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Model details

Claude Haiku 5.5 (Anthropic)

Claude Haiku 5.5 is Anthropic's small-tier model designed for high-volume, latency-sensitive workloads such as classification, routing, extraction, and subagent orchestration, where speed and cost efficiency matter more than maximum reasoning depth. The official platform overview positions it as the fastest option in the current Claude lineup, with adaptive thinking controllable through the effort parameter at a default medium setting, allowing developers to trade deliberation for responsiveness depending on the task. It pairs this throughput focus with a very large context window, giving it enough room to ingest lengthy documents, multi-turn conversations, or collections of inputs before responding.

In practical terms, Claude Haiku 5.5 is well suited as a workhorse layer underneath larger models, handling the bulk routing, filtering, and summarization work in agentic pipelines while more capable tiers handle deeper reasoning. Its thinking blocks remain scoped to the account or linked accounts that produced them, which matters for organizations running multi-tenant deployments that need predictable reasoning continuity. The model also adopts the same newer tokenizer introduced with Claude 4.7 and later, a change that shifts how text converts into tokens and that developers should account for when porting prompts or estimating usage. Together these traits make it a flexible, budget-friendly choice for production systems that need reliable text and document understanding at scale.

LLM Gatewayanthropic/claude-haiku-5-5claude-haiku

Quick Info

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Provider
LLM Gateway
Model key
anthropic/claude-haiku-5-5
Release date
Oct 7, 2026
Last updated
Oct 7, 2026
Knowledge cutoff
2026-06
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.10
Output token cost
$0.50

Limits

Output tokens
128,000 tokens
Context window
1,000,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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