Claude 3.5 Haiku is a model from Anthropic designed for rapid interaction, and claude-3-5-haiku-20241022 is its date-pinned version endpoint. While retaining 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. On this platform, you can conduct conversations through text and image messages, choosing to manage history yourself or use hosted sessions.
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 and output, and invocation methods before selecting a model.
Benchmark results represent the publicly reported performance of the native model; image-and-text submission, streaming output, and session management are usage methods of this platform's interface.
Learn what claude-3-5-haiku-20241022 can bring to your work.
The value of Haiku 3.5 is not limited to fast answers; it also lies 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 stating boundary conditions is more helpful for obtaining reusable results than simply asking it to “answer professionally.”
Programming is a capability especially emphasized officially, with a published 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 scores help clarify capability positioning; generated code should still be run and verified 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.
Provide user questions, return and exchange policies, and order status, and let the model produce reply drafts, issue categories, and information that still needs to be added. When continuous follow-up is needed, use managed sessions to retain context; leave actions involving refunds, commitments, or account changes to business systems for confirmation, while the model primarily handles understanding and communication.
Submit the relevant functions, error stack traces, and expected behavior, and ask the model to explain the issue first, then provide targeted changes and testing suggestions. Deliverables can include a patch draft and a verification checklist. Focusing on local issues better fits its lightweight positioning and also makes it easier for developers to check whether extra dependencies or behavioral changes have been introduced.
Organize purchase history, inventory records, and recommendation rules into text to generate recommendation reasons, restocking reminders, or customer service summaries. You can also attach screenshots and ask specific questions to receive written explanations. Provide clear records and rules so the model can express 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. Officially described as having speed comparable to Claude 3 Haiku while improving skills across the board. It is recommended to compare quality using your own classification, extraction, and code samples, and not interpret this comparable speed as a fixed response time.
Choose Haiku 3.5 for clearly defined tasks, fast interaction, or dedicated subtasks; for complex software engineering and longer multistep tasks, further evaluate the same-generation Sonnet. The computer-use public beta in the official announcement belongs to Sonnet; do not expect Haiku to automatically click interfaces just because both are in the 3.5 series.
From a small-scale task to production integration.
Clarify goals, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
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 release 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 up feature expectations from other versions.
On this platform, you can submit text together with image_url image blocks and have the model answer questions about the images. Chat Completions uses a message content array, while AI Chat v2 uses structured messages. Output is primarily text; this does not make it an image generation model.
If you already have an OpenAI-style client and want to organize the messages history yourself, choose /v1/chat/completions; if you want the service to retain multi-turn conversations, choose /aichat2/conversations, continue asking questions using the returned id, and receive answer and id in the standard JSON response.
It is suitable for explaining errors, generating modification 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 first output a small-scope patch, then verify the fix with tests.
Do not treat the computer-use 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 chat requests.
Model information · Updated: 2026-10-01. For invocation parameters and billing rules, see the API and pricing sections.
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 Configuration 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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