Claude Opus 4.1 is Anthropic's model for complex programming, research analysis, and agent tasks, and claude-opus-4-1-20250805 is its date-fixed version. Building on Opus 4, it strengthens real-world code tasks and reasoning, making it especially suitable for work that requires cross-file understanding, constraint tracking, and precise fixes. It can also combine image understanding for visual and textual analysis.
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-4-1-20250805",
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.
Opus 4.1's native capabilities and benchmark results indicate its task focus. The content formats of Chat Completions and Messages are handled independently, while message history and tool execution are maintained by the application.
Learn what claude-opus-4-1-20250805 can bring to your work.
Opus 4.1's programming improvements go beyond code generation, with greater focus on multi-file refactoring and precise fixes in real projects. After providing relevant modules, errors, and behaviors that must be preserved, you can have it analyze dependencies, identify where changes are needed, and explain the impact, making it suitable for debugging and maintenance work aimed at minimal changes.
When faced with multiple materials, differing criteria, and complex constraints, Opus 4.1's upgrades focus on in-depth research, data analysis, and detail tracking. It can organize claims, compare conditions, and structure conclusions around the materials provided. Asking responses to distinguish direct evidence, inferences, and items requiring verification makes deliverables easier to review.
Opus 4.1 can analyze issues by combining interface screenshots, data charts, and task descriptions, helping connect visible anomalies with code or business conditions. Specifying observation areas, evaluation criteria, and expected results, then asking it to distinguish direct evidence from inference, helps apply visual information to engineering investigations.
Start with specific tasks to find where the model can be effective.
Provide error stacks, the source files involved, reproduction steps, and interfaces that must not be changed; require the cause to be identified first, followed by patch suggestions and a regression test checklist. It is suitable for maintenance tasks that require cross-module verification and controlled modification scope; delivered code should still enter the testing and review process before being merged.
Submit specification versions, research texts, or analysis tables together with specific questions, allowing the model to track details, compare criteria, and organize differences. The report can separately list materials supporting conclusions, conflicting information, and unresolved issues, giving subsequent reviews a clear verification path.
Provide dashboard screenshots, images of interface anomalies, and business context together, asking the model to describe visible information, propose explanations, and list verification steps. It can help turn visual observations into an actionable troubleshooting checklist; when precise values are involved, it is best to also provide the raw data and avoid calculations based solely on screenshots.
Choose based on task complexity, input materials, and expected results.
If existing tasks use Opus 4 and the main challenges are code debugging, multi-file refactoring, or complex reasoning, Opus 4.1 is a clear upgrade option, and Anthropic also recommends upgrading from Opus 4. During migration, use the same task set to compare modification scope, constraint adherence, and test results rather than looking only at answer length or wording.
Applications that already use the OpenAI messages structure can use Chat Completions; if Claude-native content blocks, thinking, or tool_use/tool_result workflows are needed, check Messages API support for this model. Handle the two request, response, and parameter formats separately, and retain the full model ID.
Based on the characteristics of claude-opus-4-1-20250805, first validate small tasks whose results can be checked.
You can ask directly: Trace the call path from the entry point to data writing, explain how the error propagates, and identify the locations most likely to change behavior. Output minimal patch recommendations and the affected tests.
Provide the relevant files and logs; verify cross-file changes against actual behavior, and do not treat review suggestions as patches that have already been run.
Use the full model ID claude-opus-4-1-20250805, first confirm the public request format and available parameters on the API page, then connect your application. Preserve result parsing, exception handling, and relevant evidence, and evaluate whether it is suitable for continued use with the same set of real samples.
Before formal use, understand the quality of the output and the scope of its capabilities.
Answers to common questions about using claude-opus-4-1-20250805.
It is the date-pinned invocation ID for Claude Opus 4.1, corresponding to the version released on August 5, 2025. It is suitable for projects that need a specific version for testing and integration, and should not be confused with Opus 4, later Opus versions, or invocation names with the thinking suffix.
The upgrade focuses on agentic tasks, real-world coding, and reasoning. The release particularly emphasizes multi-file code refactoring, as well as detail tracking in research and data analysis. If a task requires precise fixes and fewer unrelated changes, you can prioritize evaluating Opus 4.1 with existing code tasks.
Prepare the document text, tabular 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-text locations, field evidence, and unconfirmed items, and verify key numbers against the source materials.
Include user and assistant messages relevant to the current task in the 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, re-summarize long histories to avoid relying on outdated information.
The model can analyze code, propose patches, and participate in tool-driven programming workflows, but ordinary text requests do not give it access to a runtime environment on their own. Automatic modification and testing require configured available tools and permissions, along with execution results; it is recommended to retain change review, test acceptance, and failure rollback mechanisms.
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 Configuration 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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