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. Use the Messages API on this platform to organize work around text, images, and multi-turn feedback.
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 anthropic import Anthropic
client = Anthropic(
auth_token=os.environ["ACEDATACLOUD_API_KEY"],
base_url="https://api.acedata.cloud",
)
response = client.messages.create(
model="claude-opus-5-5",
max_tokens=1024,
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.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/output, and invocation methods before selecting 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 existing behavior.
It is well suited to financial analysis, business judgment, and research across multiple materials, organizing facts, assumptions, and conclusions around task objectives. After providing raw data, definitions, and citation requirements, you can ask it to deliver an evidence list and items requiring confirmation, making analysis results easier to check item by item instead of producing only a fluent summary.
Its visual capabilities allow it to answer questions using charts or interface screenshots; its writing emphasizes leading with important information and following communication rules. You can place images and task instructions in the same message and ask it to organize responses by 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 make an impact.
Provide the modules to be migrated, call relationships, code differences, and test results, and let the model organize the scope of impact, potential behavior changes, and repair recommendations. Deliverables can include a file-level review checklist, migration steps, and a test plan; when connected to programming tools, the execution environment can then run tests and return failure information.
Provide financial data, metric definitions, business context, and decision questions, and require a distinction between known facts and scenario assumptions. The model can help generate operational analysis memos, comparison tables, and management summaries; listing calculation bases and key figures separately makes it easier to manually verify definitions and determine whether conclusions are supported.
Enter 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 needs to be added. You can then turn the analysis into explanatory copy for engineers or managers, retaining the same material foundation 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, the key improvements in Opus 5.5 are 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 manual review required. During migration, especially remove assumptions about disabling thinking, and allocate a shared output budget for reasoning 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 all difficult problems perform the same. For cross-codebase engineering, comprehensive reports, and text-and-image explanations, try Opus 5.5 first; existing Fable 5.1 workflows should use the same materials and acceptance criteria for comparison, retaining the more stable choice for critical tasks.
Based on the characteristics of claude-opus-5-5, first validate small tasks whose results can be checked.
You can ask directly: compare the protocols, data models, and migration plans in 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 use the same set of real samples to evaluate whether it is suitable for continued use.
Before formal use, understand the output quality and range of capabilities.
Answers to common questions when using claude-opus-5-5.
This model uses /v1/messages; you cannot simply replace the model in an OpenAI chat request. System prompts use a separate system field, and responses are read from the content block array; clients should also handle stop_reason rather than parsing results according to the choices structure.
Thinking cannot be disabled, but you can explicitly request a short final answer. The length of the visible text and whether the model performs reasoning are different matters; the output budget needs to 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 unrelated files.
Read responses by content block type. When tool_use is encountered, have the application execute the corresponding function, then return the result with tool_result. Multi-turn messages should retain the complete reasoning blocks and signatures returned by the model; do not rewrite signatures yourself. Tool outputs and authorization scope should also be reviewed 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.
Claude Code CLI Configuration Guide
Coding tool setup · requires a Coding Plan application key