Claude Opus 4.5 is Anthropic's model for complex programming, in-depth analysis, and agent tasks, and claude-opus-4-5-20251101 is its date-fixed version. It excels at understanding ambiguous requirements, weighing multiple approaches, and organizing solution paths using text and images. It is suited to work that requires deep judgment, step-by-step progress, and repeated validation, rather than simply generating a short answer.
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-opus-4-5-20251101",
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.
effort is a native Claude control capability and is not equivalent to parameters with the same or similar names in other protocols. Message history, tool results, and result retrieval each follow the selected public API.
Learn what claude-opus-4-5-20251101 can bring to your work.
Opus 4.5's programming focus is not merely code completion, but understanding cross-module dependencies and trade-offs in modifications. After providing error logs, relevant code, and expected behavior, you can ask it to first organize failure hypotheses, then propose fixes, test cases, and regression checks. It is suitable for refactoring, migration, and problems that are difficult to locate directly.
When tasks have incomplete conditions or conflicting goals, it is well suited to first identify key constraints and then compare viable approaches. Agent applications can use its planning and tool-use capabilities to connect querying, analysis, and result organization; executing external actions still requires tool connections, permission configuration, and result returns.
The model has visual understanding capabilities and can analyze UI screenshots, charts, and document images together with textual questions. Compared with reading text alone, text-and-image input is better suited to discussing layouts, abnormal states, and chart relationships; providing clear images and necessary context helps generate explanations and recommendations tailored to specific materials.
Start with specific tasks to identify where the model can be effective.
Provide existing modules, the target technology stack, compatibility requirements, and testing constraints, and have the model produce a migration sequence, a list of interface changes, and key code drafts. More complex projects can be discussed in stages: confirm the design first, then make changes item by item, and finally check for omissions based on test results, avoiding a one-time rewrite that is difficult to validate.
Provide the research question, original text, and decision criteria, and have Opus 4.5 compare viewpoints, extract assumptions, and identify items to be verified. Deliverables can include option comparison tables, executive summaries, or action recommendations; record facts and forecasts separately, and supplement necessary real-time information through external retrieval or additional materials.
Submit business charts, product screenshots, and problem descriptions together, for example to explain metric anomalies, review interaction flows, or discuss page redesigns. The model can produce observations, possible causes, and modification priorities; when precise values are involved, also provide the raw data to separate visual judgment from data calculations.
Choose based on task complexity, input materials, and expected results.
Compared with Sonnet 4.5, Opus 4.5 is more worthwhile for difficult code, complex trade-offs, and tasks requiring sustained reasoning. Official software engineering evaluations show that its medium effort can already achieve Sonnet 4.5's best results, but this does not mean all businesses will receive the same benefits. For routine extraction and short Q&A, evaluate lighter-weight options first.
Applications that already use the OpenAI messages structure can use Chat Completions; when Claude-native content blocks, thinking, or tool_use/tool_result flows are needed, check Messages API support for this model. Handle the two request, response, and parameter formats separately, and retain the full model ID.
Based on the characteristics of claude-opus-4-5-20251101, first validate small tasks whose results can be checked.
You can ask directly: compare the claims, evidence, and limitations of these materials, output a comparison table, then list the conditions required for the conclusions to hold. Do not treat marketing language in the materials as independently verified.
Provide the original text and the scope of the question; verify the evidence chain for citations, conditions, and professional judgments.
Use the full model ID claude-opus-4-5-20251101, first confirm the public request format and available parameters on the API page, then connect the application. Preserve result parsing, exception handling, and relevant evidence, and use the same set of real samples to evaluate whether it is suitable for continued use.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using claude-opus-4-5-20251101.
No. 20251101 is part of the fixed-date version invocation ID. The official release date of Claude Opus 4.5 is November 24, 2025. Using the full ID when invoking it helps clarify version dependencies, but does not guarantee exactly the same answer every time.
It is better suited for cross-module issues, code refactoring, migration planning, and complex reviews. Provide the relevant code, error messages, expected behavior, and constraints that cannot be changed, then have it propose diagnostic and testing plans before generating a draft of the changes; the code still needs to be verified in the actual environment.
Prepare document text, tabular data, or clear page screenshots related to the issue, and specify whether you need a summary, comparison, or extraction of particular information. Submit content in formats supported by the selected public API; a PDF URL cannot be used as image_url. Require the results to preserve original locations, the basis for fields, and unconfirmed items, and verify key figures against the source materials.
They cannot be treated directly as the same parameter. effort controls thinking investment in the native Claude API; compatible APIs use their own parameter formats and should not be copied mechanically. When in-depth analysis is needed, you can also explicitly ask in the task to compare options, state assumptions, and provide verification steps.
When using Chat Completions, the client organizes the messages history. For long tasks, it is recommended to retain phase summaries and clearly state which conclusions have been confirmed and which items are still pending.
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↗Input
$1.00per 1M tokens
Official $5.00
Output
$5.00per 1M tokens
Official $25.00
You pay$13,584for 142,800 credits · $0.0951 / credits46% OFF
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This is only a basic call example. See the full docs for more parameters and advanced usage.
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