Claude Sonnet 4.5 is Anthropic's general-purpose reasoning and multimodal understanding model, and claude-sonnet-4-5-20250929 is its date-pinned version. It is especially suited for code review, refactoring planning, test design, and multi-step analysis, combining requirements, code, and visual materials to produce actionable recommendations. For applications that have already established prompts and acceptance workflows around Sonnet 4.5, this specific version facilitates ongoing evaluation and maintenance.
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-4-5-20250929",
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 calling methods before selecting a model.
Sonnet 4.5's reasoning and vision are model capabilities; document content and tool results are submitted in Chat Completions or Messages format, while the application manages multi-turn history.
Learn what claude-sonnet-4-5-20250929 can bring to your work.
Sonnet 4.5 is well suited to combining code review with engineering planning. After providing the relevant implementation, error logs, and expected behavior, you can ask it to explain the issue, propose refactoring steps, outline migration impacts, and add a testing strategy. Clearly stating compatibility requirements and acceptance criteria helps produce recommendations that are easy for teams to discuss and implement.
It supports placing images and text requirements in the same task for analyzing interface screenshots, document screenshots, or charts. Compared with providing only a single question, adding page objectives, business rules, and areas of focus makes it easier to turn visible information into issue lists, explanations, or modification suggestions; deliverables remain primarily text and code.
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 returns and verification records, rather than being determined solely from the model's description.
Start with specific tasks to find where the model can be effective.
Provide the modules to be changed, dependency relationships, interface constraints, and migration goals, and have the model first list the scope of impact, then generate a phased transformation plan, sample code, and a regression testing checklist. This is suitable as preparation material for engineering reviews, especially for tasks that require explaining trade-offs and preserving existing behavior, rather than directly replacing builds and tests.
Submit interface screenshots together with interaction descriptions, and ask the model to organize issues by information hierarchy, status prompts, and consistency with requirements, then produce modification suggestions or acceptance criteria. For error pages, you can add logs and code snippets to connect visual symptoms with implementation details, helping product and engineering teams align on the investigation direction.
This is suitable for first organizing materials by topic, then asking follow-up questions about a particular difference or clause. You can request that the draft include a summary, evidence locations, and action items, then adjust the wording based on the audience and purpose. A fixed-version model makes it easier to maintain existing evaluation baselines; when materials and requirements are updated, old content should be replaced accordingly.
Choose based on task complexity, input materials, and expected results.
If prompts, tool workflows, and regression samples have already been validated around Sonnet 4.5, continuing to use this date-pinned version helps maintain a clear evaluation baseline. Sonnet 4.6 is an independent version and should not be treated as an automatic upgrade of the same ID; when migrating, compare code usability, task completion, and tool behavior rather than only comparing the style of a single response.
When migrating from Sonnet 3.5, Sonnet 4.5 is a clear upgrade candidate, especially for reassessing programming and complex tasks. New projects can also test Sonnet 4.6 at the same time.
Based on the characteristics of claude-sonnet-4-5-20250929, first validate small tasks whose results can be checked.
You can ask directly: Review this module, propose refactoring steps that do not change external behavior, and list the regression tests needed for each step. Output a risk ranking and the minimum scope of changes.
Keep the precise date-based model version for version dependencies; compare migration candidates based on project behavior and test results.
Use the full model ID claude-sonnet-4-5-20250929, 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 capability scope.
Answers to common questions about using claude-sonnet-4-5-20250929.
No. claude-sonnet-4-5-20250929 explicitly refers to the fixed Sonnet 4.5 date version, while Sonnet 4.6 uses a different ID. Existing applications can retain it as a regression baseline; before switching versions, compare code quality, instruction following, and tool interaction results using real tasks.
It can be prioritized for code review, refactoring, migration planning, and test strategy, and is also suitable for discussing system design. When providing input, include the relevant code, observed behavior, and constraints, and request that identified issues be distinguished from hypotheses that still need verification. Generated implementations still need to undergo compilation, testing, and engineering review.
In multimodal content blocks supported by the selected interface, combine text questions with clear images, and specify the areas 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.
Include user and assistant messages relevant to the current task in the messages of Messages or Chat Completions, handling the specific format according to the selected public interface. Retain the latest code, interim conclusions, and important constraints; when necessary, summarize longer history again to avoid carrying forward outdated information.
Applications that already use the OpenAI messages structure can use Chat Completions; if you need Claude-native content blocks, thinking, or tool_use/tool_result flows, check Messages API support for this model. Handle the request, response, and parameter formats separately for the two options, and retain the full model ID.
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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