Claude Opus 5 is a reasoning and vision conversational model in Anthropic's Opus series, suited for large codebase analysis, migration planning, and document review requiring careful judgment. It can combine text and images to understand problems, organize analysis around constraints, and deliver results. 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-opus-5",
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 calling methods before selecting a model.
Understand Opus 5's reasoning and visual positioning separately from API content formats. Applications are responsible for preparing materials, passing necessary history, and executing tools; model recommendations must not be treated as actions already completed.
Learn what claude-opus-5 can bring to your work.
Opus 5 is best suited not for completing an isolated piece of code, but for reviewing changes in the context of module responsibilities, call relationships, and business constraints. Provide relevant files, diffs, and testing requirements, and it can organize potential impacts, blocking issues, and repair suggestions, helping engineering teams ground discussions in specific code locations and verification steps.
For migration plans, contract clauses, or research materials, you can ask Opus 5 to separately list known facts, assumptions, and items requiring confirmation, then explain the basis for its recommendations. This task structure suits work with high judgment costs and also lets reviewers verify each item rather than receiving only an unexplained conclusion.
Opus 5 can use charts, interface screenshots, and related text together for problem analysis, helping connect visible phenomena with code or business conditions. Specify the areas to observe and the questions to answer, then ask it to separately list evidence, inferences, and next verification items to avoid getting only a fluent but unverifiable explanation.
Start with specific tasks to identify where the model can be effective.
Provide code diffs, related modules, interface contracts, and existing tests, and have Opus 5 check compatibility, edge cases, and missed regression scenarios. The deliverable can be designed as a list of issues ranked by severity, with each item including the file location, impact description, and recommended tests for developers to address one by one.
Provide the current architecture description, key code, and target environment constraints, and have the model compare migration paths, identify dependency order, and break down implementation phases. Require an output of modification lists, acceptance criteria, and rollback conditions. This is suitable for developing a discussable plan before formal development, rather than directly putting generated code into production.
Provide report text, metric definitions, and charts to Opus 5, and ask it to organize viewpoints and data definitions while identifying contradictory or unsupported parts. Deliverables can include a summary, evidence comparison, and a list of follow-up questions; verify important figures and conclusions against the original text rather than applying Opus 5.5 capability evaluations.
Choose based on task complexity, input materials, and expected results.
Opus 5 and Opus 5.5 are different versions. Opus 5.5's upgrades include performance on complex tasks and clarity of expression; these changes cannot be regarded as existing characteristics of Opus 5. Existing Opus 5 workflows can retain benchmark tasks, then compare the new version's patch acceptability, report quality, and amount of manual revision to decide whether to migrate.
Opus 5 is better suited to reviews involving cross-file relationships, multiple constraints, and careful judgment; simple classification or short summaries can also be compared with Sonnet and Haiku. Existing OpenAI message-based applications can use Chat Completions, while native reasoning and content-block workflows can use Messages; protocol selection should serve the application's actual processing method.
Based on the characteristics of claude-opus-5, first validate small tasks whose results can be checked.
You can ask directly: identify locations in the code, protocols, and migration materials that affect data compatibility, rank them by risk, and provide a minimal fix and regression plan.
Validate this model independently; do not apply Opus 5.5 figures or all capabilities; use current materials and real test results.
Use the full model ID claude-opus-5, 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 use the same set of real samples to evaluate whether it is suitable for continued use.
Before formal use, understand the output quality and scope of capabilities.
Answers to common questions about using claude-opus-5.
No. claude-opus-5 corresponds to Opus 5, while Opus 5.5 is a later version. Test task performance separately when choosing; do not directly apply the reasoning modes, safeguards, or expression improvements of the later version to Opus 5. Existing applications in particular should check for changes in output style and tool workflows.
It is better suited for work that requires judgment based on context, such as cross-module reviews, migration plan design, and complex failure analysis. When submitting code, include relevant dependencies, error behavior, and acceptance requirements, and ask it to identify the problem locations and verification methods; generated changes still need testing and human review.
Prepare 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 content in formats supported by the selected public interface; a PDF address cannot be used as image_url. Require results to retain original locations, field evidence, and unconfirmed items, and verify critical numbers against the source materials.
Include user and assistant messages relevant to the current task in messages for Messages or Chat Completions, handling the specific format according to the selected public interface. Keep the latest code, interim conclusions, and important constraints; when necessary, summarize long histories again to avoid relying on outdated information.
Tool workflows should be organized according to the tool definitions and result formats of the selected public interface. 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 responses and verification records, not 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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