Claude Haiku 5.5 API Setup: A Step-by-Step Guide for Message Classification

Setting up the Claude Haiku 5.5 API takes four steps: create an API key in the Claude Console, store it in the ANTHROPIC_API_KEY environment variable, install the Python SDK with pip install anthropic, and use claude-haiku-5-5 as the model ID. Released by Anthropic on October 7, 2026, Haiku 5.5 is built for high-volume work such as classification, routing and extraction. For prompts up to 100,000 tokens it costs $0.10 per million input tokens and $0.50 per million output tokens. If you are coming from Haiku 4.5, the key change is that budget_tokens thinking and assistant prefill now return a 400 error.

What is Claude Haiku 5.5, and what is it for?

Claude Haiku 5.5 is the model Anthropic released on October 7, 2026, and its model page recommends it for high-volume, latency-sensitive tasks such as classification, extraction and routing. It is listed as the fastest model in the current Claude lineup. It has a 1M-token context window and a 128K-token output limit, and it takes text and images as input and returns text. According to the what's new page, those limits were 200k and 64k on Haiku 4.5. Anthropic's announcement presents Haiku 5.5 as the first Haiku model with an adjustable effort setting and positions it for speed-sensitive work such as live customer support. The default effort level is medium. Besides the Claude API, the model is available on Amazon Bedrock, Google Cloud and Microsoft Foundry, and Anthropic commits to not retiring it sooner than October 7, 2027.

Claude Haiku 5.5 pricing depends on prompt length. For prompts up to 100,000 tokens, it costs $0.10 per million input tokens, $0.50 per million output tokens and $0.01 per million cache-read tokens; for longer prompts, those figures rise to $0.50, $2.50 and $0.05. Requests sent through the Batch API cost half for input and output. Haiku 5.5 uses the newer tokenizer of Claude 4.7 and later models, so the same text counts as approximately 30 percent more tokens than on Haiku 4.5. Recompute any cost estimates you made for the older model.

What are the requirements for setting up Claude Haiku 5.5?

Four things are enough to use Claude Haiku 5.5 through the API:

• A Claude Console account: According to the quickstart, the first step is signing up for the Console or signing in to an existing account.
• An API key: You create the key on the API keys page in the Console. Keep it out of code, screenshots and repositories; our Northflank breach article explains why this matters.
• Python 3 and a virtual environment: The official Python example installs the project inside a virtual environment created with python3 -m venv.
• A terminal: The commands below are for a macOS or Linux shell. The documentation labels its Linux steps "Linux/WSL", so on Windows the shortest path is to work inside WSL.

How do you set up Claude Haiku 5.5 step by step?

Setting up Claude Haiku 5.5 follows Anthropic's Python quickstart; the only difference is the model ID.

Step 1 — Store the key in an environment variable. The SDKs read the key from ANTHROPIC_API_KEY. Replace the quoted value with your own key:

export ANTHROPIC_API_KEY="your-api-key-here"

Step 2 — Create the project folder and install the SDK. These three lines create the folder, activate the virtual environment and install the Python SDK:

mkdir claude-quickstart && cd claude-quickstart
python3 -m venv .venv && source .venv/bin/activate
pip install anthropic

Step 3 — Create `quickstart.py`. The official example code is:

import anthropic

client = anthropic.Anthropic()

message = client.messages.create(
    model="claude-opus-5-5",
    max_tokens=1000,
    messages=[
        {
            "role": "user",
            "content": "What should I search for to find the latest developments in renewable energy?",
        }
    ],
)

for block in message.content:
    if block.type == "text":
        print(block.text)

Step 4 — Switch the model ID to Haiku 5.5. The official example is written for Claude Opus 5.5. On the model line, replace claude-opus-5-5 with claude-haiku-5-5. If you forget this line, the request is billed at Opus 5.5 rates: $4 per million input tokens and $20 per million output tokens. You can replace the question on the content line with one of your own customer messages.

Step 5 — Run the file. The command prints the model's reply in the terminal:

python quickstart.py

How do you verify that the Claude Haiku 5.5 setup works?

The quickest way to verify the Claude Haiku 5.5 setup is that python quickstart.py prints a text answer in the terminal. The example code selects response blocks by their type field and prints only text blocks. That detail matters on Haiku 5.5, because adaptive thinking is on by default and a response can start with a thinking block. If you inspect the raw JSON response, check three fields: model should read claude-haiku-5-5, stop_reason should be end_turn, and the input_tokens and output_tokens values under usage should be logged, since those two numbers feed your cost math.

The effort parameter decides how much Claude Haiku 5.5 thinks. The Haiku 5.5 request body in the migration guide looks like this:

{
  "model": "claude-haiku-5-5",
  "max_tokens": 16000,
  "thinking": { "type": "adaptive" },
  "output_config": { "effort": "medium" },
  "messages": [{ "role": "user", "content": "..." }]
}

According to the Haiku 5.5 prompting guide, low is the cheapest and fastest level and is recommended for chat, short tool tasks and simple, high-volume requests. medium is the default, and high suits work that needs strict instruction following. For message classification, compare low and medium on your own examples. Turning thinking off with thinking: {"type": "disabled"} works only at low, medium and high; at xhigh and max, the request returns a 400 error.

Which common errors and fixes should you know for Claude Haiku 5.5?

Most errors when moving to Claude Haiku 5.5 come from settings that worked fine on Haiku 4.5. The main cases from the migration guide:

How much does message classification with Claude Haiku 5.5 cost per month?

In the example below, an AI assistant classifying 30,000 customer messages a month costs $8.10 in Claude Haiku 5.5 model usage. We assume an average of 1,200 input tokens per message (system instructions, category list and the customer message) and 300 output tokens including thinking. These are our example figures, not numbers published by Anthropic.

