Claude Haiku 5.5: What the Cheapest Claude Means for Teams

Claude Haiku 5.5 is Anthropic's new small model, released on 7 October 2026. It is the fastest and cheapest model in Anthropic's current line-up. It costs $0.10 for every million tokens it reads and $0.50 for every million it writes, as long as the prompt stays under 100,000 tokens (a token is a small piece of a word). It can read about 555,000 words at once. It will not replace the bigger models for hard building work, but it makes a lot of routine work much cheaper.

This article covers what Claude Haiku 5.5 is, what changed since the last Haiku, and what it means for a team that builds software with AI coding agents.

What is Claude Haiku 5.5?

Anthropic's models overview lists four current models: Fable 5.1, Opus 5.5, Sonnet 5.5 and Haiku 5.5. Haiku 5.5 is listed as the fastest of the four and has the lowest price. Anthropic describes it as the model "for high-volume, latency-sensitive tasks such as classification, extraction, and routing". In plain words, it is for jobs you run thousands of times where a quick answer matters: sorting incoming tickets, pulling fields out of documents, or deciding which specialist a request should go to.

Anthropic's Haiku 5.5 page adds a fourth use that matters to coding teams: "subagent tasks". A subagent is a helper that a main AI agent sends off to do one narrow job, such as searching the code or summarising a log, before it reports back.

From launch, the model was available through Anthropic's own API (the service developers call from their code) and on Amazon Bedrock, Google Cloud, Microsoft Foundry and Claude Platform on AWS. Anthropic says it will not retire the model before 7 October 2027.

Does Claude Code use Haiku 5.5?

Yes. The release notes for Claude Code version 2.1.293, published on 7 October, say Haiku 5.5 is "now the default Haiku model on the Anthropic API". So if you use Claude Code through Anthropic's API, the work it hands to its Haiku model now goes to 5.5 once you are on that version or later.

What changed from Haiku 4.5?

Anthropic's what's new page lists the main differences:

• Much more room to read. The context window (how much text the model can hold in mind at once) grows from 200,000 tokens to 1 million.
• Longer answers. One reply can be up to 128,000 tokens long, up from 64,000.
• It decides how much to think. Haiku 5.5 now chooses for itself how much to reason before it answers. A setting called "effort" lets developers turn that up or down, trading quality against speed and cost. The default is medium.
• The same text counts as more tokens. Haiku 5.5 splits text into tokens in a newer way, so the same text comes out about 30% more tokens than on Haiku 4.5. You pay per token, so compare total bills, not only the price per token.
• More tokens in long conversations. In a back-and-forth conversation, the model's reasoning from all earlier turns now stays in its memory and is charged as input. Haiku 4.5 kept only the latest turn's reasoning.
• Cheaper repeated instructions. Prompt caching lets you pay much less when the same opening text is sent again. It now works on prompts as short as 512 tokens, down from 4,096.

Some changes also break existing code. Haiku 5.5 returns errors for some settings that older programs send, such as the ones that controlled how random the answers were, and for the older way of setting a fixed thinking budget. It also no longer accepts a common way of steering a reply, which was writing the first words of the answer for the model. If your product already calls Haiku 4.5, moving to 5.5 takes some developer work. Changing the model name is not enough.

How much does Haiku 5.5 cost?

Prices from Anthropic's Haiku 5.5 page, in US dollars per million tokens:

• Prompts up to 100,000 tokens: $0.10 to read, $0.50 to write.
• Prompts over 100,000 tokens: $0.50 to read, $2.50 to write, five times as much.
• Re-reading cached text: $0.01 (or $0.05 for prompts over 100,000 tokens).
• Batch jobs (work you can wait for instead of getting it back at once): 50% off.

For comparison, the models overview lists Sonnet 5.5 at $2 to read and $10 to write, and Opus 5.5 at $4 and $20. For short prompts, Haiku 5.5 reads text at a twentieth of Sonnet's price and a fortieth of Opus's.

