Claude Haiku 4.5 is Anthropic's conversational model for speed-first tasks, combining text reasoning, coding assistance, and image understanding. claude-haiku-4-5-20251001 uses an explicit date-based version identifier and is suitable for customer service routing, information extraction, and well-bounded coding tasks. It can be integrated into applications 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-haiku-4-5-20251001",
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, inputs and outputs, and invocation methods before selecting a model.
Haiku 4.5's context, maximum output, and reasoning method are native specifications. Applications organize message history using Chat Completions or Messages and provide an actual tool execution environment.
Learn what claude-haiku-4-5-20251001 can bring to your work.
Haiku 4.5 is positioned not merely for short-text completion, but for understanding, summarization, and judgment in fast interactions. It is suitable for organizing user questions into intent, key information, and next-step recommendations, and can also complete classification according to explicit rules. Providing decision criteria and response length in the prompt helps produce more focused results.
You can submit interface screenshots, charts, or document images together with textual questions, allowing the model to describe content, extract visible information, and explain relationships between images and text. It produces textual analysis rather than generating images; for dense tables or small text, provide clear cropped images and ask it to distinguish direct observations from inferences.
It is suitable for proposing modifications, explaining code, or generating test drafts based on given functions, error messages, and acceptance criteria. Breaking large tasks into verifiable smaller steps better matches its speed-first positioning. Tool use can connect to external results, but whether code runs and whether changes are saved still depends on the execution environment.
Start with specific tasks to find where the model can be effective.
Haiku 4.5 is suitable for quickly identifying issue types first, then generating short responses that comply with policy. Provide allowed labels and escalation conditions so the model handles routine issues, while complex tickets or those with insufficient information are handed over to humans or more capable models; reassess when customers add facts rather than retaining the old classification.
Extract customers, amounts, items, and dates from report text or clear page images, require missing fields to be blank, and provide source excerpts. Haiku's speed makes it suitable for high-frequency preprocessing; programs should still verify structure, duplicates, and key data, and important clauses should be checked against the original materials.
Provide relevant code snippets, exception stacks, and expected behavior, and have the model explain the cause of the failure, propose localized fixes, and add test cases. Deliverables can include revised functions, change descriptions, and validation steps. For cross-module changes, clarify interface constraints first, then have developers run tests and review the impact.
Choose based on task complexity, input materials, and expected results.
When task goals are clear and frequent feedback is needed, prioritize Haiku 4.5, such as for ticket classification, information extraction, and localized code changes. The current Sonnet 5.5 emphasizes a balance of speed and intelligence, while Opus and Fable are designed for longer-horizon work. For complex architecture design or cross-step decisions, choose based on evaluations using real tasks rather than series names alone.
Clearly bounded code retrieval, field extraction, or response drafts can be assigned to Haiku 4.5 as specialized subtasks. In each round, provide only materials relevant to the current responsibility and clear feedback; upon completion, return findings, evidence, and unresolved items. Higher-level workflows are responsible for global coordination and final decisions.
Based on the characteristics of claude-haiku-4-5-20251001, first validate small tasks whose results can be checked.
You can ask directly: Find all input validation branches in this code file, organize them by field, and identify missing error messages. Return only findings and evidence; do not modify unrelated implementations.
Provide a single responsibility and completion criteria; evaluate the accuracy, latency, and usage of short tasks, and do not let subtasks make the final global judgment.
Use the full model ID claude-haiku-4-5-20251001, first confirm the public request format and available parameters on the API page, then connect your application. Preserve result parsing, exception handling, and related evidence, and use the same set of real samples to evaluate whether it is suitable for continued use.
Before formal use, understand output quality and capability boundaries.
Answers to common questions when using claude-haiku-4-5-20251001.
Set model to claude-haiku-4-5-20251001. It is the explicit version identifier for Claude Haiku 4.5, suitable for recording test results and managing configurations; do not omit the date suffix yourself, and do not treat the date suffix as the knowledge cutoff date.
It supports combined image and text input, and can explain image content in text, analyze charts, or answer questions related to screenshots; it is not an image generation model. Chat Completions can combine text and image_url blocks in message content, along with specific observation goals.
Include user and assistant messages relevant to the current task in Messages or Chat Completions messages, using the specific format required by the selected public API. Retain the latest code, interim conclusions, and important constraints; when necessary, re-summarize longer history to avoid relying on outdated information.
Prepare the document text, table data, or clear page screenshots relevant to the question, and specify whether you need a summary, comparison, or extraction of particular information. Submit them in the content formats supported by the selected public API; a PDF URL cannot be used as image_url. Require results to retain original-text locations, field evidence, and unconfirmed items, and verify key numbers against the source materials.
Tool workflows should be organized according to the tool definitions and result formats of the selected public API. The model is responsible for planning, explaining results, and generating call suggestions; querying, running code, and writing are completed by the execution environment provided by the application. Actual completion status should come from tool returns and verification records, and cannot be determined solely from the model's description.
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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