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-pinned version. It excels at understanding ambiguous requirements, weighing multiple approaches, and organizing solution paths using text and images. It is suited to work requiring deep judgment, step-by-step progress, and repeated verification, 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, inputs and outputs, and invocation methods before choosing a model.
Native effort is a control capability of the Claude API; this platform's message format, conversation persistence, and tool execution are determined by the selected interface and authorization configuration.
Learn what claude-opus-4-5-20251101 can bring to your work.
The programming focus of Opus 4.5 is not just code completion, but understanding cross-module dependencies and modification trade-offs. 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 issues that are difficult to pinpoint directly.
When facing tasks with incomplete conditions or conflicting goals, it is well suited to first identify key constraints and then compare feasible options. Agent applications can use its planning and tool-use capabilities to connect querying, analysis, and result organization; performing external actions still requires tool connections, permission configuration, and result feedback.
The model has visual understanding capabilities and can analyze interface screenshots, charts, and document images together with text-based questions. Compared with reading text alone, combined image and text input is better suited for 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 find 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 using test results, avoiding one-shot rewrites that are difficult to validate.
Provide research questions, source text, or file links, and ask the model to compare viewpoints, extract assumptions, and organize items to be verified. AI Chat v2 can work with file-reading or search tools to process materials and deliver summaries, option comparisons, and action recommendations; current facts should be based on retrieval results rather than relying solely on the model's knowledge.
Submit business charts, product screenshots, and problem descriptions together, for example to explain metric anomalies, inspect 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 so that visual judgments can be separated from data verification.
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 every business will receive the same benefit. For routine extraction and short Q&A, evaluate lighter-weight options first.
When an existing project has established a test set around Opus 4.5, you can continue using the date-fixed version to manage version dependencies, but a fixed ID does not mean responses will be completely identical. If you manage message history yourself and integrate with an existing SDK, choose Chat Completions; if you need to save conversations, read files, and observe the tool process, choose AI Chat v2.
From a small-scale task to production integration.
Define the goal, 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 boundaries.
Answers to common questions about using claude-opus-4-5-20251101.
No. 20251101 is part of the invocation ID for the date-pinned version. 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 first propose a diagnosis and test plan before generating a draft of the changes; the code still needs to be verified in the actual environment.
Images can be submitted together with text through image_url. Files such as PDFs can be handled by the file-reading workflow through file_url in AI Chat v2; multimodal messages in Chat Completions cannot be used directly as a file upload entry point. File links need to be accessible, and pay attention to the reading results.
They should not be treated directly as the same parameter. effort controls reasoning investment in the native Claude API; compatible interfaces use their own parameter formats and should not be copied mechanically. When in-depth analysis is needed, you can also explicitly request comparison of options, explanation of assumptions, and validation steps in the task.
When using AI Chat v2, save the returned id and include the same id in subsequent requests to continue the conversation; stateful is enabled by default. When using Chat Completions, the client organizes the messages history. For long tasks, it is recommended to keep phase summaries and clearly state which conclusions have been confirmed and which items remain to be addressed.
Model information · Updated: 2026-10-01. See the API and pricing sections for invocation parameters and billing rules.
Choose a development approach or application suited to this model and follow its setup guide.
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