
Claude Haiku 5.5
Introducing Claude Haiku 5.5: the cheapest, fastest, and most capable small model we’ve ever released.
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Introducing Claude Haiku 5.5: the cheapest, fastest, and most capable small model we’ve ever released.
Claude Haiku 5.5 is designed for high-volume, cost-sensitive tasks. It reliably handles quick and repetitive workloads (like summaries, compactions, database queries, and classification requests). It pairs well with Opus 5.5 and Sonnet 5.5 as a subagent on coding work. And, since it’s also our fastest model to date, it works especially well for speed-sensitive tasks like live customer support and browser use.¹
Haiku 5.5 is available at a much lower price than Haiku 4.5. On average, it now costs around 75% less to run.²
Here’s how Claude Haiku 5.5 performs across a range of benchmarks:
For details on how we run our evaluations, see the Haiku 5.5 System Card.
Haiku 5.5 is our first Haiku-class model to come with an adjustable effort setting. This means that, as with our other models, users can decide whether to optimize for cost or intelligence. The charts below show how Haiku 5.5 performs on three benchmarks at each effort setting:
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OSWorld 2.1 measures how well agents can operate a real computer to finish long, multi-step tasks.
Elo, as reported0.0050.010.020.050.100.200.50125Cost per task (USD, log scale)LowMedHighXhighMax
Artificial Analysis’s GDPval-AA v2.1 evaluates agents on real-world professional work across 44 occupations.
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Humanity’s Last Exam (HLE) is a test of expert-level academic knowledge and reasoning.
In early testing, our customers reported results consistent with the performance and cost improvements shown above. Here’s what they told us about the new model:
“We’re very impressed with Claude Haiku 5.5, particularly its speed. We ran it through our eval suite for AI Teammates, our AI agent product, covering use cases like triaging bugs, setting up projects, and searching large portfolios to surface high-risk or overdue work. Compared with the model we use today, we saw over a 30% reduction in latency for task completions and up to 2.5x faster inference per agent turn. It’s a noticeably snappier experience.”
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