Anthropic released Claude Haiku 5.5, its cheapest and fastest small model yet, cutting API prices up to 90% for typical requests, matching OpenAI's GPT-6 Luna on base pricing, and adding Haiku's first adjustable effort dial - all roughly two weeks after Claude Opus 5.5 completed the Claude 5.5 family.

Anthropic is advertising Claude Haiku 5.5 as costing $0.10 per million input tokens and $0.50 per million output tokens, a rate that holds only for prompts under 100,000 tokens ^[1]. Several outlets covering the launch flagged that this threshold functions as a pricing cliff: cross it, and the rate more than quintuples, to $0.50 per million input tokens and $2.50 per million output tokens ^[1][2].

That design explains the gap between Anthropic's two headline savings figures. The company says Haiku 5.5 costs about 75% less to run than Haiku 4.5 on average, but close to 90% less specifically for requests that stay under the 100,000-token line - which Anthropic estimates covers roughly 90% of actual Haiku traffic ^[3]. Developers migrating long-context workloads from Haiku 4.5 are the ones most likely to see the smaller end of that discount rather than the splashier number in Anthropic's own announcement.

Haiku 5.5 ships with Anthropic's first Haiku-class "effort" setting, letting developers choose among Low, Medium, High, Xhigh, and Max levels to trade inference cost against answer quality ^[4]. It is a new control surface for a model line that has historically been single-speed, letting the same model ID serve both the cheapest, fastest classification jobs and harder reasoning work.

Community testing complicated that pitch. Discussion on Reddit pointed to Anthropic's own Terminal-Bench results, arguing Haiku's effort curve falls below the Pareto frontier once effort is turned up to Medium or higher - meaning that for anything but the shortest, cheapest tasks, Sonnet may deliver better results for the money. A second line of community pushback argued that price-per-token understates true cost because Haiku tends to consume more tokens per completed task than larger models, making price-per-completed-task the more honest comparison. Neither critique disputes that Haiku 5.5 is cheap at the low-effort end; they argue the dial does not hold its value as you turn it up.

Haiku 5.5's base pricing was set to match OpenAI's GPT-6 Luna exactly, and Anthropic is using benchmark comparisons against Luna - not just its own prior Haiku model - to make its case ^[3]. On OSWorld 2.1, Anthropic reports Haiku 5.5 at 72.4% versus 48.9% for GPT-6 Luna, and on Terminal-Bench 4.0, 39.2% versus 16.4% ^[1][3].

Coverage of the launch tied the pricing move to timing beyond the model itself: one outlet's headline situated the release "eight days before the October 15 deadline" ^[2], while other reporting framed it as arriving roughly a month ahead of Anthropic's planned November IPO ^[5]. That combination - frontier-lab economics intersecting a public-offering timeline - is part of why some coverage described the release less as a routine model refresh and more as a signal of margin compression at the bottom of the LLM stack, where Anthropic and OpenAI are now competing on cost per token as much as on capability.

Anthropic's own benchmark table shows large jumps over Haiku 4.5 - Humanity's Last Exam climbing from 10.2% to 45.9% without tools, GDPval-AA v2.1 more than doubling from 735 to 1620 ^[1]. Customer references echo that: a staff software engineer quoted by Anthropic reported over a 30% reduction in latency for task completions and up to 2.5x faster inference per agent turn versus the model it replaced, and HubSpot said Haiku 5.5 scored 92.8% on its internal benchmark suite, its best result yet ^[1].

One widely watched independent test framed the value proposition differently: a full suite of agentic coding tasks run against Haiku 5.5 consumed only about 1% of a creator's weekly Claude.ai usage allowance on the $200/month plan, versus roughly 10% for other models tested that same week - a gap the reviewer called more persuasive than any individual benchmark score. The same test placed Haiku 5.5's raw capability close to state-of-the-art from six months earlier, a meaningful jump after a long stretch with no new Haiku release, while still rating it behind Sonnet, Opus, and GPT-6 Astra on harder tasks.