Claude Haiku 5.5 Is Cheap and Fast, So Which Daily Tasks Is It Good For?

On October 7, 2026, Anthropic released Claude Haiku 5.5 and called it "the cheapest, fastest, and most capable small model we've ever released." Claims like this come with almost every release, so the more interesting question for me is the practical one: which everyday tasks can now go to a small model, and which still need a big one?

Which one is Haiku

Claude comes in three tiers. Opus is the most capable, Sonnet sits in the middle, and Haiku is the smallest, fastest, and cheapest. Haiku has long been associated with light tasks, because it really did trail far behind its two siblings. With Haiku 5.5, that gap has narrowed a lot.

On why the most advanced model isn't necessarily the right one for your work, see The Race to Find the Most Advanced AI Model.

How big the jump is

Of the benchmarks in Anthropic's announcement, the closest to office work is OSWorld, which measures how well a model operates a computer, like opening apps, clicking, typing, and filling in forms.

OSWorld 2.1 score (offline subset), a benchmark for operating a computer(%)

• Claude Haiku 4.515.7%
• Claude Haiku 5.572.4%
• Claude Sonnet 5.583.9%

From 15.7% to 72.4%. Haiku 5.5 is still below Sonnet 5.5, but the gap is no longer as wide as it was. The same pattern shows up in other benchmarks on the same page, such as knowledge work tasks (GDPval-AA) and hard questions across many fields (Humanity's Last Exam).

The price came down too. For prompts up to 100,000 tokens, Haiku 5.5 costs $0.10 per million input tokens and $0.50 per million output tokens, a tenth of Haiku 4.5 ($1 and $5). On average, according to Anthropic, it costs around 75% less to run. In detail, it's 90% cheaper for prompts up to 100,000 tokens and 50% cheaper for longer ones, with the note that its new tokenizer uses slightly more tokens per task.

Daily tasks that suit Haiku 5.5

Anthropic says Haiku 5.5 is designed for high-volume, cost-sensitive tasks. On the Haiku page, its use cases are grouped into five: large-scale classification and summarization, real-time experiences like chat and customer support, subagents, browser and desktop automation, and simple coding. Translated into everyday office work, here's the split I think makes sense:

The easy rule: if a task is short, has clear rules, and gets done many times, start with Haiku.

For teams building their own systems, there's one more pattern, subagents. A bigger model plans the work, then hands the small pieces to Haiku. One of the customers Anthropic quotes, Rogo, uses Haiku 5.5 to pull segment revenue figures from companies' annual reports while a bigger model builds the presentation. In their view, Haiku 5.5 fits short, high-volume work like quick lookups, subagents, and summaries.

What still fits Sonnet or Opus better

Anthropic itself says Sonnet 5.5 and Opus 5.5 remain the better choices for complex agentic coding. On Terminal-Bench 4.0, a benchmark for multi-step coding tasks, Haiku 5.5 scored 39.2% while Sonnet 5.5 scored 70.6%. That's still a wide gap.

Outside of coding, these are the signs I'd move a task up to Sonnet or Opus:

• It needs judgment, not just following rules. For example drafting a strategy, comparing several business options, or assessing contract risk.
• Many steps depend on each other, and a mistake in one step ruins the end result.
• A wrong result is costly, like figures in a financial report or a document for a major client. Whatever the model, results like these still need a human check, as I cover in At What Point Should Humans Still Check AI and Automation Results.
• You've tried it on Haiku and the result falls short. Switching to Sonnet saves more time than repeating the same prompt over and over.

A rough cost calculation

A per-token price difference looks small until you multiply it by volume. As an illustration, say you summarize 1,000 documents a month, each around 20,000 input tokens and 1,000 output tokens. With the official prices on Claude's pricing page, it looks like this:

Illustrative cost of summarizing 1,000 documents through the API(USD)

• Claude Haiku 5.5$2.50
• Claude Sonnet 5.5$50
• Claude Opus 5.5$100

At about Rp16,500 to the dollar, that's roughly Rp41,000 with Haiku 5.5, Rp825,000 with Sonnet 5.5, and Rp1.65 million with Opus 5.5. For tasks Haiku can genuinely handle, that difference is money you can save.

If you use the API, there are two more ways to save:

• The Batch API for work that doesn't have to be done right away, like a recap that can wait until tomorrow morning. The discount is 50% on input and output.
• Keep prompts under 100,000 tokens where possible, because above that Haiku 5.5's price goes up fivefold. Very long documents can be split into parts if the task allows it.

On working out whether automation like this is worth it, see How to Calculate Automation ROI Before You Start the Project.

How to start using it

• In the Claude app. According to the Haiku page, Free, Pro, Max, Team, and Enterprise users can select Haiku 5.5 on Claude.ai on the web, iOS, and Android.
• Through the API, with the model name claude-haiku-5-5. Besides the Claude Platform, Haiku 5.5 is also available on Amazon Web Services, Google Cloud, and Microsoft Foundry, as well as in Claude Code. Its context window goes up to 1 million tokens.
• Set the effort level if you're on the API. Haiku 5.5 is the first Haiku with an effort setting, from Low to Max, so you can choose between lower cost and more intelligence. For routine classification and summaries, start low, then raise it if the results fall short.

If many people on your team use Claude, also agree on which tasks Haiku is enough for and which need a bigger model, then put that in your shared templates. How to do that is in Every Employee Gets Different AI Results, So Build Shared Templates.

Wrapping up

Haiku 5.5 doesn't replace Sonnet or Opus, but it's now a far more sensible first choice for daily work that's short, clear, and repetitive. Start with Haiku, and move up to a bigger model only when the task really needs it. What makes AI expensive is often not the model's price, but using a big model for small jobs.

If you'd like to map out which daily tasks on your team can go to AI or be automated with the right model, tell me what you need.