The catch is the 100,000-token line. The 1-million-token window is useful, but once a prompt goes over that line, every token in that request costs five times the base rate, including the reply. Cached text counts toward the line too. A job that loads a whole codebase into every request will cost much more than the headline price suggests. Anthropic's cost guide suggests shortening a long session when one task ends, before its requests pass 100,000 tokens.

Is Haiku 5.5 good enough for coding?

That depends on the job. Anthropic's guide to choosing a model says "most workloads start with Claude Opus 5.5". It points to Opus for "multihour autonomous coding agents" and to Sonnet 5.5 for everyday coding. Haiku 5.5 is listed for "the lowest latency and price", including "sub-agent tasks". The same guide also describes a cheaper starting route: begin with Haiku 5.5, test it, and move up only where it falls short. It suggests that route for prototypes, cost-sensitive products and high-volume, straightforward tasks.

The cost guide gives one direct comparison. On GPQA Diamond, a hard science-reasoning test, Haiku 5.5 scored 85% at its default setting, against 91% for both Opus 5.5 and Sonnet 5.5. (Anthropic notes that Opus and Sonnet tied only because refusals counted as wrong answers.) Haiku's cost per correct answer was "about a twentieth of Claude Opus 5.5's cost per correct answer and about a tenth of Claude Sonnet 5.5's". The guide says Haiku 5.5 "suits high-volume and latency-sensitive work with checkable outputs". It calls its figures internal and "directional, not guarantees", and tells readers to measure on their own work.

"Checkable" is the important word. If a wrong answer gets caught because a test fails, a format check rejects it or a reviewer spots it, a cheaper model that is usually right can save a lot. If wrong answers slip through unnoticed, the savings can turn into rework.

What does this mean for a team building software?

One option is to mix models. Anthropic's cost guide describes two ways to do that:

1. A big model in charge, smaller models doing the bulk work. The capable model plans and checks, and cheaper models handle many separate pieces. In Anthropic's measurements, which used an earlier Sonnet model as the helpers rather than Haiku 5.5, this saved money in only two cases: routine work where a few pieces cost far more than the rest, and work too big for one model to read at once. Otherwise, the main model on its own at a lower effort setting came out ahead.
2. A small model doing the work and asking a big model for advice. The cheaper model runs the task and asks a stronger "advisor" model only at hard moments. This helps only if the small model actually asks. In one of Anthropic's tests, Haiku 5.5 paired with an Opus 5.5 advisor never asked once across 198 questions. The pair scored about the same as Haiku alone and cost 12% more per question.

Before trying either approach, Anthropic suggests a simpler step: turn down the effort setting on the model you already use. Its model guide says "tuning effort is often a better lever than switching models".

For a founder or manager, these are the questions to ask your team:

• Which of our AI tasks are high-volume and easy to check? Likely candidates are ticket triage, summarising test logs, tagging data and first-pass searches through code. Architecture design and fixing subtle bugs are not.
• Have we tested it on our own work? Anthropic suggests taking a few real tasks from your own logs, writing a check for each that shows whether it succeeded, and recording the cost of each task next to its score. Run the same set on your current model and on Haiku 5.5, and compare cost per finished task as well as price per token.
• Are we filling the big window? If prompts often go over 100,000 tokens, the higher rate applies. Cutting down what goes into each request may save more than changing models.
• Who updates the code? If your product calls Haiku 4.5 today, set aside some developer time for the breaking changes.
• Will we check again? Anthropic says to "recheck at every model release", because each new model changes both the quality gap and the price gap.

How Atlas relates to this

Atlas runs each AI engineer as its own Claude Code session, with the model and effort chosen to suit that engineer's role. That follows the same idea: not every job needs the biggest model. Atlas's published material does not say that any role uses Haiku 5.5, or that Atlas tests a new model against a role's past results before switching. In Atlas or anywhere else, treat a model change as something to measure first.

The bottom line

Claude Haiku 5.5 makes fast, simple, repeated AI work much cheaper, and it can read far more at once than the last Haiku. It does not replace Opus or Sonnet for the hard parts of building software, and long prompts cost five times as much. Anthropic's advice is to find the high-volume jobs where mistakes get caught, test Haiku 5.5 on those jobs with your own work before moving anything over, and check again when the next model comes out.