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The small model just got a 75% price cut aimed exactly at the usage curve. claude haiku 5.5 costs…

Yethikrishna ROriginal on Threads

the small model just got a 75% price cut aimed exactly at the usage curve. claude haiku 5.5 costs $0.10 per million input tokens for prompts under 100k, the slice behind 90% of haiku 4.5's requests, with a 1m context window.

the marginal cost of an ai call just dropped below the network call that carries it.

Context

Anthropic's launch page says Claude Haiku 5.5 costs around 75% less to run than Haiku 4.5 on average. Its table lists input tokens at $0.10 per million for prompts up to 100k and $0.50 above 100k, against $1.00 for Haiku 4.5, and cache reads at $0.01 and $0.05. A footnote says it is priced 90% lower than Haiku 4.5 for requests up to 100,000 tokens and 50% lower above that, and that 90% of requests to Haiku 4.5 fell into the lower category.

The footnote says the 75% average accounts for an updated tokenizer that uses slightly more tokens per task. The page describes Haiku 5.5 as designed for high-volume, cost-sensitive tasks and as a subagent for coding work.

How it compares

The $0.10 per million input tokens for prompts under 100k and the 90% share of earlier requests match the page. The headline cut depends on which number is used: 90% on list price for short prompts, about 75% on average per the footnote after the tokenizer change. The note's '75% price cut' matches the average.

The 1M context window was not found in the pages read. That is unsupported, not refuted.

'The marginal cost of an ai call just dropped below the network call that carries it' is the author's comparison, and no source read compares token cost with network cost.

Related work

Watch next

  • Open Anthropic's model overview page for the context window. Compare Haiku 5.5's effective cost on a real workload against Haiku 4.5.

Sources

  1. Introducing Claude Haiku 5.5 (Anthropic, October 7, 2026)anthropic.com

Provenance

The note above is reproduced unedited from the original post, first published on Threads on 9 October 2026 at 00:06 IST. Sources are the papers and datasets the note draws on.

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