Claude Opus 5.5 is a high-capability model in the Anthropic Claude 5.5 family, focused on cross-file programming, complex knowledge work, and image-text analysis. It combines adaptive thinking with clearer expression, making it suitable for tasks that require continuously organizing context, checking assumptions, and delivering reviewable results. On this platform, compatible chat requests can be handled through Chat Completions, and full native content blocks and tool semantics can be used through the Messages API.
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.
api.acedata.cloudclaude-opus-5-5import 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-5-5",
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 invocation methods before choosing a model.
Adaptive thinking is a model capability; the capacity figures and interface paths correspond to this platform's Messages invocation endpoint.
Learn what claude-opus-5-5 can bring to your work.
Opus 5.5 excels at codebase migrations, audits, and multi-file changes, rather than merely completing a function. Providing the relevant code, interface constraints, and regression requirements together enables it to trace dependencies, explain the reasons for changes, and propose validation steps, helping reviewers determine whether the changes preserve original behavior.
It is well suited to financial analysis, business judgment, and research involving multiple materials, and can organize facts, assumptions, and conclusions around task objectives. After providing raw data, definitions, and citation requirements, you can ask it to deliver a list of supporting evidence and items requiring confirmation, making analytical results easier to check item by item instead of simply producing a fluent summary.
Its visual capabilities allow it to answer questions by combining charts or interface screenshots, while its writing emphasizes placing important information first and following communication rules. You can include images and task instructions in the same message and ask it to structure responses around observations, explanations, and recommendations, reducing the burden on readers of finding key conclusions in lengthy text.
Start with specific tasks to find where the model can be effective.
Provide the modules to be migrated, call relationships, code differences, and test results, and have the model organize the scope of impact, potential behavioral changes, and repair recommendations. Deliverables can be set as a file-level review checklist, migration steps, and test plan; if programming tools are connected, the execution environment can then run tests and return failure information.
Provide financial data, metric definitions, business context, and decision questions, and require it to distinguish known facts from scenario assumptions. The model can help generate operational analysis memos, comparison tables, and management summaries; listing the calculation basis and key figures separately makes it easier for people to review definitions and assess whether conclusions are valid.
Input trend charts, flowcharts, or product screenshots together with specific questions, and ask it to explain the relationships in the image, visible changes, and data that need to be added. The analysis can then be turned into explanatory material for engineers or managers, maintaining the same material base while adjusting terminology, length, and the order in which information is presented.
Choose based on task complexity, input materials, and expected results.
Compared with Opus 5, Opus 5.5 focuses on improving complex engineering, knowledge work, and communication clarity. It is recommended to run comparative tests using existing code review or analysis tasks, comparing whether results pass acceptance, how much rework is needed, and the amount of human review required. When migrating, be especially sure to remove assumptions about disabling thinking, and allocate a shared output budget for thinking and the main text.
Anthropic positions Opus 5.5 as a model that can reach Fable 5.1-level performance for most work, but this does not mean they perform the same on every difficult problem. For cross-codebase engineering, comprehensive reports, and text-and-image explanations, try Opus 5.5 first; for existing Fable 5.1 workflows, use the same materials and acceptance criteria for comparison, and retain the more reliable option for critical tasks.
Based on the characteristics of claude-opus-5-5, first validate small tasks with results that can be checked.
You can ask directly: Compare the protocols, data models, and migration plans across two services, and list the differences that would change production behavior. Output the scope of impact, the step-by-step migration sequence, and verifiable rollback conditions.
Provide protocol versions, relevant code, and real test results; cross-repository dependencies and key assumptions should have evidence that can be checked.
Use the full model ID claude-opus-5-5, 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 evaluate whether it is suitable for continued use with the same set of real samples.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using claude-opus-5-5.
You can use model=claude-opus-5-5 in Chat Completions and read compatible chat responses via choices; when you need complete thinking content blocks, signatures, and native tool semantics, use /v1/messages and parse according to content blocks and stop_reason.
Thinking cannot be disabled, but you can explicitly request a brief final answer. The length of the visible text and whether the model thinks are separate matters; the output budget must accommodate both, so do not set max_tokens too tightly just because you request a one-sentence answer.
A larger context is suitable for retaining related code, specifications, and historical feedback, but that does not mean all materials are worth submitting. Prioritize modules and dependencies relevant to the task, and use count_tokens to estimate the input size before sending; clear acceptance criteria are usually more helpful than piling in irrelevant files.
Read responses by content block type. When encountering tool_use, have the application execute the corresponding function, then return the result with tool_result. Multi-turn messages should retain the complete thinking blocks and signatures returned by the model; do not rewrite signatures yourself. Tool outputs and authorization scopes should also be checked separately.
The vision capability here is used to understand images and produce textual analysis, not as an image generation endpoint. It is suitable for submitting charts, flowcharts, or screenshots and asking about trends, relationships, and interface changes; when precise numbers are involved, it is best to also provide a data table so visual observations and numerical evidence can be cross-checked.
Choose a development approach or application suited to this model and follow its setup guide.
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SDK installation, authentication and examples
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