claude-3-5-sonnet-20241022 is a fixed version of Claude 3.5 Sonnet released by Anthropic in October 2024, with a focus on software engineering, task planning, and tool use. It can analyze text and images together, making it suitable for code debugging, interface screenshot interpretation, and version regression of existing applications; when choosing, it should be distinguished from the June 2024 version and subsequent Sonnet 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-3-5-sonnet-20241022",
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 selecting a model.
Benchmark results are publicly available results for the native model; message formatting, continued conversations, and response formats are platform interface features and do not represent project success rates or guarantees of automatic execution.
Learn what claude-3-5-sonnet-20241022 can bring to your work.
This version's upgrade focuses on programming and is suitable for analyzing errors, relevant code, and expected behavior together. It can help identify causes, propose patches, and add testing ideas, rather than merely explaining syntax. Asking responses to list the reasons for changes and verification steps makes it easier for developers to review and integrate them into projects.
Through image-text messages, the model can combine interface screenshots and business descriptions to explain page content, analyze abnormal behavior, or organize visible information. Screenshots provide visual context, while text defines the question and delivery format; for interface debugging, it is best to also provide operation steps and expected results.
Official updates emphasize task planning and tool-use capabilities, making it suitable for breaking complex requirements into inspection, modification, and verification steps. Developers can design tool collaboration workflows around it, but operations proposed by the model do not mean they have already been executed; code execution, permission control, and result write-back still require explicit application arrangements.
Start with specific tasks to find where the model can be effective.
Provide failing tests, error stacks, and related functions, and request root cause analysis, modification recommendations, and regression cases. This version is suitable for software maintenance tasks that require understanding context; limiting the scope of changes to specific modules and requiring an explanation of potentially affected callers can help reduce review costs.
Submit page screenshots, the sequence of user actions, and an issue description, and have the model organize visible problems and propose a checklist. Deliverables can be bug reports, reproduction steps, or frontend modification recommendations. It analyzes the provided images; it cannot obtain page source code from screenshots alone, nor can it automatically click through a real interface.
Keeping this single date-fixed version lets you use stable input sets to check code fixes, visual understanding, and instruction following. When comparing new models, record omissions, errors, and the number of required revisions as well; do not change prompts and acceptance criteria while switching models, or it will be difficult to determine where the changes came from.
Choose based on task complexity, input materials, and expected results.
20241022 and 20240620 are different fixed versions. In official release evaluations, software repair performance improved from 33.4% to 49.0%, and improvements in tool-use capabilities were also reported. If you already have an application using the June version, prioritize regression testing for repair patches, multi-step planning, and tool parameter generation rather than assuming that all tasks improve by the same margin.
Fixed IDs have clear value when you need to study the behavior of an older version or maintain workflows that depend on it; new projects should also evaluate subsequent Sonnet versions. Anthropic has ended service for this version on its own platform and recommends Sonnet 4.6. During migration, compare prompt adaptation, tool interactions, and business test results; simply replacing the name is not advisable.
Based on the characteristics of claude-3-5-sonnet-20241022, first validate small tasks whose results can be checked.
You can ask directly: compare the failure logs, code, and interface screenshots, explain the error path, propose a narrowly scoped fix, and explain why it will not change other functionality.
Provide reproducible inputs and clear screenshots; actual execution and computer operations require an application execution environment.
Use the full model ID claude-3-5-sonnet-20241022, first confirm the public request format and available parameters on the API page, then connect the application. Preserve result parsing, error handling, and relevant evidence, and evaluate with the same set of real samples whether it is suitable for continued use.
Before formal use, understand the output quality and capability scope.
Answers to common questions when using claude-3-5-sonnet-20241022.
They are different pinned versions of Claude 3.5 Sonnet, with 20241022 being the October update. The official announcement specifically highlights improvements in coding and tool use. For code maintenance, compare the patch quality of both versions using the same set of defects and tests; do not treat pinned date IDs as automatically updating aliases.
In multimodal content blocks supported by the selected API, combine a text question 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.
It will not automatically run code or operate the desktop merely because you send a regular message. This version has relevant task-planning capabilities, and experimental computer use was also provided at launch, but actual execution requires tools and an appropriate environment. Applications should distinguish between model suggestions, executed actions, and verification results.
Do not treat it as a thinking variant, and do not assume reasoning_effort can enable the deep-thinking mode of later models for it. For complex tasks, you can ask it to break down the problem, list assumptions, and provide verification steps; if you need dedicated thinking controls, choose a model that explicitly supports that feature.
Include user and assistant messages relevant to the current task in Messages or in the messages of Chat Completions, handling the specific format according to the selected public API. Retain the latest code, interim conclusions, and important constraints; when necessary, re-summarize long histories to avoid carrying forward 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 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
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Official $3.00
Output
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This is only a basic call example. See the full docs for more parameters and advanced usage.
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