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. It enhances real-world code tasks and reasoning on top of Opus 4, making it especially suitable for work requiring cross-file understanding, constraint tracking, and precise fixes. It can also combine image understanding for multimodal 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/output, and calling methods before selecting a model.
Native capabilities and benchmark descriptions refer to the model itself; session storage, file reading, and tool collaboration are platform endpoint features and do not equate to the model's independent execution capabilities.
Learn what claude-opus-4-1-20250805 can bring to your work.
Opus 4.1's programming improvements are not limited to code generation; they also 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 definitions, 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. You can require responses to distinguish direct evidence, inferences, and items to be verified, making deliverables easier to review.
The model has visual understanding capabilities and can analyze screenshots, charts, and textual descriptions together. In AI Chat v2, it can also use file reading and authorized tools to form multi-step workflows, linking material retrieval, content understanding, and report organization; the tool execution process and final text response can be presented separately.
Start with specific tasks to find where the model can make an impact.
Provide the error stack, involved source files, reproduction steps, and interfaces that must not be changed, and ask it to first identify the cause, then submit patch suggestions and a regression test checklist. It is suited to maintenance tasks that require cross-module verification and controlled change scope; delivered code should still enter the testing and review process before being merged.
Provide research text, data descriptions, or readable file links submitted through AI Chat v2, and have the model compare conclusions according to unified questions, extract key conditions, and organize a comparison table. Deliverables can include summaries, evidence locations, and questions for further verification, making it suitable for the preparation stage of technology selection and topic research.
Enter dashboard screenshots, images of interface anomalies, and business context together, and ask the model to describe visible information, propose explanations, and list verification steps. It can help turn visual observations into actionable troubleshooting checklists; when precise values are involved, it is best to provide the raw data as well to 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 path, and Anthropic also recommends upgrading from Opus 4. When migrating, use the same task set to compare the scope of changes, constraint adherence, and test results, rather than looking only at answer length or wording.
When you need a fixed model version for evaluation or maintaining existing workflows, use claude-opus-4-1-20250805 rather than treating it as an automatically updated latest Opus. If you already have messages management logic, choose Chat Completions; if you want hosted history, file reading, and orchestration of tool tasks, choose AI Chat v2.
Start with a small-scale task before full integration.
Define the goal, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
Keep the full model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Understand output quality and capability scope before production use.
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, with the release particularly highlighting multi-file code refactoring and 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.
Images can be submitted through image-and-text messages, and the model understands them in combination with textual instructions. PDFs can enter the file-reading workflow through file_url in AI Chat v2; do not treat image-and-text input in Chat Completions as a direct PDF upload method, and extracted content should be checked for files with complex layouts.
Chat Completions uses this model ID with messages, with the application organizing the conversation history. In AI Chat v2, you can first submit model and question, save the returned id, and continue the conversation by sending the same id; stateful is enabled by default and can also be explicitly set to false.
The model can analyze code, propose patches, and participate in tool-driven programming workflows, but ordinary text requests will not obtain a runtime environment on their own. Automatic modification and testing require configuring available tools and permissions, and providing execution results; it is recommended to retain change review, test acceptance, and failure rollback mechanisms.
Model information · Updated: 2026-10-01. Please see the API and pricing sections for invocation parameters and billing rules.
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↗Input
$3.00per 1M tokens
Official $15.00
Output
$15.00per 1M tokens
Official $75.00
You pay$13,584for 142,800 credits · $0.0951 / credits46% OFF
You're billed in credits. Pick a top-up tier to see its per-credit rate — the larger the top-up, the cheaper each credit.
This is only a basic call example. See the full docs for more parameters and advanced usage.
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