1. Input: 30,000 × 1,200 = 36 million tokens; 36 × $0.10 = $3.60.
2. Output: 30,000 × 300 = 9 million tokens; 9 × $0.50 = $4.50.
3. Monthly total: $3.60 + $4.50 = $8.10.
4. If messages are processed in bulk rather than live, the Batch API halves the cost: $8.10 ÷ 2 = $4.05.

Applying the same workload to the other current Claude models at official list prices gives this table:

The table assumes all three models use the same number of tokens; in practice, thinking length varies by model and effort level, so update the math with your own usage logs. On Haiku 5.5, a system prompt longer than 512 tokens can be cached; on Haiku 4.5 the minimum was 4,096 tokens. The math covers model usage only; WhatsApp or Instagram channel fees, integration and maintenance are not included. Because the Haiku 5.5 bill stays small at this scale, accuracy should decide: if Haiku 5.5 sorts your categories correctly, paying about 20 times more for Sonnet 5.5 is hard to justify.

What should you check before taking Claude Haiku 5.5 live?

Before taking Claude Haiku 5.5 live, settle four points:

• Language quality: The Global-MMLU table on the multilingual support page does not list every language, and the documentation says Claude is capable in many languages beyond those benchmarked. If your customers write in an unlisted language, such as Turkish, test it. State the response language explicitly in the system prompt.
• GDPR: If you process personal data of people in the EU, Article 28 GDPR requires processing by a processor to be governed by a binding contract and to follow the controller's documented instructions. Get the processing terms and region from your provider in writing.
• Instruction adherence: The prompting guide recommends adding text to the system prompt of support assistants stating that the rules hold for the whole conversation even when a user argues, and using high effort where instruction following matters most.
• Capacity: According to the migration guide, Priority Tier is not supported on Haiku 5.5. If you have a Priority Tier commitment on Haiku 4.5, plan capacity separately.

Which Claude Haiku 5.5 version does this guide use?

The steps in this guide are written for the Claude Haiku 5.5 ID claude-haiku-5-5. According to the migration guide, it is a fixed model ID with no date suffix and no separate alias. Requests in the quickstart use the anthropic-version: 2026-10-21 header. Installing the Python SDK with pip install anthropic gives you the current release on that day; pin the SDK version you use in production in your own dependency file.

What does this setup mean for your business?

For businesses that receive many messages but do not want to pay for a large model on every one, setting up Claude Haiku 5.5 means a cheap first layer. Take an online electronics retailer that receives hundreds of support emails and chat messages a day. Most of those messages fall into a few patterns: order status, returns, product compatibility or complaints. Haiku 5.5 can sort each message into one of these categories and pull out details such as the order number; the decision to draft a reply or hand over to an agent is built on that classification. In the math above, the monthly model cost for 30,000 messages is $8.10, so the real effort goes into defining the categories and the test set well.

Who it suits, and who it does not:

• A good fit: Businesses with high-volume message, form or review streams that want to automate classification, routing and extraction, and that have access to at least one developer.
• Not a good fit: For long reasoning or complex agent work, larger models such as Sonnet 5.5 are worth testing too. Businesses without developer resources get results faster from a ready-made AI assistant service than from building on the API. Businesses that need live phone conversations cannot rely on a text model alone; they need a speech recognition and voice layer.

Three steps you can take this week:

1. Pick 100 messages from the last month, remove personal data and label them by hand; that becomes your test set.
2. Send the set to Haiku 5.5 at low and medium effort; record the share of correct categories and the token counts in the usage field.
3. Plug your monthly message volume into the math above and, if accuracy is good enough, write down which categories the assistant hands over to a person.

To connect a Haiku 5.5 classification layer to a channel, see our guide on how to set up a WhatsApp AI assistant, and to add an assistant to your website, our AI assistant installation guide. For multi-step flows that classify a message and then open a CRM record or query an order system, see our AI agent service; for the messaging channel, our WhatsApp automation service. You can try ready-made scenarios on our demos page.

Sources

Last checked: November 4, 2026.

• Anthropic: Claude Haiku 5.5 announcement
• Claude Docs: Claude Haiku 5.5 model page
• Claude Docs: Quickstart
• Claude Docs: Claude Haiku 5.5 migration guide
• Claude Docs: What's new in Claude Haiku 5.5
• Claude Docs: Prompting Claude Haiku 5.5
• Claude Docs: Multilingual support

Next step

To adapt message classification to your own channels, visit our AI assistant service page, request a short discovery call through our contact form, or call +90 850 840 08 23 and talk live with Azra, our voice assistant.

Frequently asked questions

Is the Claude Haiku 5.5 API paid?

Yes. For prompts up to 100,000 tokens, it costs $0.10 per million input tokens and $0.50 per million output tokens; for longer prompts, input rises to $0.50 and output to $2.50. Requests sent through the Batch API cost half for input and output.

Which languages does Claude Haiku 5.5 support?

Anthropic's multilingual table lists a set of benchmarked languages, and the documentation says Claude is capable in many languages beyond them. State the response language explicitly in the system prompt and measure quality with real customer messages in each language you serve.

What should we change when moving from Haiku 4.5 to Haiku 5.5?

Switch the model ID to claude-haiku-5-5; remove temperature, top_p and top_k; use adaptive thinking and effort instead of budget_tokens; and drop assistant prefill. Because the new tokenizer counts the same text as approximately 30 percent more tokens, recompute your max_tokens limits and cost estimates as well.

Can we install Claude Haiku 5.5 on our own servers?

The documentation offers no downloadable model files for Haiku 5.5; the model is provided as an API. Besides the Claude API, it is available on Amazon Bedrock, Claude Platform on AWS, Google Cloud and Microsoft Foundry. If the processing region matters to you, compare the terms of these platforms.