
What happened
Anthropic released Claude Haiku 5.5, a small model priced about 75 percent below Haiku 4.5 and scoring 1,620 on GDPval-AA v2.1 versus 735 for its predecessor.
Why it matters
Anthropic says Haiku 5.5 brings a major performance jump at about 75 percent less cost, which the company frames as making high-volume, cost-sensitive tasks cheaper to run.
What to watch
Real-world savings may be smaller because Anthropic says Haiku 5.5's updated tokenizer consumes slightly more tokens per task; the model is available now across all platforms, including Amazon Web Services, Google Cloud, and Microsoft Azure.
WHO IT HITSTeams running high-volume, cost-sensitive AI workloads — summarization, database queries, classification, and live customer support — gain a cheaper option that Anthropic says costs about 75 percent less than Haiku 4.5. For developers weighing computer-use and agentic coding tasks, the tokenizer caveat may narrow the effective savings.
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Anthropic positions Haiku 5.5 as its fastest and most affordable small model to date, aimed at high-volume, cost-sensitive work such as summarization, database queries, classification, and live customer support. The company says the average cost is about 75 percent below Haiku 4.5, and for prompts up to 100,000 tokens — which Anthropic says cover roughly 90 percent of all previous Haiku requests — prices drop by up to 90 percent.
The benchmark picture is where the release gets its edge. Haiku 5.5 scores 1,620 on GDPval-AA v2.1, more than double Haiku 4.5's 735, and it jumps from zero to 39.2% on Terminal-Bench 4.0. Computer use shows the largest gain, reaching 72.4% on OSWorld-2.1 versus 15.7% — a fit, the company notes, because computer use burns through large amounts of tokens. Haiku 5.5 is also the first Haiku-class model with adjustable reasoning levels, letting users trade cost against quality, though Anthropic still points complex agentic coding toward its larger models.
One caveat cuts against the headline numbers: Anthropic says the updated tokenizer consumes slightly more tokens per task, as happened with the Opus 4.x models where token usage jumped about 30 percent from the tokenizer change alone. Real-world savings therefore are likely to be smaller than per-token prices suggest. How much of the promised cost cut survives that effect — for teams running summarization or support agents at scale — is the practical test.
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