Claude 3.5 Haiku is a model introduced by Anthropic for fast interactions, and claude-3-5-haiku-20241022 is its date-fixed version endpoint. While maintaining Haiku's lightweight positioning, it enhances coding, instruction following, and tool use, making it suitable for customer service responses, business information extraction, and dedicated subtasks. Applications can integrate it using the public request format in this page's API section.
Point the Base URL to api.acedata.cloud, configure your platform API key and the model ID below, and use your compatible SDK or client.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["ACEDATACLOUD_API_KEY"],
base_url="https://api.acedata.cloud/v1",
)
response = client.chat.completions.create(
model="claude-3-5-haiku-20241022",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)Choose an available protocol for this model. OpenAI SDK uses a Base URL ending in /v1; Anthropic SDK uses the root URL. See each guide for protocol-specific parameters, tools and response formats.
Clarify capacity, input/output, and invocation methods before selecting a model.
Public evaluations help explain Haiku 3.5's task positioning; Chat Completions and Messages each define request and response formats, while multi-turn messages are organized by the application.
Learn what claude-3-5-haiku-20241022 can bring to your work.
The value of Haiku 3.5 is not only in responding quickly, but also in following task instructions more effectively. You can provide classification rules, wording requirements, and output examples to have it complete customer service replies, summaries, and information organization. Clearly specifying boundary conditions is more likely to produce reusable results than simply asking it to “respond professionally.”
Programming is a capability particularly emphasized officially, with a reported SWE-bench Verified score of 40.6%. It is suitable for explaining causes based on error messages, proposing changes around existing functions, and drafting tests. Evaluation results help clarify capability positioning; generated code should still be run and validated in the project environment.
Improved tool-use capabilities are suitable for sub-agents with clearly defined tasks, such as identifying query intent, organizing function parameters, and then generating explanations based on returned data. The model handles judgment and expression, while business programs handle execution and permission control; this division of labor is easier to test and maintain than having it independently handle open-ended long workflows.
Start with specific tasks to find where the model can be effective.
Include user and assistant messages relevant to the current task in the messages for Messages or Chat Completions, handling the specific format according to the selected public API. Retain the latest code, interim conclusions, and important constraints; when necessary, summarize longer history again to avoid relying on outdated information.
Submit the relevant functions, error stack traces, and expected behavior, asking the model to explain the issue first, then provide narrowly scoped changes and testing suggestions. Deliverables can include a patch draft and a validation checklist. Focusing on local issues better fits its lightweight positioning and also makes it easier for developers to check whether additional dependencies or behavioral changes have been introduced.
Organize purchase history, inventory records, and recommendation rules into text to generate recommendation rationales, restocking reminders, or customer service summaries. You can also attach relevant records and specific questions to obtain written explanations that customers can easily understand. Provide clear records and rules so the model expresses itself based on existing information rather than guessing prices or inventory status on its own.
Choose based on task complexity, input materials, and expected results.
If existing Haiku workloads need better instruction following, coding assistance, or tool parameter organization, 3.5 Haiku is worth testing first. Official descriptions state that its speed is similar to Claude 3 Haiku while improving capabilities across the board. Compare quality using your own classification, extraction, and code samples, and do not interpret this similar speed as a fixed response time.
For clearly bounded tasks, fast interactions, or dedicated subtasks, choose Haiku 3.5; for complex software engineering and longer multistep tasks, further evaluate the same-generation Sonnet. The computer-use public beta in the official announcement applies to Sonnet, so do not expect Haiku to automatically click interfaces just because both belong to the 3.5 series.
Based on the characteristics of claude-3-5-haiku-20241022, first validate small tasks whose results can be checked.
You can ask directly: Identify order inquiries, technical issues, or other tasks from user requests, and extract explicitly provided fields. Leave the order number blank if it is not provided, and do not perform actions on the user's behalf.
Use business classification rules and boundary samples; have the program validate fields and decide whether to execute subsequent tools.
Use the full model ID claude-3-5-haiku-20241022, first confirm the public request format and available parameters on the API page, then connect your application. Retain result parsing, exception handling, and relevant evidence, and evaluate whether it is suitable for continued use with the same set of real samples.
Before formal use, understand output quality and capability scope.
Answers to common questions when using claude-3-5-haiku-20241022.
It is the date-pinned version identifier for this endpoint, corresponding to the announcement date of Claude 3.5 Haiku. It is not Claude 3 Haiku, nor is it the later Haiku 4.5. When you need to explicitly specify this version, use the full model ID to avoid mixing expectations for features from other versions.
This page introduces Claude 3.5 Haiku for text tasks. For vision tasks, use a model explicitly labeled as supporting image input; you can also extract and verify the document text first, then pass it to this model for classification, summarization, or information organization.
Applications that already use the OpenAI messages structure can use Chat Completions; if you need Claude's native message structure or the tool_use/tool_result flow, see Messages API support for this model. Handle the request, response, and parameter formats of the two separately, and retain the full model ID.
It is suitable for explaining errors, generating change drafts, and suggesting tests, but sending a code issue will not automatically modify your project. Actually writing files, running tests, or committing changes requires tools and permissions provided by the application. It is best to have it output a small-scope patch first, then verify the fix with tests.
Do not treat the computer-operation capability in the contemporary Sonnet announcement as a feature of Haiku 3.5. Haiku's strengths in tool use are better suited to working with defined functions and business tasks; actions such as moving the mouse and clicking windows require dedicated capabilities and an execution environment, and are not the default behavior of ordinary conversation requests.
Choose a development approach or application suited to this model and follow its setup guide.
↗OpenAI Chat Completions API
SDK installation, authentication and examples
↗
↗Claude Code CLI Setup Guide
Coding tool setup · requires a Coding Plan application key
↗Using Ace Data Cloud with Cherry Studio
Chat client setup · Base URL, API key and model
↗Using Ace Data Cloud with ChatBox
Chat client setup · Base URL, API key and model↗This is only a basic call example. See the full docs for more parameters and advanced usage.
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