Claude Sonnet 5 is Anthropic's Sonnet model for programming, reasoning, and agent work, with a focus on improving sustained execution from task planning to tool collaboration. It supports analyzing problems using both text and images, making it suitable for code maintenance, technical investigation, and knowledge organization. Applications can integrate it using the public request format in this page's API section.
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-sonnet-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, inputs and outputs, and invocation methods before selecting a model.
Sonnet 5's reasoning and vision are model capabilities. Request content is organized according to the public protocol, while the application maintains message history and executes tools; selecting a tool does not mean the operation has been completed.
Learn what claude-sonnet-5 can bring to your work.
Sonnet 5 focuses not only on code completion, but also on planning continuous work around objectives. Compared with Sonnet 4.6, it has improved in programming, reasoning, and tool use. You can have it first break down the scope of changes, then generate implementation suggestions and a test plan; after connecting execution tools, it can continue adjusting based on runtime feedback.
Tool workflows should be organized according to the tool definitions and result formats of the selected public interface. The model is responsible for planning, explaining results, and generating call suggestions; querying, running code, and writing are completed by the execution environment provided by the application. Actual completion status should come from tool responses and verification records, not be determined solely from the model's description.
Text and images can jointly provide the context for a problem, for example by placing error screenshots, interface states, and requirement descriptions in the same input turn. Sonnet 5 can provide explanations and troubleshooting suggestions based on them, making it suitable for converting visual information into discussable textual conclusions rather than mistaking image understanding for image generation capability.
Start with specific tasks to find where the model can be effective.
Provide relevant code, error logs, and expected behavior, and ask the model to produce hypotheses about the issue, modification plans, and regression testing recommendations. When testing tools are already available, results can be fed back to continue the investigation. Deliverables should be clearly defined as patch recommendations, test cases, and items to verify, making it easier for engineers to review before merging.
Give the model technical documents, screenshots, and questions that need comparison to organize differences between approaches, implementation dependencies, and risks. When files need to be read, use the file content blocks and reading tools in the conversation interface. Finally, require reports to be organized by conclusions, evidence, and open questions, so the team can continue making decisions.
Sonnet 5 is suited to maintaining planning, tool collaboration, and feedback iteration around engineering or knowledge-work goals. With each update, restate the conditions already confirmed and specify the information and acceptance criteria needed for the next step; when the task shifts in a new direction, redefine the scope so the application can check whether changes serve the current goal.
Choose based on task complexity, input materials, and expected results.
If existing tasks are often interrupted between planning, tool feedback, or code modifications, Sonnet 5 is worth prioritizing for testing. Its improvements focus on reasoning, programming, tool use, and knowledge work. During migration, reuse real examples, compare accuracy and the amount of subsequent correction required, and recalculate Token usage rather than copying the old budget.
Sonnet 5 is suited to everyday engineering and multi-step analysis that require a balance of capability and investment; in official evaluations, higher reasoning effort can match Opus 4.8 on some tasks, but this does not mean they are equivalent across the board. For extremely difficult judgments, complex reviews, or critical deliverables, conduct parallel validation before deciding whether to use a higher-capability model.
Based on the characteristics of claude-sonnet-5, first validate small tasks whose results can be checked.
You can ask directly: Based on the goal and available tools, define the planning steps, explain what each step needs to read, what results are expected, and how to proceed if it fails.
Tool responsibilities and permissions should be clear; verify completion with actual returns, and do not claim execution merely because the model can plan.
Use the full model ID claude-sonnet-5, 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 use the same set of real samples to evaluate whether it is suitable for continued use.
Before formal use, understand the range of output quality and capabilities.
Answers to common questions about using claude-sonnet-5.
Primarily programming, research, and knowledge work that require continuous reasoning and tool feedback, such as breaking down modification plans and revising approaches based on test results. If you already have a stable Sonnet 4.6 workflow, you can first compare it using the same set of examples; there is no need to migrate immediately just because the version has changed.
It can analyze code, propose changes, and participate in tool collaboration, but running a project requires an available execution environment and permissions. If you send only text, you will receive code or suggestions; only after connecting tools can it iterate based on execution feedback, and testing and merge reviews should still be retained in the end.
Combine a text question and clear images in the text-and-image content blocks supported by the selected interface, and specify the area of interest and expected output. Chat Completions uses text and image_url, while Messages uses its native image content blocks; do not use PDF or video URLs as image_url.
Prepare the document text, 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; PDF URLs cannot be used as image_url. Require the results to retain original-text locations, the basis for fields, and unconfirmed items, and verify key figures against the source material.
Include user and assistant messages relevant to the current task in messages for Messages or Chat Completions, with the specific format handled according to the selected public interface. Keep the latest code, interim conclusions, and important constraints; when necessary, summarize longer history again to avoid relying on outdated information.
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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