claude-opus-4-20250514 is the date-fixed version of Anthropic Claude Opus 4, designed for tasks that require combining context, breaking down conditions, and forming written conclusions. It has reasoning and vision capabilities, bringing text, screenshots, and charts into the same analysis workflow, making it suitable for complex problem discussions, code reviews, and material organization. The fixed model version makes it easier to maintain existing applications and conduct version comparisons, but it is not equivalent to newer Opus models.
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-4-20250514",
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.
claude-opus-4-20250514 is an exact version identifier; include relevant messages history in multi-turn requests, and handle input content and response parsing according to the selected public protocol.
Learn what claude-opus-4-20250514 can bring to your work.
For reasoning tasks, you can submit goals, constraints, existing judgments, and unresolved questions together, allowing the model to break down problems by condition, compare options, and form explanations. This is better suited to analysis that needs to explain the basis for judgments rather than simply provide short answers; requiring responses to distinguish known facts, assumptions, and items to be verified also facilitates human review.
Visual input can be used together with text questions, such as attaching an interface screenshot and asking about the location of an anomaly, or discussing trends and presentation meaning around a chart. The output remains text analysis, not image generation. Explaining the area of interest, business context, and desired response format when submitting can make the discussion more relevant to the specific task.
Existing applications can retain the exact version claude-opus-4-20250514 for regression comparison. OpenAI-style clients use Chat Completions; use Messages when native system, content block, and tool result formats are needed. Message history and parameters are handled according to their respective protocols, and the two response parsing methods cannot be mixed directly.
Start with specific tasks to identify where the model can be effective.
Provide relevant code, error logs, expected behavior, and the runtime environment, and ask the model to organize possible causes, suggested changes, and verification steps. Deliverables can be set as review comments or a troubleshooting checklist, with test results added in subsequent rounds. The core here is analysis and recommendations; actually running code and confirming fixes are still completed through the development process.
Provide report screenshots, product interfaces, or flowcharts, and clearly specify the metrics, interaction issues, or logical relationships you want explained, so the model can produce observation notes and a list of questions. For local details, crop them separately and continue asking; when accurate numbers are needed, it is best to include textual data as well, avoiding treating visual estimates as precise values.
Submit requirement descriptions, candidate solutions, and constraints as text materials, first obtain a comparison framework, then add budgets, dependencies, and exceptions in successive rounds to form a decision memo. The application should carry relevant history and the latest conditions in follow-up requests, clearly specifying in each round the parts to revise and the final delivery format.
Choose based on task complexity, input materials, and expected results.
If an application has already built prompts, acceptance examples, and output-processing logic around claude-opus-4-20250514, using the exact ID helps keep the version selection explicit. It is not a compatible alias for Opus 4.1 or later Opus versions; when switching models, answer quality, format stability, and tool interaction performance should be compared again rather than assuming results will remain unchanged.
This version is better suited to maintaining existing tasks and comparing historical versions; new projects should also evaluate newer Opus models. Anthropic has retired it from the native Claude API and recommends Opus 4.8. Long context, image processing, or reasoning controls in later versions cannot automatically be considered capabilities of this version; model selection should be based on real task examples.
Based on the characteristics of claude-opus-4-20250514, first validate small tasks whose results can be checked.
You can ask directly: Review this code against the same set of requirements, record missing conditions, incorrect assumptions, and places that need changes, then compare it with the historical baseline saved by the application.
Keep the original prompts and samples to avoid changing acceptance criteria while switching versions; for new projects, evaluate the current model.
Use the full model ID claude-opus-4-20250514, first confirm the public request format and available parameters on the API page, then connect the 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 capability scope.
Answers to common questions when using claude-opus-4-20250514.
claude-opus-4-20250514 is a date-pinned version of Claude Opus 4, with the date suffix used to clearly identify the version. It is not a name that “always uses the latest Opus,” nor is it an alias for Opus 4.1 or Opus 4.8; it is suitable for applications that need to explicitly record the model version.
It has visual understanding capabilities and can provide analysis based on images and text questions, such as explaining screenshots, discussing charts, or describing scenes. The image-and-text workflow here produces text answers and does not involve image generation; when submitting images, also specify what should be observed and what conclusions are needed.
Include user and assistant messages relevant to the current task in the messages of Messages or Chat Completions, using the specific format required by the selected public interface. Keep the latest code, interim conclusions, and important constraints; when necessary, re-summarize longer history to avoid carrying forward outdated information.
Prepare the document body, table data, or clear page screenshots relevant to the question, and specify whether you need a summary, comparison, or extraction of particular information. Submit according to the content formats supported by the selected public interface; a PDF address cannot be used as image_url. Request that results retain original-text locations, field evidence, and unconfirmed items, and verify key numbers against the source materials.
Reasoning capabilities and thinking-budget parameters are different things. When using this version for complex analysis, you can clearly state goals, constraints, and checking requirements in the prompt, but do not directly interpret the shared reasoning_effort field as Anthropic's native thinking budget, nor do you need to rely on displayed thinking content to judge answer quality.
Choose a development approach or application suited to this model and follow its setup guide.
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