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# Claude Haiku 5.5 Could Run My n8n Agents. It Hasn't Earned It Yet.
- URL: https://blog.kyleelliott.net/claude-haiku-5-5-could-run-my-n8n-agents-it-hasnt-earned-it-yet/
- Published: 2026-10-09T13:07:24.000Z
- Updated: 2026-10-09T13:07:24.000Z
- Description: Claude Haiku 5.5 cuts short-prompt costs by 90% versus Haiku 4.5 and adds effort controls. On paper, it’s a strong engine for high-volume n8n agents. But benchmarks aren’t production. Here’s why it looks promising and what I’d want proven first.
- Author: Kyle Elliott
- Tags: ai, n8n, anthropic

## Why I'm looking

I run most of my LLM steps on Gemini because of token cost. My entire homeschool system runs on Gemini 2.5 Flash, including image reads for submitted screenshots, and it costs me less than $1 a month. That model is deprecated, and the newer Gemini models aren't as cost efficient in my own comparisons, so I'm shopping for what comes next.

Anthropic released Claude Haiku 5.5 on October 7, and the price got my attention. Price gets a model onto my test list. It doesn't get a model into production.

## What's promising

On paper, Haiku 5.5 is built for the work I hand to cheap models: high-volume, latency-sensitive tasks like classification, routing, and extraction ([Anthropic release notes](https://platform.claude.com/docs/en/release-notes/overview?ref=blog.kyleelliott.net)).

Anthropic lists it at $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100,000 tokens. Haiku 4.5 was $1 and $5 ([pricing](https://platform.claude.com/docs/en/about-claude/pricing?ref=blog.kyleelliott.net)). Anthropic also says the cheaper tier covers about 90% of requests to its previous Haiku, and that Haiku 5.5 runs about 75% [cheaper on average](https://www.anthropic.com/claude-haiku-5-5?ref=blog.kyleelliott.net). Those are vendor claims about vendor traffic, not my traffic.

The more interesting part is effort. Haiku 5.5 is the first Haiku with effort levels, from Low to Max, defaulting to Medium ([effort docs](https://platform.claude.com/docs/en/build-with-claude/effort?ref=blog.kyleelliott.net)). Effort sets how much the model thinks and how many tokens it spends, including on tool calls. For routing, where most inputs are easy, that's a real lever. In n8n's Anthropic Chat Model node, it shows up as Thinking Mode set to Adaptive plus an Effort setting. In my instance, that setting only offers Low, Medium, and High for Haiku.

## Open question 1: reliability

Cheap doesn't matter if the output is wrong. The failures I see most are misclassifications and inconsistent output. A step asks for raw JSON, gets JSON wrapped in a markdown code fence, and the next node chokes on it.

Anthropic's [benchmarks](https://www.anthropic.com/claude-haiku-5-5?ref=blog.kyleelliott.net) don't tell me how Haiku 5.5 handles my inputs. They're run by the vendor, on tasks the vendor picked.

There's also a new wrinkle. Haiku 5.5 rejects custom temperature, top\_p, and top\_k values ([migration guide](https://platform.claude.com/docs/en/models/haiku-5-5/migration-guide?ref=blog.kyleelliott.net)). I can't turn temperature down to chase consistent labels. Consistency has to come from structured output, schema validation, and prompting, and I have to measure it rather than assume it.

![a sign that says atm on the side of a building](https://images.unsplash.com/photo-1648484505469-d3646b801440?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDE0fHxhaSUyMG1vbmV5fGVufDB8fHx8MTc5MTU1MDkwNXww&ixlib=rb-4.1.0&q=80&w=2000)

Photo by [Maria Oswalt](https://unsplash.com/@mcoswalt?ref=blog.kyleelliott.net) / [Unsplash](https://unsplash.com/?utm%5Fsource=ghost&utm%5Fmedium=referral&utm%5Fcampaign=api-credit)

## Open question 2: real cost

The sticker price isn't my price yet, for two reasons.

First, Haiku 5.5 uses a newer tokenizer. Anthropic says the same text produces roughly 30% more tokens than on Haiku 4.5 ([migration guide](https://platform.claude.com/docs/en/models/haiku-5-5/migration-guide?ref=blog.kyleelliott.net)). Any token count I measured on another model is wrong for this one.

Second, pricing is tiered by prompt length. A request whose prompt goes over 100,000 tokens pays $0.50 input and $2.50 output, and cached tokens count toward that threshold ([pricing](https://platform.claude.com/docs/en/about-claude/pricing?ref=blog.kyleelliott.net)). A single classification call won't get close. An n8n AI Agent loop that resends its history and tool results on every iteration can. I need to recount my real prompts on this model and watch for long agent runs crossing that line.

## Open question 3: low effort

Low effort is where the savings are, and it's also where Anthropic's own docs flag risk. In long agent prompts at low effort, the model is more likely to skip a search, stop early, or skip a check ([effort docs](https://platform.claude.com/docs/en/build-with-claude/effort?ref=blog.kyleelliott.net)). In an automation, an agent that quietly stops early and reports success is worse than one that fails loudly.

I've been burned by a version of this already. I once had less capable models handling the planning phase of a workflow, and the steps that built from that plan were less successful because of it. A weak plan poisons everything downstream.

So my starting position is simple. Cheap effort goes into execution steps with narrow inputs, like routing and extraction. Planning stays on a stronger model or a higher effort level until testing says otherwise.

## What I'd validate before going live

My bar for going live with my use case: the model completes a task in fewer than three retries, keeps hallucinations to a minimum, and asks a follow-up question when it doesn't know something instead of making it up. For Haiku 5.5, that becomes this checklist:

- **Classification accuracy:** on a labeled set of my own real inputs, checked per category, not just overall.
- **Raw JSON every time:** zero markdown-wrapped outputs across repeated runs, with a schema check that rejects anything else.
- **Fewer than three retries:** to complete a task, measured rather than estimated.
- **Asks instead of guessing:** inputs deliberately missing information should produce a question, not an invented answer.
- **Effort sweep:** the same tests at Low, Medium, and High, keeping the lowest level that passes.
- **Real cost per task:** from actual token usage, compared against what I pay on Gemini today.
- **Monitoring before launch:** token usage, stop reasons, and retries logged per execution. This is a gap I haven't closed yet, and it's the first one I need to.

## Where I land

Haiku 5.5 is the most interesting cheap model I've looked at in a while. The pricing and effort controls line up with exactly the kind of work I run in n8n. But almost everything I know about it comes from the company selling it, and it's been out for days. I'll test it and see how it performs. It still has to earn production the same way every other model has.