Haiku 5.5 in business: what it’s good for, pros, cons and a test
Haiku-class AI models in business: where they work best, where they fall short, how to test them on your own questions and what to watch out for.
People ask me whether a new, small AI model is enough for a chatbot, for answering emails or for sorting inquiries in a company. A model from the Haiku family is exactly this kind of candidate: it is meant to be fast and cheap to use. Before you decide, though, it is worth knowing what such a model is good at and where it is better to reach for a larger one.
First, one honest note. I don’t give test results or prices for the specific version 5.5 here, because I don’t want to make them up for you. I write about what works across the whole class of small models, and I show how to evaluate Haiku 5.5 yourself on your own data. You will find the technical details and the price list in Anthropic’s documentation.
What Haiku is and what to look at
Haiku is the name of the smallest and fastest models in Anthropic’s Claude family. The larger models in this family are called Sonnet and Opus. A small model usually answers faster and costs less per conversation, but it has less “headroom” on hard tasks.
Before choosing a model, check four things in the provider’s documentation:
- how well it handles Polish, because that is not the same as English,
- how much text it can read at once (the context window),
- whether it supports images and tools, if you need them,
- what a request and a response cost at your traffic level.
Where a small model is strong
For these tasks a small model is usually enough, and speed and low cost give a real advantage:
- Answers to typical customer questions from a knowledge base: the offer, opening hours, the scope of services, what an order looks like.
- Sorting messages: is it a quote request, a complaint, spam or an invoice.
- Extracting data from text: a name, phone number, city and type of service from a customer’s loose message.
- Short summaries of conversations and emails.
- Draft replies that a person reads and approves.
- Tasks repeated thousands of times, where the price of a single call matters.
Where a customer is waiting for an answer in a chat, speed matters too. An answer after a few seconds and an answer after a dozen or so are two different experiences for the visitor.
Where it does worse
Small models more often fail at tasks that require longer reasoning:
- complex calculations with many conditions, for example a quote built from a dozen or so dependencies,
- hard code and debugging large projects,
- long documents in which you have to connect facts from distant passages,
- ambiguous instructions, when a customer writes chaotically and you have to guess the intent,
- texts your reputation depends on: offers for large clients, legal letters.
In such cases a larger model usually gives a more stable result. That doesn’t mean the small one won’t try. It means it will more often do so with confidence and still get it wrong.
Pros and cons at a glance
| Pros | Cons | |
|---|---|---|
| Speed | short response time in chat | doesn’t always translate into quality |
| Cost | cheaper with a large number of conversations | the savings vanish when you have to fix mistakes |
| Simple tasks | enough for FAQs, sorting and extraction | – |
| Hard tasks | – | more often wrong in complex reasoning |
| Scaling | easy to handle heavy traffic | needs limits and monitoring like any model |
How to test a model in 5 steps
This is the most important part. No ranking replaces a test on your own questions.
- Collect 30–50 real customer questions from emails, chat and phone calls. Add a few tricky ones, for example about a price you don’t give or a service you don’t offer.
- Prepare model answers, or at least a list of facts that have to appear in them.
- Run the same questions through two models: a small one and a larger one, with identical instructions.
- Rate the answers on a scale: good, needs work, wrong. The wrong ones matter most, especially the made-up ones.
- Calculate the cost at a realistic number of conversations and compare it with the number of errors.
If the small model performs about the same as the large one, you choose the smaller. If it mixes up prices or invents services, you pay more, but you have peace of mind.
Tips for deployment
Regardless of the model, a few rules make the biggest difference:
- Give the model only verified information: the offer, the price list, the rules. Write in the instructions that it should answer solely on that basis.
- Say plainly what it should do when it doesn’t know the answer: “I don’t have that information, please leave a number and we will call you back”.
- Set limits: conversation length, the number of messages per hour and a daily cost budget.
- Keep the set of test questions. After every change of model or instructions, run it again.
- Hand difficult matters to a person, especially complaints and quotes.
I described security issues, such as impersonating instructions or forging the conversation history, in the guide on a secure AI chatbot. They apply to every model, including a small one.
When to choose a larger model
Consider a larger model when:
- the chatbot is supposed to calculate quotes from many variables,
- it is supposed to analyze long contracts or documentation,
- a mistake costs more than the difference in price per request,
- the test clearly showed more wrong answers.
You can also combine both approaches: a small model answers the typical questions, and the harder matters go to a larger one or to a person. In such a setup you pay less, and the risk stays under control.
Remember the AI Act
Regardless of the model, a chatbot on a website has to inform the customer that they are talking to AI. From 2 August 2026, Article 50 of the AI Act requires this. I described the details in the guide on AI chatbots and the AI Act.
Summary
A small model such as Haiku is a good choice for fast, repetitive tasks: answers from a knowledge base, sorting and extracting data. For hard reasoning and important texts, a larger one is sometimes better. The choice is decided by a test on your own questions, not by the name of the model.
If you want me to pick a model and run such a test for your company, see the AI chatbot for your website or process automation. Write to me; I reply within 24 hours, and the first conversation and quote are free.
Frequently asked questions
For answering typical customer questions, such as the offer, opening hours, the scope of services or the ordering process, a small and fast model is usually enough. Always check it on 30–50 real questions from your customers before you deploy it. The test result matters more than opinions from the internet.
Not always. In simple tasks, such as classifying messages, extracting data or giving short answers from a knowledge base, the difference is often small. In long, multi-step tasks and hard code, larger models usually do better.
Work out how many conversations or documents you want to handle per month and multiply that by the average length of the text. Always take the current price list from the model provider’s website, because prices change. For a chatbot, it is worth setting a daily cost limit so that nothing surprises you.
Yes, if the deployment is done properly, the model is a replaceable element. You change a setting, then run the same set of test questions and compare the results. That is why it is worth keeping such a set.
Yes, from 2 August 2026, under Article 50 of the AI Act. The information should appear in the chat from the first message, regardless of which model is behind it.
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