Claude 3.7 Sonnet is a chat model in Anthropic's Sonnet series, and claude-3-7-sonnet-20250219 is the date-fixed version, combining reasoning and visual understanding capabilities. It is suited to organizing text, screenshots, and task constraints into analyses, code suggestions, or document drafts, and is also suitable for existing projects that retain a specific version for regression comparisons; new projects should consider newer Sonnet models in light of its lifecycle.
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-3-7-sonnet-20250219",
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 choosing a model.
The date version specifies the model identity; multimodal and streaming requests are organized according to the selected public protocol. For ongoing discussions, the application must include relevant history; the model name does not imply that the platform automatically stores sessions.
Learn what claude-3-7-sonnet-20250219 can bring to your work.
Suitable for placing the problem context, decision criteria, and expected results in the same task, allowing the model to organize analysis steps, compare options, and form written conclusions. When handling code or business rules, you can ask it to separately list known conditions, assumptions, and items to be verified, making responses easier to review instead of receiving only an unexplained recommendation.
Text and images can be combined as input to ask specific questions about interface screenshots, document screenshots, or charts. Compared with describing the image alone, attaching it directly makes it easier to discuss layout, visible text, and information relationships. Deliverables are still mainly text, and you can request an issue list, chart description, or follow-up revision suggestions.
In messages for Messages or Chat Completions, include user and assistant messages related to the current task, handling the specific format according to the selected public API. Retain the latest code, interim conclusions, and important constraints; when necessary, re-summarize lengthy history to avoid relying on outdated information.
Start with specific tasks to find where the model can be useful.
Provide relevant code snippets, error messages, and expected behavior, and ask the model to explain possible causes before offering modification suggestions and testing checklist items. This is suitable for turning troubleshooting into a discussable repair draft; code execution, test runs, and deployment should still be completed in the development environment, and generated suggestions should not be treated directly as verified results.
Submit product screenshots or charts and explain what needs to be checked, such as information hierarchy, field meanings, or whether the presentation is easy to misunderstand. Ask the model to describe issues by location and separate visible information from inferences, ultimately producing a review checklist or explanatory draft. Key values should preferably also be provided as text for item-by-item comparison.
Prepare document text, table data, or clear page screenshots related to the issue, and explain whether you need a summary, comparison, or extraction of specific information. Submit them in the content formats supported by the selected public API; PDF URLs cannot be used as image_url. Request that results retain original-text locations, field sources, and unconfirmed items, and verify key figures against the source materials.
Choose based on task complexity, input materials, and expected results.
If existing prompts, review records, or regression samples were built around Claude 3.7 Sonnet, explicitly using the date ID helps organize same-version comparisons. It should not be mixed with claude-3-7-sonnet-thinking, nor should specifications from later Sonnet versions be applied to it. Pinning a version clarifies the test subject, but does not guarantee word-for-word identical responses or long-term availability.
Anthropic has listed this model as retired, with claude-sonnet-4-6 as the officially recommended replacement. New projects should prioritize evaluating later Sonnet versions; existing projects can compare answer quality and API compatibility using real code, image-and-text samples, and multi-turn tasks. Do not judge benefits solely by version names, and do not skip migration testing by reusing old configurations.
Based on the characteristics of claude-3-7-sonnet-20250219, first validate small tasks whose results can be checked.
You can ask directly: Check whether this fix draft meets the requirements, list missing conditions and tests that could disprove it, then prepare a review summary.
Retain regression samples for applications using existing pinned versions; handle thinking parameters according to the public API, and do not infer all configuration options from the model name.
Use the full model ID claude-3-7-sonnet-20250219, 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 output quality and capability boundaries.
Answers to common questions when using claude-3-7-sonnet-20250219.
claude-3-7-sonnet-20250219 is the date-pinned model ID for Claude 3.7 Sonnet, not a general term for subsequent Sonnet models. When calling it, use the full ID to clearly identify the testing and integration target; a fixed date does not mean the output is fully deterministic, nor is it a version name that is continuously upgraded automatically.
They should not be used interchangeably. They are different call IDs, and this page describes the date-pinned endpoint. Reasoning capability and how Extended Thinking is enabled are not the same concept; if dedicated thinking control is needed, use an endpoint that explicitly supports this feature and revalidate the parameters and returned content.
In multimodal content blocks supported by the selected API, combine a text question with a clear image, and specify the area of interest and expected output. Chat Completions uses text and image_url, while Messages uses its native image content block; do not use PDF or video addresses as image_url.
Applications that already use the OpenAI messages structure can use Chat Completions; if Claude-native content blocks, thinking, or tool_use/tool_result flows are needed, check Messages API support for this model. Handle the request, response, and parameter formats separately, and retain the full model ID.
The officially recommended replacement model is claude-sonnet-4-6. During migration, prioritize comparing code suggestions, screenshot understanding, instruction following, and multi-turn consistency on real tasks, while also checking response parsing and error handling. The old version has entered official retirement status, so also verify whether the replacement model can cover core workflows.
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↗This is only a basic call example. See the full docs for more parameters and advanced usage.
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