settings. AI Gateway routes requests to bedrock, vertex and handles authentication, retries, and failover. Use the identifier anthropic/claude-3-haiku in your API calls.

  • Is Claude 3 Haiku suitable for agentic workflows?

    Haiku handles structured tool calling and simple multi-step tasks. For complex agentic workflows that require deep planning, later Sonnet or Haiku generations offer stronger instruction following and tool use accuracy.